According to the latest McKinsey report (The State of AI), in 28% of organizations using artificial intelligence, the CEO personally oversees AI governance. In 17% of companies, responsibility for these matters rests with the board as a whole. Interestingly, in many cases responsibility is shared among several people – on average, two leaders are identified as responsible for AI-related projects.

Sounds like a solid organizational structure? Not necessarily.

"Two leaders responsible" often means that no one is truly fully responsible. When responsibility is shared, decisions tend to be delayed because each party waits for the other to make a move. When governance is "shared," risk becomes no one's – it's easy to assume someone else is watching for potential threats. When "the team" is supposed to defend ROI, ultimately no one stands before the board with concrete numbers in hand, ready to take responsibility for the results.

In our view, this is one of the more important – though often overlooked – reasons why most AI projects die in the pilot phase. The problem isn't always flawed technology or a lack of team competence. It can be diffused responsibility that paralyzes decision-making and prevents effective scaling of the project.

This article shows how to change that. And it needs to be addressed at the very start of the project, during the planning phase. You'll learn about four key roles that must be filled by specific individuals before your AI project moves beyond the experimental phase. You'll also learn what function each of these roles serves in the project, what their scope of responsibility should be, and how to recognize whether the roles have been properly filled.

Lack of Defined Responsibility Is One of the Key Barriers to Scaling AI

When we look at AI projects that have actually been implemented and deliver measurable business value, we usually see a common denominator: from day one, they had a clearly defined accountability structure. Everyone knew who makes decisions, who's responsible for financial results, who manages risk, and who to turn to when organizational blockers appear.

Is this an absolute requirement? If several people are simultaneously responsible for AI in your organization, is it not even worth trying to implement anything because it won't work anyway? Of course, such a categorical statement would be an exaggeration. However, it's worth being aware that diffused responsibility is a factor that significantly increases the risk of failure for the entire initiative, and certainly greatly complicates scaling the project beyond the pilot phase.

What can happen when responsibility is diffused? Most commonly, it means that every decision requires approval from all people who are in any way involved in the project. The decision-making process extends disproportionately to the importance of the issues being decided. Every risk – even small and easily manageable – becomes a reason to halt the project because no one wants to take responsibility for potential consequences. Every cost meets resistance and becomes difficult to justify in the budget because there's no person who can definitively say: "this is my decision and I'm responsible for it." And every organizational blocker, even seemingly minor ones, becomes insurmountable because no one has sufficient mandate to remove it.

Defining the accountability structure isn't boring bureaucracy or corporate formality. It's a foundation necessary for every project – including, and perhaps especially, those using artificial intelligence.

Without clearly assigned roles, AI implementation becomes an "IT project," an "innovation experiment," or a "digital initiative" – it sounds good on slides and in quarterly reports. However, such a project usually doesn't develop enough to become part of the core business and deliver real value. This happens because no one truly has it within their scope of responsibility, no one is held accountable for its results, and no one has the motivation to push it through inevitable organizational obstacles.

Four Main Roles to Fill in an AI Project

To facilitate our clients' work, we've built a simple but effective strategic tool – the AI Transformation Canvas. It helps organizations plan AI transformation in a structural and comprehensive way: from defining the business problem, through analyzing available data and selecting technology, to establishing governance and defining success metrics. You can download the Canvas by clicking the link below and use it as a starting point for planning AI projects in your organization.

At the center of the AI Transformation Canvas is the "Ownership & Accountability" area – this is where we define four key roles that must be filled by specific individuals before the AI project starts in earnest. Without completing this section, the other elements of the Canvas – even the most well-thought-out ones – won't translate into effective implementation.

Overview of the Four Roles You Should Fill

Why are these four roles key?

Because they answer four fundamental questions that the board must ask in the context of every major project – not just AI-related ones. These questions always come up, sooner or later, and the organization must be ready to answer them:

• Who's responsible for this and who makes key decisions? → Decision Owner
• Who's funding this and who's responsible for return on investment? → Economic Owner
• Who manages risk and ensures the project doesn't harm the organization? → Risk Owner
• Who will remove organizational obstacles when the project gets stuck? → Executive Sponsor

If you can't answer these questions with specific names – your project most likely isn't ready to scale. It may work in the pilot phase, may deliver promising results under controlled conditions, but collision with organizational reality will probably stop it.

Hierarchy and Relationships Between Roles

These four roles obviously don't operate in isolation – they form a cohesive governance structure for project management. Each operates at a different level of the organization and reports differently:

• Executive Sponsor operates at the board level, providing strategic and political support. This is the person who can open doors that remain closed to others.
• Decision Owner operates at the operational level, making specific executive decisions about the project's direction. This is the person the team turns to when they need a resolution.
• Economic Owner reports to the CFO or CEO, defending the project's financial value and justifying costs incurred. This is the person who must be able to "sell" the project in the language of numbers.
• Risk Owner collaborates with legal, compliance, and security departments, managing risk in a systematic and cross-cutting manner. This is the person who sees threats that others may not notice.

Understanding these relationships is key because it helps avoid competency conflicts and ensures that every issue reaches the right person.

Executive Sponsor: Who Removes Blockers and Ensures Cross-Functional Support?

Executive Sponsor is a person at the C-suite or senior leadership level who ensures that the AI project has political support, access to resources, and appropriate priority in the organization. This role is particularly crucial in large organizations where cross-functional projects – and almost all AI projects are cross-functional – must break through organizational silos and compete for resources with other initiatives.

Main Tasks of the Executive Sponsor

• Removing organizational blockers – when the project needs resources, data, or support from another department, and that department isn't willing to cooperate, the Executive Sponsor intervenes and resolves the impasse. They have sufficient position in the organization for such interventions to be effective.
• Ensuring cross-functional collaboration – AI projects rarely fit within one department. They usually require collaboration between IT, business, legal, operations, and others. The Sponsor's task is to break down silos and build bridges between different parts of the organization.
• Protecting the project from priority shifts – in every organization, new "urgent" initiatives regularly appear that compete for attention and resources. The Executive Sponsor defends the AI project from being pushed aside and ensures it maintains its priority.
• Opening doors – provides access to key stakeholders and decision-makers who may be inaccessible to the project team. Organizes meetings that wouldn't have a chance of happening without their involvement.

This is NOT a ceremonial role. The Executive Sponsor isn't an "honorary patron" whose name is written into documentation for prestige. This is a person who actively acts when the project stalls – and who is ready to invest their time and political capital so the project can move forward.

How Does a Good Executive Sponsor Operate?

Good example: A COO who personally removes a blocker in access to operational data. When the AI team can't obtain needed data from the operations department for two weeks, the COO organizes a meeting between IT and operations within 48 hours and personally participates. After the meeting, the data is made available within a week.

Bad example: A CEO who is "sponsor" of 15 different projects and doesn't remember what this AI project specifically does. When the team asks for intervention, the response comes after three weeks and reads: "talk to the department director."

Red Flags – Warning Signs

Pay attention to the following signals that may indicate the Executive Sponsor role isn't properly filled:

• The Sponsor doesn't participate in key project meetings and isn't up to date with its progress
• The Sponsor has no real executive power in the organization – has the title but no influence
• The Sponsor is a "committee" or "board of directors" – meaning effectively no one specific
• The Sponsor is so busy with other responsibilities that the AI project is marginal to them

Qualifying Question

Before you decide that a given person will be the Executive Sponsor of your project, ask yourself one question:

Has this person ever removed an organizational blocker in any project?

If yes – take a closer look at how it looked and how long it took. If no – consider whether there really are no blockers in your organization (which is unlikely), or maybe this person simply doesn't act effectively in a role that requires active intervention (which is much more likely).

Decision Owner: Who Has Ultimate Decision-Making Authority and Bears Its Consequences?

Decision Owner is the person who has formal mandate to make the final decision on key project matters – and who will bear personal consequences if that decision turns out to be wrong or if the decision isn't made in time.

Why Is the Decision Owner Critical?

Because in many organizations, decisions are made by committees, working groups, or through informal consultations among many people. And this, in our view, is one of the more significant causes of decision paralysis in AI projects.

A committee doesn't bear responsibility – responsibility is diffused among all its members. No one is personally accountable for the outcome, so no one has sufficient motivation to make a risky decision. It's easier to "wait for more data," "consult with additional experts," or "analyze alternative scenarios" – and the project stands still.

A specific person – the Decision Owner – bears responsibility. They have a face, a name, and an annual review where the project's outcome will appear. This changes the dynamics of decision-making.

Main Tasks of the Decision Owner

• Making key decisions about the project scope, technical architecture selection, integration with existing systems, timing and method of scaling. These are decisions that determine the project's direction and chances of success.
• Accepting responsibility for consequences – if the decision turns out to be wrong, the Decision Owner is accountable. They don't hide behind the team, a committee, or external circumstances. This responsibility is the price for decision-making authority.
• Resolving disputes – when the team has different visions, when experts disagree, when conflicts of interest arise between departments, the Decision Owner makes the final decision and closes the discussion.
• Maintaining momentum – prevents "analysis paralysis," the situation where the project gets stuck in endless analyses and never moves to action. The Decision Owner knows when there's enough information to make a decision.

How Does a Good Decision Owner Operate?

Good example: A VP of Operations who decided that the AI demand forecasting system would be deployed first in two pilot regions, and then – after verifying results – scaled nationwide. They took personal responsibility for results in those regions and committed to monthly progress reports to the board.

Bad example: A "steering committee" of seven people that meets once a month and "discusses" next steps. Every meeting ends with a list of "points to clarify" and another meeting in a month. No one makes decisions because no one wants to take responsibility for them.

Difference Between Decision Owner and Executive Sponsor

These two roles are often confused, but they serve completely different functions:

• Executive Sponsor = removes organizational blockers, provides political support, opens doors. Operates "outside" the project.
• Decision Owner = makes operational decisions about the project itself, bears consequences for the outcome. Operates "inside" the project.

These can be different people – and often should be, especially in large organizations. The Executive Sponsor may not have the time or competence to make detailed operational decisions. Meanwhile, the Decision Owner may not have sufficient position to remove blockers at the organization-wide level.

However, in smaller organizations or in smaller-scale projects, the same person may fulfill both roles. This is acceptable as long as the scope of responsibility for both roles is clear and properly executed. It's important that this person is aware of when they're acting as Sponsor (removing a blocker) versus when they're acting as Decision Owner (making an operational decision).

