AI Strategist role: What creating an AI strategy in the organization involves 

What does an AI Strategist do, and why is this role becoming increasingly important today? What does creating an AI strategy involve, how does collaboration with business and technical teams work, and how do you assess an organization’s readiness to implement artificial intelligence? Find out about this in the interview. 

Our expert, Łukasz Biegański, shares his insights. He combines technological, business, and project experience gained through numerous digital transformation initiatives. If you think this career path might be right for you, check out our current AI-related job opportunities at Sii.

AI Strategist – who they are and why this role was created 

What does an AI Strategist do, and why is this role becoming increasingly important? 

An AI Strategist helps organizations consciously navigate the next stage of digital transformation – one driven by artificial intelligence. In practice, they answer the question of how a company should operate and make decisions in a world where AI is becoming ubiquitous. They also help develop an AI implementation strategy and define how artificial intelligence should be used within an organization in a way that aligns with its business objectives. 

Artificial intelligence is the next stage of digital transformation. It has much in common with previous technological shifts, but at the same time it has its own unique characteristics, which is why it requires a dedicated approach. 

This role has emerged now because managers are operating under enormous pressure resulting from the pace of change and the feeling that this is a pivotal moment. That pressure increases the need for thoughtful rather than ad hoc action. As an AI Strategist, this is the support I provide, helping organizations carry out AI transformation in a deliberate way that delivers business value. 

How does an AI Strategist differ from an AI Engineer, Data Scientist, or AI Product Manager? 

An AI Strategist takes a much broader view of AI transformation. The areas of responsibility covered by an AI Engineer, Data Scientist, or AI Product Manager are all part of the bigger picture that an AI Strategist oversees. 

In our approach, this encompasses seven domains: strategy, value, governance, people and culture, organization, engineering, and data. The role of an AI Strategist is to help the organization address all these areas holistically – from the strategic level, through the organizational level, to the technological level. 

This is what sets the role apart: it spans the entire organization, from executive leadership to operational teams, connecting business, organizational, and technological perspectives throughout the AI implementation journey.

What business challenges does an AI Strategist help solve? 

An AI Strategist helps determine where artificial intelligence can genuinely support the organization, where risks may arise, and how AI should be implemented so that it generates value rather than losses. 

Many organizations invest heavily in AI yet fail to achieve the expected return on investment. Significant budgets are spent, but tangible results fail to materialize. The role of an AI Strategist is to direct these efforts so that investments translate into measurable business value while supporting the organization’s long-term growth strategy. 

Does every company need an AI strategy, or only the largest organizations? 

Every organization needs an AI strategy, although its scale and scope will vary significantly. The point is not that every company should build a large, complex AI program. Rather, every organization should make a conscious decision about how it intends to operate in a world where artificial intelligence is already present. 

The point of reference is not the size of the company but how that particular organization generates revenue and delivers value. That determines where AI makes sense and where to begin. An AI strategy is therefore not reserved for the largest enterprises – the only difference lies in its scale and the first steps to be taken. 

Regardless of the size of the organization, an AI strategy should be driven by business objectives and address the company’s real needs. 

How can you tell whether an organization is ready to implement AI? 

Interestingly, organizations are often unaware that the process has already begun. This is described by the concept of shadow AI – the unauthorized use of AI tools by employees without the organization’s knowledge or approval. 

People start using these tools on their own because they see them work faster and more efficiently. As a result, transformation begins from the bottom up, without the involvement of executives or managers. 

That is why every organization is already at some stage of AI readiness. The question is no longer “Should we start?” but rather “What should our first steps be?”  

Defining those first steps is also part of my role – helping organizations assess their readiness to implement artificial intelligence and plan the next stages of their AI transformation. 

Why does AI implementation begin with strategy rather than selecting a model or a tool? 

Because strategy takes precedence over models and tools. The goal is not to implement technology for its own sake. 

I know many cases where a company spent money, purchased licenses, and implemented a system – only to see no meaningful results. This happens when decisions are driven by the popularity of a technology rather than by answering the fundamental question of what AI is supposed to accomplish and how it should support the way the company creates and delivers value. In fact, this issue is not limited to AI alone. 