Qualifying Question

Before you conclude that your project has a Decision Owner, honestly answer one question:

If the AI project stalls due to lack of key decisions – who specifically will bear consequences in their annual review?

If the answer is "the whole team," "hard to say," or "it depends on the reasons for failure" – you don't have a Decision Owner. You have a group of people who are involved in the project, but none of them are responsible for it.

Risk Owner: Who's Responsible for Compliance, Security, and Reputation?

Projects using artificial intelligence involve new types of risk that don't occur – or occur to a much lesser extent – in traditional IT projects. We're talking about reputational risk (what if AI says something offensive?), regulatory risk (how to ensure compliance with GDPR and AI Act?), ethical risk (are our models discriminating?), and operational risk (what if the model stops working correctly?).

Risk Owner is the person responsible for ensuring the AI project doesn't harm the organization – either in the short or long term.

Main Tasks of the Risk Owner

• Managing compliance risk – ensuring compliance with GDPR, AI Act, industry regulations (e.g., in the financial or medical sector), and the organization's internal policies. This isn't just a one-time analysis but continuous monitoring.
• Protection against reputational risk – what happens if AI makes a controversial decision that ends up in the media? What if it gives a customer false information? The Risk Owner must anticipate such scenarios and prepare the organization to handle them.
• Data and model security – protection against data leakage, attacks on the model (adversarial attacks), model quality degradation over time (model drift). These are technical risks that require collaboration with IT security teams.
• Auditability and transparency – can the organization explain why AI made a specific decision? This is increasingly important in the regulatory context, but also in terms of customer and employee trust.

Why Is the Risk Owner Not Just "Legal & Compliance"?

It's tempting to assign the Risk Owner role to the legal or compliance department. After all, they deal with risk, right? Not entirely.

Risk in AI projects isn't just legal risk. It's also:

• Business risk – what if the model stops working correctly and customers lose trust in it? What if competitors exploit our mistakes?
• Technical risk – what if the model starts hallucinating? What if the model provider's API stops responding for an hour? What if changing to a newer model causes degradation in system performance? What if the data we use in the process turns out to be insufficient or incorrect?
• Operational risk – what if AI does something that paralyzes a key business process?

The Risk Owner must think holistically – see the full picture of risk, not just its legal slice. They must collaborate with legal, IT security, operations, PR, and other departments, integrating their perspectives into a coherent risk management strategy.

How Does a Good Risk Owner Operate?

Good example: A Chief Risk Officer who personally reviews the model testing plan in the context of edge cases and failure scenarios. Collaborates with the legal department on documenting decisions made by AI so the organization can explain them in case of an audit or complaint. Establishes specific procedures with operations for handling anomalies in model behavior. Ensures proper system "observability" (logging system, such as Langfuse) and its evaluation (periodic tests on benchmark data + quality assessment).

Bad example: A "compliance team" without a specific person responsible, who "will monitor the situation" and "prepare recommendations if problems arise." No one is personally responsible, no one takes proactive action, and problems are only addressed after the fact – if at all.

Qualifying Question

Answer one question:

If AI makes a mistake that leads to financial loss or a reputational crisis – who will have to stand before the board and explain what went wrong and what remedial steps have been taken?

If you don't know a specific name – you don't have a Risk Owner. At best, you have hope that nothing bad will happen.

Economic Owner: Who Defends ROI and Has AI in Their P&L?

Economic Owner is the person who has the AI project written into their budget (P&L) and who must defend the return on investment at the board level. This role forces treating AI not as a technological experiment but as a business investment with specific financial expectations.

Why Is the Economic Owner Key?

Because AI costs money. Infrastructure, data, talent, integration with existing systems, maintenance and development – all of this generates costs, often significant ones. And if no one has these costs in their P&L, the project quickly becomes an "IT cost" or "innovation investment" that never demonstrates real ROI.

The Economic Owner fundamentally changes how people think about the project. The question stops being "is AI interesting?" or "are we innovative?" and becomes "does AI pay off?" and "when will we see return on investment?" These are brutal but necessary questions if the project is to survive the next round of budget cuts.

Main Tasks of the Economic Owner

• Defining the project's financial model – how much the project costs (one-time and ongoing), how much value it will generate (savings, additional revenue, avoided costs), when it will pay off. This must be calculated and documented, not based on general promises.
• Defending ROI before the board – explaining the project's business value in financial language that the CFO and CEO understand. Not in technological language ("we have the latest LLM model"), but in business language ("we reduced customer service costs by 15%").
• Monitoring return on investment – regularly tracking costs vs. value generated over time. Are we on track to achieve the assumed ROI? Have unexpected costs appeared? Is value materializing according to plan?
• Deciding on scaling or withdrawal – if the project isn't delivering value and has no real chance of improvement, the Economic Owner makes the difficult decision to close it. This is painful but better than continuing a project that only generates costs.

How Does a Good Economic Owner Operate?

Good example: A VP of Finance who has the AI project written into their P&L and is held accountable for it. Regularly reports ROI to the board using specific numbers. Can show how much AI reduced operational costs or increased revenue compared to a scenario without AI. When results are worse than assumptions, initiates discussion about causes and possible corrections.

Bad example: "The CTO is responsible for project finances" – but the AI project isn't part of their budget, so they don't bear real financial consequences. Costs are "somewhere" in the IT budget, and business value is "difficult to measure." No one can say whether the project pays off.

Difference Between Economic Owner and Decision Owner

These roles address different dimensions of project success:

• Decision Owner = responsible for operational success. Does the project work? Does it achieve its functional goals? Is it implemented according to plan?
• Economic Owner = responsible for financial success. Does the project pay off? Does it deliver value greater than costs incurred?

Most often these are different people because they require different competencies and perspectives. The Decision Owner thinks in terms of "does this work and does it solve the problem," the Economic Owner thinks in terms of "does this pay off and is it worth continuing the investment." Both perspectives are needed.

Of course, it may happen that the same person fulfills both roles – especially when the project is smaller or when the organization is less complex. However, it's important that this person consciously switches between both perspectives.

Qualifying Question

Answer honestly one question:

Who will have to explain to the CFO why the AI project didn't pay off in the expected timeframe?

If the answer is "not applicable, this is an innovation project" or "hard to say because we don't have a defined ROI" – you don't have an Economic Owner. And you probably don't have a plan for how to measure your project's business value – which means the project is particularly vulnerable to budget cuts.

How to Use AI Transformation Canvas in Practice: The Role Assignment Stage

You already have the AI Transformation Canvas. You have basic information about each of the four key roles. Now it's time to move from theory to practice and actually fill these roles in your project.

Step 1: Complete the "Ownership & Accountability" Section in AI Transformation Canvas

Take the Canvas and find the section on ownership. Ask yourself four questions – and for each role, write a specific name, not a position or department:

1. Executive Sponsor: Who will remove organizational blockers and ensure cross-functional support when the project gets stuck?
2. Decision Owner: Who has ultimate decision-making authority on key matters and bears personal consequences for the project's results?
3. Risk Owner: Who's responsible for compliance, security, and reputational risk? Who will stand before the board if something goes wrong?
4. Economic Owner: Who has AI in their P&L and will defend ROI at the board level?

If you can't write a specific name for any role – that's a signal that you must first solve this problem before the project moves forward.

Step 2: Do a "What If" Test

Just writing names isn't everything. You need to verify that these people will actually be able to fulfill their roles when needed. The best way is to conduct a simple scenario test.

Below you'll find example scenarios you can use. Of course, it's worth finding more – the more scenarios you analyze "dry run," the better prepared your governance structure will be for real challenges.

Scenario 1: The AI project needs access to data from another department, but that department has its own priorities and isn't willing to cooperate. The director of that department claims they "don't have resources" to prepare the data. What happens?

→ The Executive Sponsor should intervene and resolve the impasse. Who specifically will do this? How quickly? What tools do they have?

Scenario 2: The AI model gives unexpected results – significantly different from what you assumed. The team doesn't know whether to continue the current approach, change the model, or perhaps return to the data analysis stage. Expert opinions are divided. What happens?

→ The Decision Owner should make a decision and end the discussion. Who specifically will do this? On what basis? How quickly?

Scenario 3: A potential GDPR compliance risk emerges – someone noticed that input data may contain information that shouldn't be processed this way. The team isn't sure whether this is a real problem or a false alarm. What happens?

→ The Risk Owner should assess the risk and decide on remedial measures. Who specifically will do this? What competencies do they have? Who will they consult with?

Scenario 4: Six months after project start, the board asks whether AI is paying off. The CFO wants to see specific numbers. What happens?

→ The Economic Owner should present ROI with specific data. Who specifically will stand before the board? What numbers will they present? Where will they get them from?

If you can't answer these questions with a specific name and describe a specific way of acting – the roles aren't well filled. You have names on paper, but you don't have a functioning governance structure.

Step 3: Formalize the Team

Project success largely depends on whether you build alignment with key people from day one. Among these people, definitely in first place are precisely these four roles you've just established. But "verbal agreement" to join the project isn't enough.

You must formalize these people's involvement in the project. This means clearly establishing and documenting:

• Who fills which role – not "who's involved," but who has specific responsibility
• What authority and scope of responsibility they have – what this person can decide independently, and what requires escalation
• How often they report and in what form – whether it's weekly meetings, monthly reports, or ad hoc as needed
• Who they report to – what's the escalation path when problems arise

As a result, you should have a document or slide in a presentation that every project participant understands and accepts. This isn't about bureaucracy for its own sake – it's about everyone having clarity about the accountability structure.

Equally important: the scope of responsibility for each of these people should be clear and officially written into their job duties. A scenario where someone agreed "on the side" to help you with this project alongside their regular tasks is definitely not good. When priority conflicts arise (and they will), such a person will always choose their "official" duties at the expense of the AI project.

FAQ: Most Common Questions About AI Governance Roles

Can the same people fill more than one role?

It depends on the organizational context.

In small organizations or in smaller-scale projects – yes, they often must. It doesn't make sense to engage C-suite people for a pilot project. But even if one person fills several roles, always document which role they're acting in at any given moment. This helps avoid misunderstandings and conflicts of interest.

Most risky combinations:

• Decision Owner + Risk Owner – this is a potential conflict of interest. The person responsible for project results may tend to underestimate risk so the project can move forward. The Risk Owner should be able to say "stop" even if the Decision Owner wants to continue.
• Economic Owner + Decision Owner – risk that decisions will be made solely from the perspective of short-term ROI, ignoring other important factors. Pressure to demonstrate quick returns can lead to bad operational decisions.

Beneficial combination:

• Executive Sponsor + Economic Owner – the project can benefit if a person at the COO or CFO level takes full responsibility both financially and organizationally. Such a person has both the resources and motivation for the project to succeed.

What If We Don't Have Someone at the C-Suite Level Who Could Be Executive Sponsor?

This is a red flag. If no one from the highest management level wants or can engage as Executive Sponsor, it's a sign that the AI project isn't treated as a strategic priority. And this significantly reduces its chances of success.

Alternative: The Executive Sponsor can be a VP or Director with sufficient mandate – but this person must have real executive power in the organization and direct access to the board. If they have to go through three levels of hierarchy to get anything done, they won't be an effective Sponsor.

Are These Roles Needed in Every AI Project?

It depends on the project's scale and stage of development.

• Pilot phase / proof of concept: You can skip full formalization, but you should know who de facto makes decisions and who's responsible for results. Even informally – you must have answers to the four fundamental questions.
• Scaling (impact on core business): Yes, these roles are absolutely needed. Without them, the project may not break through organizational blockers, may not obtain needed resources, and may be stopped at the first difficulties. The larger the project, the more formal the governance structure must be.

Who Should Fill These Roles?

Usually the project lead or person responsible for the AI initiative does this. But they can't do it alone – they must have agreement from the people they're engaging in the project.

Moreover, informal agreement isn't enough. This must be a decision officially writing these roles (in this specific project) into the scope of responsibility of the people involved. This means a conversation with these people's supervisors, updating annual goals, including the project in job responsibilities. Without this formalization, roles will remain on paper, and in practice no one will feel truly responsible.

Summary: From Planning to Action

In summary: AI projects rarely fail due to insufficient technology. The cause of failure is often a lack of defined scope of responsibility in the project and thus a lack of decision-making authority and proper organizational management.

AI Transformation Canvas is a tool that will help you solve this problem while still in the project planning phase – before these problems actually occur.

The Canvas imposes a structure that forces answers to four fundamental questions:

1. Who has ultimate decision-making authority? → Decision Owner
2. Who's responsible for return on investment? → Economic Owner
3. Who manages risk? → Risk Owner
4. Who will remove organizational obstacles? → Executive Sponsor

If you can't answer these questions with specific names – in our view, the project isn't ready for scaling. It may work in the pilot phase, may even deliver promising results, but collision with organizational reality will probably stop it.

INTRODUCTION

Why do companies invest in AI and see no results?

Most organisations that leverage AI with varying degrees of success don't build or train their own models from scratch. In business practice, companies typically use ready-made, publicly available models—whether large language models or specialised AI tools. These models come pre-trained and off-the-shelf, which might suggest that data becomes a secondary concern. This is a fundamental misconception.

Even the best AI model is useless without access to the right data. A language model may be brilliant at understanding language, but if it's meant to create or translate content aligned with a brand's tone of voice, using the correct terminology and preserving all the nuances that matter to the business, it needs access to information about the brand, products, and target audiences, detailed content guidelines, glossaries, and more. A risk analysis model may be highly sophisticated, but without complete and consistent financial data, it will generate worthless results. The quality of what AI delivers in any given process is directly dependent on the quality of the data feeding into that process.

In an IBM study (IBM GLOBAL AI ADOPTION INDEX – ENTERPRISE REPORT, 2023), the second most common barrier to AI adoption cited by respondents was excessive data complexity (24% of all responses)—right after the lack of AI-related skills, experience, and knowledge (32%).

At the same time, many organisations make the same mistake—they invest in models and technology first, only to discover later that their data isn't ready. This is the inverse of the approach taken by companies that achieve real business value from AI. The success of an AI implementation doesn't depend on technology; rather, it hinges on organisational maturity, and above all, on how the organisation approaches its data.

Data as a Strategic Asset

AI transformation has forced many organisations to rethink how they view data. Many companies treat data as a by-product of operations—something that simply emerges in the course of doing business. ERP systems generate transaction data, CRMs collect customer information, and so on, but these resources are often not treated as assets requiring deliberate and consistent management.

This perspective must change in the context of AI implementations: organisations that treat data as a strategic asset and invest in managing it deploy AI faster and achieve better results. This isn't a technical matter—it's a strategic one, requiring decisions at board level.

In AI projects, to simplify somewhat, we can talk about two types of data:

• Data for training models – this is the domain of companies building their own AI models. It requires vast historical datasets for the model to "learn" from. Most organisations implementing AI don't need to deal with this—they use models already trained by specialised technology companies.
• Data needed to run AI processes – this is the domain of every organisation implementing AI. Even an off-the-shelf model needs data to perform a specific task. An AI assistant needs access to the company's knowledge base. A recommendation system needs data on customer preferences. A reporting automation tool needs up-to-date financial data. Without this data—or with poor-quality data—the AI process won't generate value.

Referring to the AI Transformation Canvas we've prepared for our clients, the key questions for strategic planning are:

• What data assets already exist that are critical for this use case?
• Who owns the data?
• How complete, current, and consistent is the data?
• What data gaps could limit success?
• What risks exist around data quality or access?

In the remainder of this article, we'll explain what these questions really mean and what's worth considering at the planning stage.

Trust in AI Starts with Trust in Data

AI is built on statistics. A model's job is to predict, in the context of a query, the most semantically relevant token. At the same time, an AI model makes its predictions based, among other things, on the data it receives in the process. In most cases, AI won't be able to detect that the quality of the data you're providing has dropped, that it's outdated, or that errors and gaps have crept in. Trust in data is therefore the foundation for trust in AI. This is a fundamental relationship that organisations often overlook.

The credibility of results delivered by a model has two sources:

1. The quality of the model itself – does it "understand" the task, does it process information correctly? When using proven, off-the-shelf models from reputable providers, appropriately matched to the task, this source of risk is relatively well controlled.
2. The quality of input data – has the model received the right, complete, filtered (task-appropriate), and up-to-date information to process in a given workflow? This source of risk lies with the organisation implementing AI.

For example: a company implements AI Translation Engine—a translation tool powered by AI. The language model used by the translation engine is excellent at understanding languages and delivers pretty good translations right out of the box. However, like any translator, it needs detailed guidelines to know how to translate technical terms, what not to translate, or, for instance, what tone of voice the company uses when communicating with customers on Instagram. It learns all this from the guidelines it receives and stores in its memory. If those guidelines contain errors, or aren't regularly reviewed and updated, translation quality may decline over time.

Four Dimensions of Data Readiness for AI Processes

In the AI Transformation Canvas, you'll find space to answer a crucial question—is your data ready for AI? More specifically, do you have data that will be sufficient for the particular process you're planning, and can you actually use it?

But what does "data ready for AI" actually mean? We can talk about four key dimensions that determine the quality of data needed to execute a process: completeness, currency, consistency, and accessibility.

Completeness – Do we have all the data we need?

Data completeness isn't just about "whether data exists," but "whether we have all the data needed to perform the task." An AI process is only as good as the data feeding it.

Example: we're implementing an AI system for sales lead qualification. The system needs information about lead source, industry, company size, and interaction history. If the CRM consistently lacks industry information (because sales reps don't fill in that field), the model won't be able to factor this criterion into qualification—even if industry is an important consideration in sales potential analysis.

Currency – Does the data reflect reality?

In AI processes, outdated data is just as problematic as incomplete data. The model operates on what it receives—it doesn't know the information is a year old.

Example: an AI Support Assistant supporting the customer service department draws information from systems such as ERP, CRM, OMS, and the product database. If, for instance, a product price changed two days ago but the product database hasn't been updated, the assistant will use outdated information. The problem doesn't stem from model limitations—it stems from how the data update process works.

Consistency – Do data from different sources tell the same story?

Most AI processes require combining data from multiple systems. If the same information is recorded differently in different places, the AI process receives conflicting signals or may not be able to use such data at all.

Example: an AI system for customer value analysis pulls data from CRM (contact details, interaction history), ERP (order history, payments), and the support system (ticket history). In the CRM, the customer is recorded as "ABC Ltd.," in the ERP as "ABC Limited," and in the support system as "ABC Company". Is this one customer or three? Without a consistent identifier, the AI model may treat one customer as three different ones.

Accessibility – Can data be delivered to the AI process?

Data may be complete, current, and consistent, but inaccessible to the AI process. The reasons are most often technical (e.g., no API, incompatible formats), organisational (data in silos, no authorisation to share), or legal (GDPR restrictions, confidentiality agreements).

Example: A company wants to implement an AI assistant to help customer service staff quickly find information about products, complaints procedures, current offers, and typical problem-solving scenarios. All this information theoretically exists within the organisation—scattered across dozens of Word documents, Excel spreadsheets, presentations, and internal wikis. The problem is that these materials aren't available to the AI process in a usable form. Each document has a different structure and contains formatting elements, graphics, and tables with inconsistent layouts. Extracting knowledge from them would require costly and time-consuming processing of each file individually. For the AI assistant to effectively answer employee questions, the company must first transform this scattered knowledge into a structured database—for example, a collection of Markdown files with a clear topic hierarchy and consistent formatting conventions. Only then will the data become truly accessible to the AI process.

The Data Silos Problem – A Barrier to AI Processes

When implementing AI in a large organisation, you often encounter yet another barrier. AI models are frequently capable of performing complex tasks, but they need access to diverse data. The more advanced the use case, the more data sources must feed the process. And data in organisations is often scattered across silos.

A typical scenario might look like this: a company wants to implement AI to predict which customers are likely to cancel their subscription. To do this, the AI process needs information from multiple sources:

• Purchase history (sales system)
• Customer service contact history (ticketing system)
• Payment history and any delays (finance system)
• Activity in digital channels (marketing automation system)
• Complaints history (quality management system)

Each of these systems is managed by a different department. Each has different formats, different standards, different update cycles. Combining this data into a coherent customer picture that can be delivered to the AI process is an organisational challenge, not a technical one. No AI model, no matter how good, will solve the silos problem.