The starting point must always be the organization’s overall strategy. The AI strategy is subordinate to it and should be derived directly from it. Only then does it make sense to discuss specific models, tools, and solutions that genuinely support AI implementation and help achieve business objectives. 

From idea to AI strategy 

What does the first workshop with a client who says, “We want to implement AI,” look like? 

We begin with an AI Maturity Assessment, which evaluates the organization’s maturity in terms of artificial intelligence adoption. We assess its maturity across the same seven domains: strategy, value, governance, people and culture, organization, engineering, and data. This establishes the starting point. 

Next, we determine where the organization wants to go and what it actually wants to achieve through AI. The third step is to define the path from the current state to the desired future state and prepare an AI implementation roadmap tailored to the organization’s needs. 

The first workshop is therefore not a single meeting, but the beginning of a series of conversations with different stakeholders across the organization. Their purpose is to answer three questions: Where are we today? Where do we want to go? And how will we get there? The next stage is executing that plan. 

How do you identify the best AI use cases? 

We use our own methodologies and workshops designed to identify the most valuable AI use cases. However, the goal is not to find places where AI can be forced into use. We do not use artificial intelligence simply for the sake of using it. 

We start by identifying the organization’s real business problems and only then look for the best way to solve them. For some of those challenges, AI turns out to be the right answer – and when it does, we propose a meaningful solution. 

The order is always the same: first the problem, then the technology. That is why successful AI implementation always begins with understanding business needs rather than selecting a tool.  

How do you assess whether AI will actually deliver business value? 

It depends on the specific use case. Where the objective is to increase productivity, we use the existing process as a baseline. We measure the current state: how much time the process takes, how many errors it generates, and what resources it consumes. Based on this, we estimate the expected value of a particular use case. 

The assessment becomes more challenging when AI enables entirely new capabilities – allowing an organization to do something it has never done before or even changing its business model. That type of value is difficult to quantify in advance. 

We always encourage experimentation. At the current stage of technological development, exploring AI’s potential is an ongoing element of every AI strategy because a single successful experiment can generate returns many times greater than the initial investment. 

Instead of making assumptions without evidence, we formulate a hypothesis about the expected benefit and validate it as early as possible. A proof of concept (PoC) or an MVP shows whether the expected outcome is achievable. We measure not only the outcome itself but also how accurately we formulate our hypotheses. This allows us to evaluate AI’s actual business value even before a full-scale implementation. 

How do you create an effective AI strategy that supports an organization’s business objectives? 

There are several ways to approach AI strategy. One of them is to start by analyzing the organization’s previous experience with AI: what successes it has already achieved, and which use cases it has identified on its own. 

The areas where a company naturally turns to AI reveal a great deal about how it operates and what matters most to it, making them an excellent starting point. We examine two dimensions. The first is the nature of the application: is AI intended to improve existing day-to-day work, or is it meant to create entirely new opportunities? 

The second is the target audience: is the solution designed to support employees within the organization, or is it intended for external users and customers? 

These two dimensions make it possible to define a strategy tailored to the specific organization and develop a coherent AI strategy that supports the achievement of its business objectives. 

What does the AI Discovery process look like, and why is it critical to successful AI implementation? 

We begin AI Discovery by analyzing current processes and the way work is performed and value is delivered. We start with the existing state and identify where improvements can be introduced. AI Discovery enables us to effectively identify new use cases and prioritize them. This allows us to move from ideas to execution by embedding solutions into existing processes and workflows – or, with the help of AI, redesigning those processes so they deliver greater value. This stage makes it possible to build a realistic AI implementation strategy while reducing the risk of investing in the wrong initiatives. 

How do you convince executive leadership to invest in AI? 

First and foremost, you need to speak the language of business benefits. We already have numerous examples showing that AI can accelerate work, improve quality, reduce errors, and enable capabilities that were previously impossible. The second argument is even more important: this transformation is happening regardless. Whether or not an organization consciously manages it, AI will continue to have an increasing impact on its operations. The real risk is therefore not that the transformation will fail – it is that the company will fall behind. The question is no longer “Should we embark on AI transformation?” but rather “How can we approach it deliberately?” and “How can we use artificial intelligence to build a sustainable competitive advantage?” 

When is it appropriate to tell a client that AI is not the best solution? 

Whenever our analysis shows that AI is not the most effective way to solve the problem. In many situations, advanced AI systems are unnecessary. Simpler automation solutions or additional capabilities within tools the organization already uses are often sufficient. 

That is why we always start by understanding the problem and only then selecting the appropriate technology. We do not recommend AI where it is not needed. We work with experts from a wide range of disciplines, which enables us to recommend alternative solutions whenever they better address the client’s needs. Our goal is not to sell AI – it is to find the solution that best addresses the organization’s business challenges. 

AI Technologies 

How do you choose the right AI model for a specific project? 

Nie chodzi o to, by sięgać po najnowszy model. Najbardziej zaawansowane rozwiązania bywają kosztowne, a w konkretnym zastosowaniu często zbędne. Systemy budujemy tak, by The goal is not to use the latest model simply because it is new. The most advanced solutions can be expensive and, in many cases, unnecessary for a given use case. We design our systems, so they remain resilient to the rapid pace of change in the AI model landscape. We start by looking at the client’s existing technology stack and their requirements regarding compliance, security, and regulatory obligations. 

We break the selection process down into three independent decisions: 

• Where should the model run – on the client’s own infrastructure or in the cloud?  
• How should it be deployed – as an API service or self-hosted?  
• Whose model should it be – a commercial provider or an open-source model?  

We also take into account the resources the client already has in place to avoid unnecessary costs, while leveraging experience gained from previous projects. 

Most importantly, however, we design systems in a way that allows the model to be replaced. This prevents the client from becoming dependent on a single solution and helps avoid vendor lock-in. 

The role of an AI Strategist is to recommend a technological approach that supports the organization’s business objectives not only today but throughout the future stages of its AI journey. 

GPT, Claude, Gemini, or open-source models – what determines the choice? 

The choice depends on the environment the client already operates in and on their specific needs: the solutions they already use, security requirements, cost considerations, and how the model is expected to be used. 

We also support the development of Sovereign AI – solutions that give organizations greater control over their models, data, and infrastructure. For clients who consider this particularly important, we recommend models running on their own infrastructure. 

Today’s leading models offer tremendous capabilities, but we do not base our recommendations solely on their overall market position. The deciding factors are cost, as well as the results of internal tests and benchmarks conducted for the client’s specific use cases. 

Increasingly, we also evaluate models specialized for tasks, because the largest AI model is not always the best choice for a given use case. 

What role do AI Agents play, and will they really change the way applications are built? 

This is one of the most exciting directions in AI, and it is already delivering increasingly strong results. The technology is maturing; its applications are becoming more efficient and predictable, and proven implementation patterns are emerging – while experimentation and continuous learning remain essential. Today, AI already supports various stages of the Software Development Life Cycle (SDLC). AI Agents can assist individual roles throughout the process, but their potential extends much further. 

We help clients adopt approaches such as the Software Dark Factory – the automation of the entire SDLC, where humans take on a supervisory role instead of performing every step manually. This represents a shift from supporting individual tasks to coordinating and executing entire processes. It demonstrates that AI Agents are becoming not only productivity tools but also an integral part of designing modern AI-powered enterprise solutions. 

Is prompt engineering still an important skill? 

The latest AI models understand users increasingly well and can even help formulate prompts themselves, making prompt engineering much easier than before. However, proven prompting techniques still deliver better results than having no structured approach at all. Experience in clearly describing the desired outcome and effectively guiding interactions with AI models remains valuable. In knowledge-based work, practical AI proficiency is becoming an essential skill. What is becoming even more important than prompt creation itself, however, is the ability to use AI models consciously and understand both their capabilities and their limitations in specific business contexts. 

AI Strategist in Practice 

What does a typical day in the life of an AI Strategist look like? 

A significant part of my daily work is dedicated to understanding the organization, its semantic layer, and the stage of AI transformation it has reached. 

Established frameworks help structure the work, but they never provide a complete picture of how a particular company truly operates. That is why I gather knowledge directly from the people who work within the organization. I strive to understand how processes function, how work is carried out, and where value is created. In practice, this means collaborating with stakeholders at multiple levels – from C-level executives and people responsible for strategy, through business representatives, to the IT teams responsible for implementing the solutions we design. The day-to-day work of an AI Strategist is about connecting these perspectives and guiding the organization through its agreed AI implementation roadmap and overall transformation journey.