The question from the AI Transformation Canvas: "What data gaps could limit success?" often reveals silos alongside gaps in essential data. The data theoretically exists within the organisation, but is inaccessible to the AI project because it "belongs" to another department.

Personal Data and GDPR – The Hidden Risk in AI Implementations

There's another issue that's often overlooked in the early phases of AI projects, yet can have serious legal and business consequences: personal data protection in the context of using AI tools.

Many AI tools, particularly those based on large language models, operate on a cloud model. Data we send to such tools—whether as queries or documents for analysis—may be processed on the provider's servers. What's more, some providers reserve the right to use data submitted by users to further train and improve their models.

If an employee pastes into an AI tool the content of a customer email containing personal data, a contract excerpt with contractor details, or a client list with phone numbers—this information may end up in an external provider's systems. In the worst-case scenario, it could be used to train the model, meaning fragments of this data could "leak" into responses generated for other users.

GDPR places an obligation on data controllers to maintain control over where and how personal data is processed. Transferring data to an external AI tool without appropriate safeguards may constitute a regulatory breach—with all the consequences that entails (financial penalties, reputational damage, claims from data subjects).

Before implementing an AI process that uses customer or employee data, an organisation should establish clear rules for data anonymisation or pseudonymisation. This means removing or masking information that allows individuals to be identified before the data reaches the AI tool. It's an additional step in the process that requires careful thought and often automation, but it's essential for the company's legal security and ethical operation.

Who Is Responsible for the Data Feeding AI?

Companies that succeed with AI have clearly defined roles in the area of data management. For clarity, we can divide them into: data owners, data stewards, and process owners. In the context of implementing ready-made AI models, these roles are just as important as when a company creates and trains its own model.

An AI model is something users don't fully understand. They often don't know how it works or grasp its mechanisms. And certainly, no user of a publicly available AI model has any influence over how it operates. However, as users, we do have influence over many things. These are primarily: carefully thinking through the process, properly matching the model to the task, and ensuring the quality of input data. If the output of an AI process is poor, the first question should be: "Did we provide the right data?" And to answer that, there must be someone responsible for that data.

We can identify three key roles in data management processes:

Data Owner – the person responsible for a given dataset. They decide what data is collected, who has access to it, and what quality standards apply. In the context of AI: they decide whether data can be used in an AI process and under what conditions.

Data Steward – the person who looks after data quality on a daily basis. They monitor completeness, currency, and consistency. In the context of AI: they ensure the quality of data feeding the process.

Process Owner (AI Process Owner) – the person responsible for the entire business process that utilises AI. They understand what data the process needs and collaborate with data owners to obtain it.

In many organisations, the problem is that nobody formally "owns" the data. IT is responsible for systems (infrastructure), business uses the data, but no one is accountable for its quality. When implementing AI, this can be a significant blocker to progress.

Data Management Is a Board-Level Responsibility, Not IT's

There's a fairly common belief that data management is IT's domain. In the context of AI implementations, this approach can be fatal. The lack of an organisation-wide data acquisition and management strategy makes it difficult to scale AI projects, and as a result, they often fail to progress beyond the pilot stage.

Data management requires C-level engagement in several areas:

1. Strategic decisions: Which data is most important for planned AI processes? Which datasets should be invested in first? What data is missing and how can it be obtained? These are decisions about resource allocation and priorities—they cannot be delegated to IT.
2. Organisational decisions: Breaking down silos requires intervention at the organisational structure level. A CIO typically doesn't have the formal authority to mandate that the sales department share data with marketing. No AI project leader can force the finance department to provide payment data. These decisions must come from the top.
3. Cultural decisions: Building a culture of data accountability is a long-term process. It requires communication, motivation, and enforcement. This is a process that demands management at the organisation-wide level.
4. Budget decisions: Getting data in order requires investment. Companies that treat data as a strategic asset and invest in managing it achieve better results with AI. But this investment must be a conscious decision by the board.

This is why one of the key roles to fill in an AI project (the Ownership & Accountability section in the AI Transformation Canvas) is the Executive Sponsor—a person who "removes organizational blockers." Data silos and the lack of data governance are undoubtedly such blockers.

A Practical Framework for Assessing Data Readiness

To make it easier to assess whether your organisation's data is ready for AI, we've prepared a simple framework. You'll find it reflected in the AI Transformation Canvas in the form of questions you should answer before starting a project.

Question 1: What data is critical for this use case?

This is a question about consciously defining requirements. It's not about what data we have—it's about what data the AI process needs to generate value. This requires understanding the logic of how the process works: what information enters the process, what happens to it, and what the end result is. Before you ask "do we have this data?", consider what data you actually need. The list may be shorter than you think—or longer.

Question 2: Who owns this data?

This is a question about accountability. Is there a person responsible for each required dataset? Does this person know their data will be feeding an AI process? Do they agree to share it? If the answer is "probably IT" or "no one specifically," this is a problem that needs solving before the project starts.

Question 3: How complete, current, and consistent is this data?

This is a question about quality. It's not enough for data to exist—it must be usable. Completeness: do we have all the necessary fields for all the necessary records? Currency: when was the data last updated, what's the refresh cycle? Consistency: is data from different sources aligned? Don't rely on declarations—conduct a data quality audit on a sample. Reality often differs significantly from assumptions.

Question 4: What data gaps could limit success?

This is a question about consciously identifying gaps and planning how to address them. Gaps can be absolute (the data doesn't exist and needs to start being collected), relative (the data exists but isn't of sufficient quality), or temporal (the data will be available, but not at project launch). Each identified gap requires a decision—can the project proceed regardless; can gaps be filled in parallel?

Question 5: What risks are associated with data quality or access?

This is a question about risk management. Data can carry legal risks (GDPR, intellectual property), reputational risks (sensitive customer data), operational risks (dependency on an external provider), or quality risks (issues with the data source). Identified risks should be recorded in the "Key risks & constraints" section of the AI Transformation Canvas, with assigned management strategies.

Data as a Prerequisite for AI Success

In the era of ready-made, publicly available AI models, competitive advantage doesn't lie in technology. Everyone has access to the same models. The advantage lies in data—its quality, accessibility, and integration. A company with well-organised data can quickly implement an effective AI process. A company where data is collected and managed chaotically can experiment for years without achieving expected results.

To summarise the key takeaways:

1. Data fuels the AI process. The quality of input data that the process relies on directly translates into the quality of results.
2. Trust in AI requires trust in data. Without reliable, well-prepared data, even the best AI model won't deliver high-quality output.
3. Completeness, currency, consistency, and accessibility—these are the four dimensions of data readiness that must be assessed before every AI project.
4. Data silos are an organisational barrier, not a technical one. Breaking them down requires decisions at board level.
5. Data anonymisation is a necessity, not a formality. Data sent to external AI tools may be used to train models—without proper anonymisation procedures, you risk GDPR violations and uncontrolled leakage of information about your customers and company.
6. Data management is a C-level responsibility. Companies without a data management strategy often stall at the pilot stage due to data not being ready for project needs.
7. The five questions from the AI Transformation Canvas in the Data section provide a practical framework for assessing data readiness—use it before every project.

A thorough analysis, which you can conduct with the help of the AI Transformation Canvas, can protect you from costly failure—or give you confidence that your project has truly solid foundations.

Planning to implement an AI project in your organisation?

To help you make the right decisions and properly prepare for this project, we're sharing our proprietary AI Transformation Canvas by Shiftum.

In the canvas, you'll find:

• a ready-to-use framework covering all the key areas of AI project planning
• a blank version, ready to print and work with
• a version with guiding questions for each section, designed to help you understand what information matters at every stage of planning

For many years, we've been running a digital marketing agency. When ChatGPT came along, we bought access for the team. We expected faster work and better quality content. Instead, we got twenty different styles and results we couldn't control.

So we decided to approach this systematically. Over the past few months at Shiftum, we've been building an internal AI Content Engine - a system that generates over 150 content assets from a single source material in less than 10 minutes.

This article is a collection of lessons we learned along the way.

If you're managing content at a company with tens of thousands of SKUs and wondering whether AI can help generate content faster and better - this text is for you.

One Product, Dozens of Content Pieces

Selling and promoting a product online isn't just about a description and a few photos in your store. It's an entire ecosystem of content that needs to be created before a customer even sees your offer.

Let's take a simple example: dog food. To sell it online, you need:

• Product description for the store
• SEO title
• Meta description
• Tags and categories
• Alt descriptions for images
• Bullet points for Amazon (if you sell there)
• Social media posts
• Newsletter content
• Ad copy

So one product requires creating anywhere from a few to dozens of additional assets to support its visibility and promotion. Now multiply that by the tens of thousands of SKUs that an average online pet store carries.

That's incredibly difficult to do manually. At least not within a reasonable timeframe and budget.

The Solution That Doesn't Work

Most companies try the same thing we did: give the team access to ChatGPT and hope productivity shoots up.

And sure enough - every copywriter, marketer, and content manager starts using AI. The problem is that everyone does it their own way. They paste a command like "write me a product description for the store" into the chat window, add a PDF brochure or a spreadsheet with parameters, and expect a finished piece.

Sometimes it works satisfactorily. But usually not.

Why Chat + Manual Work Doesn't Lead to Better Results

There are several main reasons why a person working manually with an AI chat interface rarely achieves good results:

1. Not enough context for the model. AI gets bare technical parameters and is supposed to create an engaging description from that. It doesn't know the customer persona, the brand's tone of voice, doesn't know what problems the product solves. The result? Generic texts that could describe any dog food.

2. Cluttered context. The user dumps in a 100-slide product presentation because "everything's in there." They add a few more company presentations about clients and communication styles. AI processes it all, even though maybe 5 slides are important. The rest is noise that lowers the quality of responses. The result is very chaotic and often random content that may or may not meet user expectations.

3. Trying to take shortcuts. People assume that since they can go directly from a messy product sheet to a good e-commerce description in one work cycle, AI can too. It can't. AI is great at transforming content, but weak at creating complex materials from scratch. A typical model works best when guided through the generation process step by step - first a description template, then main points, then paragraph 1, and so on.