Who do you work with most often during projects? 

Primarily with three groups. The first consists of people responsible for strategy, such as executive leadership. The second includes business representatives who either define business needs themselves or work with us to identify them. The third group comprises IT teams that ultimately implement solutions. An AI Strategist therefore operates at the intersection of strategy, business, and technology. 

What are the most common mistakes organizations make when implementing AI? 

The most common one is local optimization – implementing a tool that improves only a narrow part of the overall process. For example, AI might accelerate software development severalfold, but if the remaining stages of the process remain unchanged, new bottlenecks quickly emerge. As a result, the overall improvement across the entire process is relatively small compared to the investment made. The second mistake is believing that purchasing AI licenses alone constitutes AI transformation. Making a tool available is not enough. Organizations also need to monitor whether it is actually being used and whether it delivers the expected benefits. Without measuring adoption, even an excellent solution will fail to generate business value. The third mistake is the distrust of AI. This concern is understandable – we are dealing with a new technology whose long-term consequences are difficult to predict. However, the worst possible response is to ban its use entirely. The transformation will happen anyway – even if only through shadow AI. Instead, organizations should treat this phenomenon as a valuable source of insight into where employees already recognize AI’s real potential and how they see opportunities to apply artificial intelligence in their daily work. 

What does collaboration between an AI Strategist, executive leadership, business teams, and technical teams look like? 

The foundation is ongoing collaboration with all of these groups. What we bring as AI Strategists is experience gained from numerous transformation initiatives. We understand the challenges organizations face, and we know which approaches work in practice, and which do not. We provide something different for each group. For executives and managers who operate under tremendous pressure, we highlight the available paths forward as well as the dead ends to avoid. For business teams, we provide methods for identifying use cases that deliver real value rather than simply looking impressive in presentations. For technical teams, we share best practices that enable them to build solutions quickly and scale them effectively over time. This is a continuous process involving working sessions, status meetings, jointly defining evaluation criteria, and prioritizing use cases. The key is to fully understand the needs of every stakeholder and translate them into a single, coherent AI implementation roadmap. 

Which is more challenging – technology or changing the way people work? 

This is well illustrated by Martec’s Law: technology changes exponentially, while an organization’s ability to absorb that change evolves much more slowly. As a result, technology advances faster than organizations can adapt. That is why changing the way people, and the organization as a whole – operate is usually much more difficult than implementing the technology itself.

Companies that design their processes with AI in mind from the very beginning are able to leverage their potential much faster. They do not need to spend years changing habits, organizational structures, and ways of working. As a result, they deliver business value more efficiently and respond to change more quickly. We have seen a similar pattern during previous waves of digital transformation, such as the rise of software development and the Internet. Organizations that reacted too late often faced serious consequences. AI is another transformation of this magnitude – one that cannot be ignored. Organizations need to approach it proactively.

How do you measure the success of an AI implementation? 

We measure success primarily through business outcomes – whether AI actually delivers the expected value. The key metric is ROI: determining whether the objectives defined for a specific use case have been achieved. In this respect, measuring AI initiatives is like measuring traditional IT projects – we want to know whether the investment is generating a return. Before ROI can be achieved, however, adoption must come first. That is why we first verify whether the licenses, tools, and implemented solutions are actually being used. Without adoption, even the best solution will fail to deliver business value. Monitoring AI adoption allows us to evaluate both the scale of usage and the way these solutions are being utilized. The specific KPIs depend on the objective of a given use case: reducing process execution time, lowering the number of errors, increasing sales, or delivering any other business outcome the solution was intended to achieve. 

Are there projects where you know from the outset that they are unlikely to succeed? 

It is rarely the project itself that is doomed to fail. More often, the problem lies in unrealistic expectations. AI is sometimes viewed as a silver bullet – a magical solution capable of fixing every problem on its own. If an organization lacks high-quality data and its processes are poorly organized, artificial intelligence will not solve those issues. That does not mean the organization cannot be helped – it absolutely can. However, the first step must be to establish solid foundations: reliable data, well-structured processes, and effective ways of working. Only then can AI deliver real value. Interestingly, AI itself also helps in this process. It enables organizations to quickly build prototypes or proofs of concept that demonstrate, at an early stage, whether a solution is viable. That is why managing expectations is so important. Organizations need to understand that successful transformation requires disciplined work – not magic. 

Skills and career development 

What skills are most important for an AI Strategist today? 

My experience from previous digital transformation initiatives has been extremely valuable. Years of working in this field allowed me to develop competencies that are equally relevant for AI-driven transformation. The most important quality, however, is openness. You cannot become attached to previous ways of working because every stage of digital transformation has its own characteristics – and AI is no exception. You need to continuously follow technological developments, explore new solutions, and remain committed to lifelong learning. Equally important is the ability to see the bigger picture and understand the organization as a whole. Ultimately, technology must deliver business value. It is not enough to be fascinated by AI’s capabilities – you need to understand what business objective it is meant to achieve and what value it should create for both the organization and the business. 

Should an AI Strategist know how to code? 

Programming is more accessible today than ever before, thanks in part to AI models that help people write code. Even those with no previous programming experience can now get started much more easily. My own software development background helps me in ways that are not always obvious. I worked as a software developer, which gave me a deep understanding of the software development lifecycle and enabled me to communicate effectively with technical teams. However, writing code itself is not the most important part. What matters more is understanding what software development involves, how software is built, and the challenges technical teams face when implementing AI solutions. 

How much technical knowledge does an AI Strategist need? 

Technical knowledge is certainly valuable because it allows you to assess more quickly which direction a project can take. Part of the AI Maturity Assessment focuses on technology-related areas, so understanding them makes it easier to accurately evaluate an organization’s current state. That does not mean an AI Strategist needs to be an expert in every aspect of technology. I often work alongside an AI Architect or Solution Architect who specializes in the technical side of the solution. The AI landscape evolves so rapidly that it is difficult for one person to stay up to date with everything. That is why we rely on experts specializing in different AI domains and involve them according to the specific needs of each project. 

Is it easier to become an AI Strategist from a business or an IT background? 

The ideal combination is having experience in both areas. I have a technical background, while at the same time I’ve been working closely with business stakeholders for many years. This combination allows me to communicate effectively with a wide range of stakeholders. If I had to choose one area as the most important, I would say that understanding the business is key. An AI Strategist needs to understand how an organization operates, how it creates value, and how it achieves its business objectives. At the same time, it’s essential to invest time in understanding technology and the fundamentals of IT. Without that knowledge, it’s difficult to properly assess the opportunities, limitations, and implications of AI-based solutions. 

What has your career path looked like? 

I started my career as a software developer, so I have a solid technical foundation. Later, I led a team and delivered projects for clients, including solutions supporting their day-to-day operations and helping them meet regulatory requirements. That project experience has proven extremely valuable in my current role. My first encounter with artificial intelligence dates to my university years. While writing my master’s thesis, I worked with artificial neural networks. The capabilities of the technology at that time were incomparable to what we have today, but that was my first exposure to the field, and I was immediately fascinated by it. I’ve been professionally involved in digital transformation for many years. I’ve witnessed and helped shape changes related to mobile technologies and cloud computing, and even earlier, as a user, I experienced the Internet revolution firsthand. For me, becoming an AI Strategist has been a natural continuation of that professional journey. 

Which certifications or training programs are worth pursuing today? 

I wouldn’t point out a single mandatory certification. What matters far more is continuous learning and selecting training that aligns with the area in which you want to grow. AI is evolving so rapidly that the need for continuous development is greater today than ever before. It’s hard to imagine someone completing their formal education and then never learning anything new again. At the same time, this is an excellent moment to learn. AI itself can act as a personal tutor, explaining concepts in a way that is tailored to each individual. It’s something worth taking advantage of. For an AI Strategist, competencies beyond technology are also highly valuable: agility – particularly at scale in large organizations – project management, and methodologies such as Lean, which help measure effectiveness. Knowledge of AI itself can be developed through training provided by OpenAI, Anthropic, and NVIDIA, as well as educational resources from DeepLearning.AI, where I serve as an ambassador. 

What skills will the market demand in the coming years? 