4. The speed vs. quality dilemma. This is where we get to the main scalability limitation. If you want to work fast (one prompt, shortcuts) - quality drops dramatically. If you want to work with quality (you guide the model step by step) - you need to prepare context so precisely and iterate so carefully that you don't increase content production scale. AI doesn't speed up work, it just moves it to a different place, which is usually the chat interface.

5. Losing context in long sessions. You try to generate all assets in one conversation and guide the model very precisely? You might find that after 6-7 iterations, AI starts internally compressing data from your conversation. As a result, the quality of subsequent generated elements drops because AI "forgets" your earlier agreements.

6. Repeating the process for every content type. Even if you have perfectly prepared context, for each new product you have to manually repeat the entire process. Some AI chat interfaces make this easier by creating "Projects" or "Gems," but each time it's still manual work. This approach doesn't scale well with thousands of products.

Conclusion

Chat plus manual process gives you a choice: either low quality or low speed. You can't have both.

The question is: is there another way?

There Is Another Way: 150 Quality Assets in Less Than 10 Minutes

At Shiftum, we're building an AI Content Engine - a system for producing content at large scale. I'll show you how it works using our own process as an example.

One of our clients runs a podcast as part of promoting their company. A typical episode is about 30-45 minutes of recording. Before we publish it online, we need to create many different assets: titles, descriptions, posts for various social media platforms, quotes, key takeaways, promotional materials, transcripts, chapters and timestamps, ideas for promotional reels, etc.

It used to take the team 8-12 hours to create a complete set of materials promoting one episode. Today? About 2-3 hours. AI system work cost: less than 2 dollars.

How It Works

About 30 specialized AI units work on each episode. We call them our "young, dynamic team of interns" ;) Such a team is typically built for a client's project and specialized in creating assets only for them.

Each "intern" has only one task and does it well because they have access to precise guidelines and source data. One extracts key quotes from the material. Another writes draft LinkedIn posts. Another prepares Instagram versions. Each generates several variants of their asset or ideas for communication.

People haven't disappeared from this process - they've changed roles. Instead of creating content from scratch, they evaluate the system's proposals, select the best variants, refine details, and plan publication.

The Numbers

What Changed

The team's work has undergone a fundamental transformation:

Before: Physically generating content from scratch. A copywriter sits, listens to the recording, reads the transcript, takes notes, writes posts.

Today: Quality oversight. A person reviews proposals, selects the best ones, corrects nuances, decides on publication. There are still a few assets we create manually from scratch, like blog posts based on episodes, but there are fewer and fewer of them.

This is an important distinction. We haven't fully automated publication - I don't think that would make sense in the near future. We've automated the most time-consuming part: first drafts.

The system's work results are qualitatively very close to what copywriters created from scratch on the same materials. The difference is that instead of 16 hours of work, we have 10 minutes of generation plus 2-3 hours of review, refinement, and planning.

Three Pillars of Creating Quality AI Content at Scale

Before you start using large language models to generate content at scale, you need to prepare three things. Without them, even the best AI model on the market will produce mediocre results.

1. Content Map and Quality Guidelines

Start with a simple question: what exactly are we creating for each product?

The store description is just the tip of the iceberg. Underneath are dozens of elements people often don't think about: SEO title, meta description, tags, alt descriptions for images, bullet points for marketplaces, variants for different social media channels.

Make a list. Literally. Write down every asset that gets created in the process of introducing a product for sale.

Then, for each element, document the quality requirements:

• How many characters?
• What structure?
• What headers?
• What format?
• What must it contain?

Here's where the first problem often appears: in most companies, these requirements aren't written down anywhere. Copywriters and editors intuitively know what a good asset and its components should look like. They do it well because they have experience. But that knowledge lives in their heads.

For AI to work well, you need to extract that knowledge and document it as detailed instructions. This is work you simply can't skip.

2. Brand Data Repository

Ask yourself: what would an intern need on their first day to create a given asset well?

The list usually looks similar:

• Detailed descriptions of customer personas
• Brand tone of voice (or for a specific product line)
• Examples of good materials to model after
• Information about what differentiates us from competitors

Companies often have these materials. The problem is they're old, not updated, scattered across different folders and documents. Or they exist only in the heads of people who've worked there for years.

Gather it in one place. Update it. Fill in the gaps. This will be your context repository - a knowledge base that AI will draw from for every generated element.

3. List of Intermediate Assets

This is the element most people skip - and that's why they get poor results.

Not everything can be generated directly from product data and rigid guidelines. Some things need to be created earlier to supplement the context of target assets.

Example: you want to generate a Facebook post about new dog food. You can feed AI product data and tell it to write a post. The result will be mediocre.

Better approach: first, based on the product and customer persona, generate 3-5 "theses" - communication angles that might interest a given target group. Let a person review and select and refine the best ones. Only then generate a draft post using the selected thesis as a starting point.

These "intermediate assets" can include:

• SEO keyword research from external tools
• Initial communication angles and marketing theses
• Competitive product analysis
• Extract of the most important product features for a specific persona

These aren't things that can be universally recorded in a knowledge repository and company guidelines. Each time they must be generated or checked dynamically, depending on context. It's also good practice to have them checked and refined by a person before they're used to generate target assets.

Final Step: Generating Quality Content

When you have these three things - an asset map with guidelines, a brand data repository, a list of intermediate assets - you can start building a system for generating content at large scale and very high quality.

The technical side of such a system is a topic for a separate article, but in short: it's about choosing the right models for different tasks, precisely building context for each asset type, and monitoring output quality.

Without the three foundations described above, no system will work well. With them - you have a solid base for scaling.

What We Learned

Building an AI Content Engine was a process of many trials and errors. Below is a list of several important things we discovered along the way.

1. Precision of Context > Amount of Context

When working with an LLM system, the first thought is usually: give AI as much information as possible. Throw in all the brand documentation, all the personas, the complete brand book. The more it knows, the better it will write.

That's a mistake, as I've written about before.

Lots of information in context creates noise. The model gets lost in information overload and produces mediocre results.

Better approach: instead of an entire, highly detailed persona description, we extract only the fragments that are relevant to the quality of a specific asset. If we're writing about customer challenges - we provide the challenges section. If we want to reference their hidden goals - we provide only those.

Result: cleaner context, better output. Bonus: lower token costs when creating content at scale.

2. Progressive Model Usage

Not all assets require the same level of model "intelligence" or "quality" per se.

That's why we built a three-tier system for created assets:

Tier 1: People. Main guidelines, strategy, key decisions - these are always made by people. AI won't replace strategic thinking. Some of the most important assets are also created from scratch by people.

Tier 2: Best models + verification. Materials supplementing context - communication theses, marketing angles, product analysis for a specific persona. We generate them with the best available models, and a person reviews and approves before use. The quality of intermediate assets and communication ideas directly translates to the quality of final content, so we don't leave these elements to chance.

Tier 3: Standard and smaller models. We generate final assets with different models, selected based on their capabilities and quality expectations for a given asset. In practice, this means using small models for simple assets like tags or SEO keywords, and slightly better models for communication content that people see.

This approach optimizes both quality and costs.

3. Intermediate Assets Are Key

The biggest quality breakthrough came when we started generating "intermediate" materials before actual content.

Static context data (personas, ToV, product information) isn't enough. You need dynamic elements - thoughtful theses, selected communication angles, information filtered for specific use.

These elements aren't static so they don't exist in documentation. They need to be generated specifically for a given product and a given persona. And give a person a chance to verify them before they go further.

Limitations of the Current AI Generation

Before we go further, an important caveat: this isn't a magic solution that eliminates all content production problems. The current generation of AI models has a number of limitations that directly affect this type of system.

1. AI Won't Replace People (Yet)

At least not in this generation of technology. The system does great at generating first drafts, but final quality decisions are made by people.

Someone needs to:

• Evaluate whether generated variants are at a sufficiently high level
• Choose the best communication direction
• Refine nuances and details
• Catch errors the model will miss

The nature of work changes, but people's work doesn't disappear.

2. It's Not "Set and Forget"

The system requires ongoing maintenance. Guidelines change. Perfect product descriptions and content formats evolve. Personas need updating. AI models develop and it's worth testing new ones.

If you leave this type of system without "care," quality will slowly decline.

3. Gradation of Automation

Not everything is suitable for full automation. Assets "invisible" to the customer - SEO tags, technical metadata - can be generated with less human oversight and automatically transferred to target systems, for example via API. But everything that directly affects brand perception - product descriptions, marketing communication - requires a human eye.

I'd be cautious about assuming it will be different in the foreseeable future. At least as long as you care about quality.

In Closing

Scaling content production with AI is possible. But it requires a change in approach.

Instead of giving people access to chat and hoping for a miracle, you need to:

• Map exactly what you're creating
• Build a brand knowledge repository
• Design a process with intermediate assets
• Consciously match models to tasks
• Leave room for human verification

It's not an easy path. But the results - 150 assets in 10 minutes instead of 12 hours of work - speak for themselves.

If you want to talk about how to approach scaling content in your company - reach out.

Case study: AI Translate implementation

From this article, you will learn:

• How to build alignment with key stakeholders from day one — IT, legal, marketing teams, and product owners of global systems.
• Why a clearly defined business value (e.g. multi-million euro savings over two years) gives a project credibility and accelerates decision-making.
• How to plan and measure a Proof of Concept (e.g. user rating of 4.4/5, qualitative feedback, identification of weak points).
• How to launch different processes for individual departments during the pilot phase to test AI in diverse — including the most demanding — scenarios.
• Why success requires clear progress criteria and careful scaling instead of a global “big-bang” rollout.

When the client approached us — an international electronics manufacturer operating in dozens of markets — the question we heard was:

“Can AI significantly reduce translation costs?”

The scale was massive: hundreds of products, dozens of markets, millions of words per month.

The traditional model of working with language service providers meant costs measured in millions of euros per year.

This question became the starting point of our shared journey into implementing AI in translation. Instead of immediately thinking about another expensive enterprise-grade system, we looked for a way to prove the value of an AI-powered tool quickly and in a measurable way.

That’s how the project began — with a Proof of Concept delivered in two weeks, demonstrating potential savings of up to 99%, and then evolving step by step into a full production deployment.

AI as an alternative to traditional enterprise systems

Replacing traditional enterprise systems with AI-based solutions is not just a technology project. It is a transformation process that requires trust, change management (alignment), and continuous quality control.