The most important skill will be the ability to learn quickly and adapt. It’s difficult to predict exactly what the market will look like in a few years, but the ability to respond to change will certainly remain essential. One particularly interesting, and perhaps less obvious – trend is the growing importance of management skills. As we increasingly collaborate with AI agents, we’ll need to know how to delegate tasks to them, evaluate their results, and provide effective feedback. This is a different type of collaboration than managing people, but many of the same principles will still apply. Today, we are at a stage where almost everyone, within their own field of expertise, can discover new and groundbreaking applications of AI. The prerequisite is active engagement: experimenting, testing, and working with technology on a daily basis. 

Future of AI 

How will the role of an AI Strategist evolve over the next five years? 

Over time, the ways organizations implement artificial intelligence will become increasingly formalized and standardized. We’ll have access to a much larger body of proven examples from organizations across industries, making it easier to apply validated methodologies and AI implementation best practices. Organizations’ level of AI awareness will also change. It is likely that much less time will need to be spent convincing companies that AI transformation is necessary. It will become self-evident, and a much larger portion of an AI Strategist’s work will focus on implementing concrete solutions and delivering measurable business outcomes. The role may also become even more closely connected with technology itself. In organizations with more mature AI adoption, part of the collaboration may take place directly with AI systems and AI agents.

Which trends will have the greatest impact on the development of artificial intelligence? 

One of the most important trends is Sovereign AI – the development of solutions that give organizations and governments greater control over models, data, and infrastructure. We can already see global changes related to model availability and the ability to use AI technologies in different parts of the world. The importance of this area is likely to continue growing. Infrastructure will also play a crucial role. During previous waves of digital transformation – such as the Internet, mobile technologies, and cloud computing – infrastructure did not evolve nearly as quickly. With AI, however, the pace of innovation may cause hardware and architectural requirements to become outdated much more frequently. A key question will therefore be whether organizations begin building their own AI computing environments and infrastructure or continue relying primarily on the global infrastructure provided by major vendors. The direction this market takes will have a significant impact on the availability, cost, and methods of implementing artificial intelligence across organizations. 

Will AI replace some specialists, or will it simply change the way they work? 

Some roles will probably disappear, but many more will evolve. We’ve seen similar patterns during previous waves of technological transformation. The rise of the Internet reduced the importance of some traditional forms of communication while simultaneously creating entirely new services and professions. Demand declined for certain tasks, such as delivering physical mail, while at the same time e-commerce and courier services experienced tremendous growth. The same will happen with AI. The responsibilities of many professionals will change; new roles – such as AI Strategist – will emerge, and some existing positions will no longer be needed. However, I’m optimistic. I believe that, in the long run, AI will also create many new opportunities for both businesses and the labor market. 

How important are AI Governance and Responsible AI today? 

Every new technology is initially met with skepticism. History has shown that while some concerns eventually prove to be exaggerated, technological progress can also create genuine risks. We have seen this with the Internet and social media, which have brought enormous benefits but also challenges such as online abuse, attention deficit, and the misuse of data. Similar concerns have accompanied many other technological breakthroughs. Artificial intelligence, however, has the potential to have an even greater impact than previous technological revolutions. Its potential is enormous and difficult to compare with anything we’ve seen before. 

That is why AI Governance and the responsible use of AI are areas that deserve particular attention from the very beginning – already during the design and implementation of AI solutions. 

Will the AI Act change the way AI-based solutions are designed? 

The AI Act is already influencing how AI-powered solutions are designed. However, we should not use the AI Act to create fear or present it solely as a barrier. I see these regulations as a set of principles that organizations can understand and apply in a responsible and pragmatic way – allowing them to develop AI solutions that comply with the law without sacrificing efficiency. The AI Act is new legislation, and it doesn’t have to be perfect. However, regulating such an important technology is necessary, even if the first version of the regulations still leaves room for improvement. Organizations should learn to incorporate these requirements from the very beginning of every project and treat them as an integral part of responsible AI implementation – not as an obstacle that needs to be addressed at the very end. 

If you had to name one future-proof skill, what would it be? 

It would be curiosity – the desire to understand what’s new in the world – combined with the ability to learn. Curiosity is what drives learning, and in a world that is changing as rapidly as today’s, it is what determines who will keep pace with change. At the same time, theory alone is not enough. You must act yourself – experiment, test, and explore new solutions in practice. Thanks to AI, both learning and doing has never been easier. The future concerns all of us – but curiosity and experimentation are what will help us prepare for it and even actively shape it. 

AI projects at Sii 

What types of AI projects do we deliver in Sii? 

At Sii, we deliver AI projects across every stage of AI maturity. The difference between them is not so much the technology itself, but how deeply we engage with the client’s organization – from delivering a single AI solution for a clearly defined business problem, through strengthening the client’s team with our AI specialists, to leading end-to-end AI transformation, from strategy and data all the way to changes in ways of working. A significant proportion of our projects are production-grade AI implementations rather than experiments. We have delivered dozens of such projects across highly regulated industries, including finance, healthcare, and the public sector. Interestingly, the most challenging projects we undertake are usually not the technically most complex ones. 

They are the projects that require genuine organizational change – in business processes and in the habits of the people who work within them. Those are exactly the kinds of challenges that interest me the most. 

Which industries are investing the most in AI solutions today? 

The industries best positioned to benefit from AI are those that combine large volumes of data with numerous repetitive and costly processes. These include financial services and banking, where data maturity is particularly high, as well as healthcare and pharmaceuticals, manufacturing and industrial sectors, the public sector – which is becoming increasingly active – and retail. This aligns with what we observe among our own clients. However, as an AI Strategist, I would add that the question, “Which industry?”, can be somewhat misleading. The determining factor is not the industry itself, but the maturity of the individual organization – whether it has well-organized data, a genuine business problem to solve, and the willingness to embrace change.

What does collaboration between an AI Strategist and experts from other domains look like? 

In my role, I frequently rely on experts from other disciplines. Depending on the project, I work with specialists in cybersecurity, compliance, or legal matters. During implementation projects, I most often collaborate with an AI Architect, who is responsible for the technological aspects of the solution. This collaboration is essential because successful AI implementation requires a combination of many different areas of expertise. It ensures that the solutions we deliver are not only valuable from a business perspective but also secure, compliant with regulations, and responsibly designed. 

What sets the role of an AI Strategist at Sii apart? 

First and foremost, the people. I work alongside truly outstanding specialists. Our AI Competence Center brings together exceptional experts, and having the opportunity to learn from them and work with them is something I value immensely. It gives me tremendous energy and, more importantly, creates a significant advantage for our clients. The second differentiator is the diversity of our projects. It allows me to continuously observe how organizations across different industries approach AI transformation, what challenges they face, and which solutions prove successful in practice. You simply cannot gain that perspective from working on a single project. On top of that, we have access to proven frameworks and methodologies validated through previous implementations, as well as numerous strategic partnerships – including Gartner, Microsoft, and Google, which provide us with additional knowledge and capabilities that support AI transformation. In short, I never have to start from scratch, and I never work alone, and in this role, that makes an enormous difference. 

What development opportunities are available for people interested in AI in Sii? 

Sii’s AI Competence Center is constantly growing, and the number of AI projects continues to increase across a wide variety of domains. As a result, career opportunities span both technical and consulting roles. You can build AI solutions, become part of client delivery teams, support organizations throughout their AI transformation journey, or help create entirely new concepts such as the Software Dark Factory. The greatest opportunities are available to people who are independent, ambitious, and capable of taking a project all the way from an initial idea to a production-ready implementation. The AI landscape continues to present new challenges to solve and new directions to explore. 

Why is now the right time to build a career in AI? 

It may sound like a cliché, but extraordinary things are happening in AI right now. It’s an exciting field for people who are curious about technology, enjoy learning, and want to be part of a period of rapid change. Many standards, best practices, and ways of working have yet to be fully established. There is still tremendous room for experimentation, discovering new applications of artificial intelligence, and influencing how this field will evolve. In many ways, it resembles the Age of Discovery – except that today, artificial intelligence is the new frontier waiting to be explored. It is also an opportunity to work with cutting-edge technologies and learn from world-class experts. For anyone who wants to be part of this transformation, there has rarely been a more exciting time to build a career in AI.  

If you also want to grow your career in AI, collaborate with experts, and take part in AI transformation initiatives delivered to clients across multiple industries, join Sii. Explore our current AI-related job opportunities.