Our experience with AI Translate — an internal translation tool we built for this client — shows that success requires both courage and pragmatism.

1. Build alignment from day one

The key success factor in this project was involving the right people from the very beginning: IT teams (AI, cloud), global system product owners, legal teams, and key marketing stakeholders.

This helped us avoid later roadblocks and ensured that everyone understood both the potential and the limitations of the project.

The business value (“size of prize”) was clear to everyone from the start — in this case, multi-million euro savings over a two-year horizon. This created a strong mandate for the project and enabled fast decision-making.

The case in numbers:

2. Communication is your shield against risk

AI projects are full of unknowns and naturally raise concerns. Risks include, among others:

• output quality — AI can make mistakes, especially in specialist contexts,
• solution stability — will the system work reliably and remain controllable,
• user adoption — employees must trust the new tool,
• integration with existing systems,
• regulations and compliance — e.g. personal data (GDPR, AI Act).

Regular, proactive communication with the business, sharing progress, and maintaining an open feedback loop help minimize these risks and build trust.

Examples of how we addressed security when implementing AI Translate

3. Start small — Proof of Concept

Don’t try to replace the entire infrastructure at once. A PoC shows whether AI delivers real value and where it generates the greatest savings.

In our case, the key PoC KPIs included:

• User satisfaction: target above 4.0 on a five-point scale — we achieved an average rating of 4.4.
• Qualitative feedback: conversations and workshops with users helped identify strengths and weaknesses.
• Identification of weak points: e.g. challenges with niche language pairs (such as Baltic languages) → defined as a development priority.

4. MVP — different workflows for different audiences

The Proof of Concept confirmed the initial assumptions, met KPIs, and generated strong expectations within the organization. Different teams working with translations on a daily basis began reporting their needs — which naturally led us into the MVP phase.

At the MVP stage, we did not limit ourselves to a single translation process. We launched multiple scenarios and workflows — for marketing teams, regulatory affairs, and customer care. This allowed us to:

• validate quality in extreme scenarios (edge use cases),
• test how AI performs across different formats and contexts,
• better adapt the tool to real organizational needs rather than only “ideal” cases.

What do employees gain? Examples from the implementation

5. Scale carefully while controlling quality

Once a solution works in one area, it’s tempting to roll it out globally as fast as possible. But that is the easiest way to lose quality and trust.

A better approach is phased rollout — with clear quality checkpoints, user acceptance metrics, and training that supports the organization.

How to approach enterprise-scale AI implementation wisely? The deployment strategy we adopted

6. Clear progress criteria

From PoC to MVP, from MVP to global rollout — each stage must have clearly defined progress criteria. For AI Translate, these included:

• minimum translation quality (benchmarked against human translations),
• generated savings,
• acceptance of the tool by local teams.

Key takeaways from implementing AI in a large organization

• Change management: AI does not replace people — it changes their role. A transparent approach (“AI supports, humans decide”) increases adoption.
• Data security and compliance: the architecture must account for regulations (GDPR, AI Act).
• Measure not only ROI but also UX: without positive user experience, the project will not sustain itself.
• Think about scalability early: AI is “lightweight” at the start, but enterprise scale increases integration and maintenance requirements.
• Treat AI as a product, not a project: AI systems require continuous learning, updates, and a clear development roadmap.

AI can solve the problem of scale in content marketing and translation. But not through “better prompts” or “general guidelines for ChatGPT.” Maintaining consistency and quality requires a systemic approach based on a deep understanding of processes.

At Shiftum, we build, among other things, AI tools for content creation and localization for large organizations. In this article, I share what we have learned while working with clients — and where to start if you want to introduce AI into your company’s content processes.

The Problem — Operational Chaos at Scale

If you manage content in a multinational organization, you probably know this scenario:

The product team creates a new product description and communication guidelines. Based on that, dozens of derivative assets are produced — product cards for PIM, Amazon listings, email campaigns for different audience segments, Facebook and Google ads, social media posts, and a large amount of supporting content (meta tags, SEO descriptions, etc.). Each content type has different requirements: different lengths, formats, and tone.

Then all of this needs to be translated — or rather localized — into a dozen languages. Because localization is not word-for-word translation. A marketing claim in German may sound completely different from its English original, as it must resonate with local culture and language nuances. Some technology names are not translated at all. Others require adaptation to local markets.

In practice, content creation and localization often look like this: Excel files circulating via email between teams, agencies, and local marketing managers. Scattered guideline documents. Processes that exist mostly in people’s heads.

And this is not a sign of backwardness or outdated organizational processes.

Why Do Companies Still Work in Excel?

There are tools for managing translations — Translation Management Systems such as Phrase, Smartling, or Lokalise. They are technologically mature, offer dozens of integrations, and use AI.

The problem is that they were designed for software localization — managing short strings in applications, working with developers, integrating with Git and CI/CD. They operate in a project-based model: you create a project, upload a file, wait for the translation, and import the result.

Meanwhile, marketing operates in a continuous publishing model:

• On Monday, you publish a post in English
• On Tuesday, you want it in five languages
• On Wednesday, data shows the German version is not performing — you need a variant adapted to local preferences
• On Thursday, you want to combine learnings from all versions and report results

TMS tools are not designed for this flow. Marketing needs the flexibility that… Excel provides.

According to the Nimdzi report “What Buyers Really Want 2025”, enterprise companies working with spreadsheets and email are not acting out of ignorance, but making a rational decision due to the mismatch between available tools and real needs.

What About Using AI in the Form of ChatGPT?

In many organizations, employees try to solve the problem on their own — using ChatGPT or Claude to support content creation and translation.

At a small scale, this works. At a large scale, it creates more problems than benefits:

• Each employee prompts differently, so content is inconsistent.
• Each session requires re-feeding the model with language guidelines.
• In longer sessions, the model loses context and starts hallucinating.
• There is no central place for guidelines, terminology, or translation memory.
• No one controls quality at the organizational level.

Individual use of AI is not a solution to the problem of scale. It multiplies it.

From Chaos to a System — What It Looks Like in Practice

Theory is one thing, but what does a real transformation look like? Let me describe a project we are delivering for a global consumer electronics company.

Starting Point: Two Problems — Cost and Time

The client was spending approximately EUR 400,000 per year on translating product content (PIM) through an external provider. The process itself was automated — the PIM system sent a translation request whenever product data changed. The external system completed the task in 10–15 days, after which the Marketing Manager responsible for a given language received a notification that a translation was waiting for review. After approval (or after 72 hours), the content automatically returned to the PIM.

Technically, the process worked well. But it created two problems: cost (approx. EUR 400,000 per year) and time (10–15 days per request).

This PIM translation process became the trigger — the search for time and cost savings pushed the client to test AI-based solutions.

Other translation processes in the company (email campaigns, ads, content marketing) were handled manually — Excel files and email coordination.

Stage 1: Proof of Concept — Quality Validation

To determine whether AI tools could even be considered as an alternative to existing solutions, we built the simplest possible version of the system:

• A text field to paste content
• Selection of target language and context (what is being translated)
• Selection of the model used for translation
• A database of stylistic guidelines for several languages
• A technical terminology glossary (product and technology names that should not be translated)

At this stage, we had several goals:

• Verify whether AI can generate translations of acceptable quality — equal to or better than existing systems
• Determine which model performs best in this role
• Understand the real cost of such a solution — and the scale of savings achievable at larger volumes

The test results were positive in both quality and cost terms. The client decided to build an extended version of the system.

Stage 2: MVP for Manual Translation Processes

Before touching the automated PIM process, we had to prove the value of the solution in smaller, specialized translation workflows already operating in the organization.

We built an MVP for teams manually translating three types of content:

• Email campaigns (CRM) — translation of customer email campaigns
• Google/Facebook Ads — with control over field length limits
• Culinary recipes — content marketing used in the client’s application

Here, a key learning emerged: we did not try to change the client’s processes to fit the tool.

Teams worked in Excel — pasting source content, receiving translations, pasting them back, and sending them to local marketing managers for review. We adapted our system precisely to this working style:

• Support for structured translations — multiple fields translated together to preserve context
• Control of maximum translation length, as ad platforms do not accept overly long text
• Consistency across related elements within a single translation — headlines, leads, CTAs must sound coherent within an ad or email
• An interface optimized for Excel workflows — structured copy, paste, and CSV export

If we had tried to “revolutionize” the process at this stage and force teams to work differently, the tool would likely not have been adopted.

Stage 3: Automated PIM Integration

Only after the system proved its value in manual workflows did we move to automatic PIM integration.

We replaced the previous vendor with our solution — in a “plug and play” mode, without any changes on the client’s side. The process remained identical:

• The PIM system automatically sends a translation request when product data changes
• Our system detects and translates only changed fields (not the entire product description)
• The Marketing Manager receives a notification that a new translation is waiting for review
• Reviews and optionally edits it (with internal AI support tools such as “re-translate with X considered”)
• Approves — the translation automatically returns to the PIM

The same process. A different vendor. Radically different time and cost.

Stage 4: Changing Manual Processes (in progress)

Now that the system is established and proven, we are beginning to change processes.

We are using the moderator panel concept from the PIM integration to replace manual Excel file exchanges in remaining workflows. Email campaigns, ads, content marketing — everything moves to an internal translation flow with moderation in the system.

This is the moment when process change actually begins — not earlier.

Result: From 10–15 Days to 3 Minutes and Massive Cost Reduction

The entire project — from PoC to production deployment of automated PIM translations — took approximately 12 months, filled with testing, refinement, and close collaboration with individual teams.

Looking at the results, it was worth it:

For a single PIM translation process, we achieved real cost savings: from approximately EUR 400,000 per year to around EUR 4,000 per year. PIM translation turnaround time was reduced from several days to an average of 3 minutes per translation request.

These figures apply only to the single, most measurable translation process in the organization. In reality, there are many more, resulting in significantly greater — though harder to quantify — savings.

More important than the numbers: quality remained at an acceptable level thanks to native-speaker moderation, and the organization can now scale content at a pace previously impossible. The system currently handles around 8,000 translation requests per month, and this number continues to grow as new processes are integrated.

Where to Start — Practical, Step by Step

The case study above shows that transformation with quality preservation is possible. But where should you start if you want to introduce AI into your organization’s content processes?

From our experience, a sequence of four steps emerges. The order matters.

Step 1: Map One Process.

Not all of them. One.

Choose the process that hurts the most — costs the most, takes the longest, causes the most frustration. For our client, it was PIM. For you, it may be something else.

We map processes in two stages:

Stage A: Management Interviews

We start at a high level. What does the workflow look like? Who is responsible for what? Which systems are involved? This provides a general picture.

Stage B: Workshops with Operational Teams

This is where the real work begins. We talk to the people who actually perform the tasks daily. They show us how they work, with what tools, and which rules they follow. We receive concrete examples of content and formats. We enrich the process map with details that managers often lack — because in companies, processes usually exist in people’s heads, not in documentation.

An additional benefit: having real content examples allows us to initially test how AI handles this type of material — before building the system.

Step 2: Understand the Limitations of AI

Before deciding where to plug in AI, you need to understand what AI cannot do.

We often encounter assumptions driven by the massive hype around AI:

Expectation: “We’ll define guidelines once and the system will work.”

Reality: AI model guidelines require regular updates as new products, campaigns, and ideas emerge. This is not “set and forget” — it is a continuous process of updating and quality evaluation.

Expectation: “The system will learn automatically from moderator corrections.”

Reality: AI models do not learn like humans — automatically from corrections or feedback. You can build programmatic mechanisms that simulate this, but it is not magic. It requires deliberate system design around the AI model.

Expectation: “AI will always apply all guidelines we provide.”

Reality: AI models are non-deterministic. Given the same instructions, they may produce slightly different results each time. This means the model may occasionally omit a guideline. Human validation of AI output is therefore essential, not optional.

There is one more limitation that regularly surprises people: AI is bad at counting. Length control (e.g. “maximum 150 characters”) requires programmatic support, as language models are not reliable in this area.

Step 3: Decide Where to Use AI

A key principle to remember: AI excels at transforming content, but performs poorly at creating it from scratch.

AI works well for transforming one piece of content into another:

• Translation and localization are high quality (with good guidelines)
• Derivative content — turning product descriptions into titles, meta descriptions, tags, Amazon bullet points, ad variants
• Format adaptation — the same message, different length, different channel

AI requires caution when used for:

• Niche languages (e.g. Baltic countries) — less training data means lower output quality; strong moderation and very detailed guidelines are essential
• Creative content — AI has more freedom here, but marketing claims often must be standardized and reused consistently across formats and markets, without “creative” interpretation by the model; this requires an additional control layer

AI is not suitable for:

• Creating content from scratch without solid input data — output quality will be very low
• Situations with limited or poor-quality source content — poor input equals poor output, e.g. when product descriptions are based on weak, short “masters” from external providers

Process design takeaway: if you cannot ensure sufficient quantity and quality of input content (guidelines, examples, product data, target groups, content context, channel specifics), you must design the process so that this information is available before AI is used. Otherwise, results will be poor.

Step 4: Detailed Guidelines (Only Now)

Guidelines for AI models are the result of understanding the process in which they operate — not the starting point. That is why this step comes last.

AI guidelines should be built modularly — as separate, specialized blocks that the system dynamically combines depending on the task.

Example guideline structure:

• General brand guidelines (tone, style)
• Product-group guidelines (e.g. coffee machines vs vacuum cleaners — different language)
• Regional guidelines (Europe, Asia, South America)
• Language variants within a country (e.g. Brazilian vs European Portuguese)
• Content-type guidelines (PIM, ads, email campaigns)
• Terminology and marketing claim glossaries
• Translation memory (previous translations and selected examples)

Sounds like a lot of work? It is.

The good news: not everything requires maximum quality and ultra-precise guidelines.

PIM product content reused across many channels — yes, this needs detailed guidelines. Translating user reviews in a store? It must be understandable, not perfect.

Prioritization by content importance helps avoid paralysis in the “guideline preparation” phase and endless evaluation.

There is also a cost aspect: more detailed guidelines mean more input tokens processed by the model, which increases content creation or translation costs. At scale, this has a significant budget impact.

What We Learned

To conclude — a few lessons from real implementations. Some were painful.

1. Augmentation, Not Revolution

The biggest mistake when implementing AI is trying to change organizational processes to fit a new tool.

People do not want to change habits. If a team works in Excel and email, and you introduce a “revolutionary system” that requires a completely new way of working, adoption will fail. The pilot will fail. Everyone will go back to the old way.

That is why our MVP for manual workflows was designed to support Excel, not eliminate it. Only after the tool built value and trust did we begin to gradually change processes.

First, augment existing work. Revolution — if ever — later.

2. General Guidelines Do Not Work

Initially, we thought one set of guidelines would work everywhere.

It didn’t.

Each context requires its own rules. Translating product cards follows different principles than translating creative ads. German-market communication requires different nuances than Brazilian. Coffee machines use different language than vacuum cleaners.

Attempts to apply “universal” guidelines reduced quality relative to team expectations. We had to go much deeper than anticipated.

Yes, we use some universal guidelines in the form of glossaries, but the level of reusability across processes is much lower than we initially assumed.

3. The Hidden Cost of the System

AI in content is not “set and forget.”

There is a cost rarely discussed: maintaining model guidelines. New products, new campaigns, new markets — everything requires updates. Someone must do this. And then validate output quality.

There is the cost of working with native speakers. Moderation requires people who know the language and culture. Sometimes they are available internally, sometimes not.

There is the cost of quality oversight. AI is non-deterministic — outputs must be reviewed, at least in processes where quality and consistency are critical.

This does not mean it is not worth it. It is — we saw cost reductions of over 90%. But expectations about post-implementation effort must be realistic.

4. We Rebuilt the System Three Times

We did not build the final system on the first attempt. Nor the second.

Each time we moved to a new stage (PoC → MVP → PIM integration), new guidelines and expectations emerged that had not existed before. The previous architecture could not support them.

If we were starting again, we would gather very deep guidelines from multiple teams before writing the first line of code.

But also: maybe this is natural. Maybe not everything can be predicted upfront. Maybe it is better to start with a simple PoC and iterate than to try to build a “perfect system” from day one.

Summary

AI can solve the problem of content consistency at scale. We have seen cost reductions of over 90% and turnaround times reduced from days to minutes.

But not through “better prompts” in ChatGPT. Not through “general AI guidelines.” Not through tools that try to revolutionize organizational processes.

What works is a systemic approach:

1. Start with one process and map it thoroughly.
2. Understand AI limitations before building.
3. Consciously decide where AI helps and where humans are needed.
4. Only then create detailed, process-specific guidelines.

And remember: first augment existing work, then — if ever — change processes.

This is not a magic bullet. It is an infrastructure investment that requires upfront effort and ongoing maintenance. But for organizations producing content at scale and in many languages — the investment pays off.

When a leading consumer electronics company asked whether AI could reduce their translation costs, our answer wasn’t immediate. To confirm that the impact could be real — not theoretical — we needed a few fundamentals:

• How much content do they translate?
• What does it cost today?
• Which elements drive most of that cost?
• Which business processes depend on translations?

As we spoke with more teams and learned how the business operated, we realized that the first area with the potential for multi-million-euro savings was clear: reducing the spend on external translation agencies.

And there was something to fight for. The company was spending almost €0.7M per year on translations alone. We knew that with the right approach, AI could theoretically cut these costs dramatically — even by more than 90%.

At that point, however, one key question remained:

Could AI deliver not just cheaper translations, but high-quality, multi-language output that preserves brand tone, consistency, communication strategy, and the company’s terminology?

To answer this, we began with data.

That’s how the Proof of Concept was designed:

• 27 languages
• 9 context scenarios
• 11 LLMs

Results:

• 2.5 million words translated
• Costs reduced from €425,000 to €75 — a 99.99% decrease
• User satisfaction rated at 4.3 / 5

The outcomes not only validated our assumptions; they also generated strong internal momentum and high stakeholder engagement for the next stage of development.

Within a few months, we expanded the solution and moved into the MVP phase, introducing:

• structured translation forms
• character-limit management
• XML support
• PIM integration
• a translation memory exceeding 800,000 records

Within the first year, the system processed over 42,000 translations at a total estimated cost of €226.

This case shows what happens when AI is built around the process — not the other way around.

The Translation Problem

Every global company depends on accurate, timely translations. Product descriptions, marketing campaigns, contracts, user manuals — all of them must speak the brand’s language, across dozens of markets and formats.

But translation has long been one of the most fragmented and expensive processes in global organizations. Traditional workflows rely on agencies and native editors, while generic machine translation tools often fail to capture tone, nuance, or intent.

The result? Repeated manual reviews, inconsistent voice across regions, and high operational costs that scale with every new market.

AI Translate was created to change that. It replaces fragmented translation operations with a single, intelligent system that adapts to the brand — not the other way around.

The Shiftum AI Translate Solution

AI Translate is a custom-built translation engine designed for enterprise environments. It combines the accuracy of top large language models (LLMs) — such as ChatGPT or Claude — with deep contextual understanding of your brand, tone, and communication rules.

The system doesn’t just translate text. It interprets content through the lens of your company’s communication standards. Whether it’s a product page, a social media post, or legal documentation, each translation is automatically shaped to reflect regional nuances, channel-specific style, and tone of voice.

Unlike traditional tools, AI Translate fits directly into your existing workflows. It connects with systems you already use — e.g. PIM, CRM, CMS — and keeps translations consistent through an integrated translation memory. The result is a living, adaptive system that grows with your organization and continuously improves over time.

Business Results and ROI

AI Translate delivers measurable results from day one.

Companies that adopt the system typically see a 99% reduction in translation costs, while the average delivery time decreases from days to minutes.

Teams maintain consistent messaging across all markets and channels, and translation memory ensures that quality improves with every new project.

For enterprises, this means shifting translation from a recurring expense to a strategic capability — one that scales globally and operates continuously.

Core Features

AI Translate is built to replicate the precision of native translation — with the speed and scalability of automation.

Each feature addresses a specific pain point of enterprise translation management.

How It Works: From Text to Translation

AI Translate was built to be flexible. It supports both manual and automated translation flows — allowing your teams to work directly in the interface or integrate it seamlessly into enterprise systems.

Manual translation

In the user interface, employees can paste text or upload files directly. They select a region, language, product group, and context — for example, marketing, CRM, or legal.

The system then sends the content along with structured instructions to a selected LLM. These instructions define not just the target language, but also the brand tone, sentence style, and terminology preferences.

Within seconds, a complete translation appears — ready for use. It can be fine-tuned, shortened, or reformulated using the built-in AI assistant. When approved, the output can be copied, downloaded, or exported directly to integrated platforms such as PIM or CMS.

Automated translation

In automated mode, connected systems like PIM or CRM send translation requests to AI Translate via, e.g. API.

The engine automatically checks translation memory for existing pairs. If the segment has already been translated, it’s reused. If not, the system generates a new translation using the best-matching LLM.

A moderator then receives a notification for review and approval before the translated file is returned to the source system.

This dual approach allows organizations to combine flexibility and automation: ad-hoc creativity, and high-volume efficiency.

Use Cases

AI Translate adapts to multiple contexts across an organization:

Paid Search

Create and localize ad copies while respecting character limits and advertising tone.

1. Copy the ad text from the table along with the character limit into VersAI.

2. In just a few seconds, you’ll get a structured translation that respects the character limit. You can then copy and paste it back into the table.

Website Content

Automatically adapt product pages and marketing copy to local markets.

1. Duplicate the structured content intended for display on the landing page.

2. Paste it into AI Translate. Select the region, target language, and context for the translation. You will receive the finished translation in a few seconds, ready to be copied into a file.

Social Media

Generate informal, region-specific language for digital campaigns in social media.

1. Paste the master text into AI Translate.
2. Select the region, language, and “social media” context.
3. In just a few seconds, you’ll get a ready translation that matches your brand’s tone of voice and the style appropriate for social media communication.

Legal

Translate contracts, disclaimers, and compliance documents with precision.

1. Paste the content of the document that requires a formal style (e.g., contract, leaflet, official letter, etc.).
2. Select the region, language, and choose the “legal documents” context.
3. In just a few seconds, you’ll get a translation ready to copy and use.

Product Data

Automate large-scale product translation workflows directly integrated with internal systems.

1. AI Translate receives the translation request, automatically analyzes it, and generates the translation.
2. The moderator gets a notification that the translation is ready, reviews it, and approves it.
3. The approved translation is then sent back to the integrated system.

Each of these use cases leverages different translation guidelines and context rules, ensuring that the output fits both the purpose and the platform.

Integration and Scalability

AI Translate connects easily with enterprise ecosystems through robust APIs and modular design. It integrates with systems such as PIM, CMS, CRM, and DAM, enabling automatic data flow without manual intervention.

Role-based moderation ensures that translation reviews are controlled and traceable, while dedicated hosting and data segregation maintain compliance with IT policies.

The system scales horizontally — from small pilots to global rollouts — adapting to organizational structure, regional teams, and growing data volumes.

Security and Compliance

Security is built into every layer of AI Translate. No text is ever stored, reused, or exposed to external systems beyond the defined scope. Translated content is never used to train AI models.

The solution runs in a secure environment — in your cloud (AWS, Azure) or on-premises — and adheres to the organization’s internal security and privacy policies. Each translation process can be fully monitored and audited, giving IT and compliance teams complete visibility.

Join the Early Adopters Program

It’s not just a tool for translation. It’s a foundation for a more connected, efficient, and globally coherent organization.

A virtual support agent that helps teams respond to customer requests faster, more consistently, and at lower cost.

Why Customer Service Needs AI Support

Customer support teams process thousands of messages every month — questions like “Where is my order?”, “Can I return this product?”, “When will I get my refund?”

Each of these requests requires employees to search for information across multiple systems: CRM, ERP, OMS, payment platforms, or logistics dashboards.

As the volume of inquiries grows, the ability to expand teams proportionally disappears. Response times increase, and customers lose patience — expecting near-instant replies and consistent communication.

Customers expect an answer within one hour. For global e-commerce companies, this standard is becoming the new baseline.

AI Support Assistant was built to meet that expectation — without overloading human teams or compromising quality.

The Challenge: Manual Work and Delays

In traditional workflows, every customer inquiry requires manual investigation. Agents check order statuses, refunds, or payment confirmations in multiple tools. Each step adds seconds — multiplied by thousands of tickets every month.

Response delays are the biggest problem. When customers ask about a missing shipment or pending return, they expect an immediate update.

Yet support teams often need to cross-reference fragmented data from separate systems before they can reply accurately. This slows down service, increases costs, and makes scaling impossible during peak seasons.

AI Support Assistant addresses this by automating the process of gathering and synthesizing data, so teams can focus on the message — not on searching for information.

Introducing Shiftum AI Support Assistant

AI Support Assistant is an intelligent virtual agent designed to help customer service teams manage repetitive inquiries with speed and precision.

It retrieves the necessary data from internal systems, generates a draft response in the company’s tone of voice, and lets the agent review and send it within seconds.

The system integrates with internal platforms — CRM, OMS, ERP — as well as external providers such as courier services, payment processors, or helpdesk SaaS platforms.

All communication between the assistant and enterprise systems happens via API or dedicated MCP servers, ensuring fast and secure data exchange.

The solution is deployed on-premises, within the client’s own infrastructure. No customer data is ever transmitted to external model providers or used for model training.

AI Support Assistant was designed to work inside existing support processes — not to replace employees, but to remove repetitive, low-value tasks from their workflow.

How It Works

1. A new customer inquiry enters the system — from any communication channel (email, chat, form, or external helpdesk). The company maintains one unified point for collecting all incoming requests.
2. AI Support Assistant identifies the inquiry type — for example, order status, refund, or product return.
3. The assistant extracts relevant details (such as customer name, email address, or order number) from the message.
4. It searches connected systems for the necessary information — including order history, delivery status, or payment confirmation.
5. Based on the collected data, the assistant drafts a response aligned with brand templates and customer’s language. If information is missing, it automatically generates a polite request for clarification.
6. The employee reviews, edits if needed, and sends the message.

This workflow reduces the average handling time from minutes to seconds, without sacrificing accuracy or tone.

Core Features

Business Impact

Response time is one of the strongest predictors of customer satisfaction.

By accelerating information retrieval and response drafting, AI Support Assistant directly improves:

• Customer satisfaction (NPS) — faster, clearer communication increases trust.
• Post-purchase ratings — improved support translates into higher service scores.
• Customer retention — satisfied clients are more likely to reorder.
• Cart completion rate — fewer abandoned carts when pre-sales questions get answered quickly.
• Operational efficiency — teams handle higher volumes with the same resources.
• Cost reduction — fewer manual tasks and shorter resolution times.

Together, these effects redefine how support teams deliver value to the business.

Security & Infrastructure

AI Support Assistant was built with enterprise security at its foundation. It can be deployed either in the client’s private cloud or entirely on-premises.

No customer data leaves the organization, and no requests are sent to public model providers.

The system fully complies with corporate IT and data protection policies, ensuring secure integration into existing customer support ecosystems.

Join the Early Adopters Program

AI Assistance turns every support interaction into a faster, more consistent, and more human experience – by giving teams the tools to focus on what truly matters: the customer.

Every company talks about artificial intelligence. Few know where to begin.

For most organizations, AI still feels like a distant concept — promising, but difficult to implement. Teams see the potential but face uncertainty:

• What if experiments expose sensitive data?
• What if the tools don’t comply with security policies?
• What if costs spiral out of control?

The Challenge: AI Feels Distant

In many organizations, AI adoption starts — and ends — with curiosity.

Employees test public tools, share screenshots, and quickly hit a wall of security restrictions. IT blocks access to open models. Legal raises data concerns. Managers fear loss of control.

This fragmented approach prevents teams from building knowledge and confidence in how AI could support daily operations. At the same time, business leaders want to explore potential use cases, but need a structured, compliant way to do so.

AI Playground solves this divide: it gives employees access to AI in a secure, internal environment — one that satisfies IT governance while allowing real experimentation.

Introducing Shiftum AI Playground

AI Playground is a dedicated, private platform for experimenting with AI models within your organization. It provides a safe and controlled environment where teams can learn, test, and prototype ideas — without the risks of using public systems.

It helps marketers explore creative prompts, IT teams test integrations, and analysts discover how AI can support data workflows — all in one shared, governed space.

The goal is not to build production solutions immediately, but to create the foundation: a sandbox where AI becomes tangible, useful, and understandable.

Core Features

AI Playground combines flexibility for teams with control for IT and management. Each component supports safe, meaningful experimentation with real AI capabilities.

How It Works

The system was designed to make AI exploration simple and secure.

1. Single Sign-On (SSO) authentication ensures that only authorized employees can access the company’s Playground.
2. Users can interact with multiple AI models — such as OpenAI, Anthropic, or Mistral — within one consistent interface.
3. The environment offers ready-to-use prompts and templates for typical business scenarios, helping teams start faster.
4. Usage is billed by tokens, allowing full transparency and cost control.
5. All activity runs within the company’s secure cloud — with no risk of data exposure to external providers.

It’s a structure that balances freedom to explore with enterprise-level control.

Why It Matters

Most AI transformations fail not because of technology — but because of adoption. Employees need a space to learn, test, and build confidence before automation begins.

AI Playground accelerates this process. It helps teams develop AI literacy and identify processes that can later be automated or improved. Quick experiments in Playground often evolve into proofs of concept, and then into real implementations.

In practice, it becomes an AI incubation environment — where ideas, use cases, and workflows are safely tested before becoming enterprise systems.

Security & Governance

Data security and governance are at the core of AI Playground’s design. Each instance is isolated — accessible only to users within your organization.

Authentication is managed through SSO, and administrators can assign roles and permissions for each team. No user data or prompt history leaves your environment. The system does not store or reuse any content to train external models.

It fully complies with IT security and data protection standards, giving both business and technical leaders confidence in safe AI adoption.

Business Impact

AI Playground delivers measurable value even before full-scale AI deployment begins.

Employees learn faster how to use AI tools effectively, which increases team productivity and innovation.

IT retains full control over access and compliance.

In short, AI Playground lowers the barriers to AI adoption — safely, cost-effectively, and at your organization’s own pace.

Join the AI Playground

Every AI journey starts with a single experiment. Make yours safe, smart, and scalable — with AI Playground.