arrow_circle_right AI strategy & enterprise AI consulting
Tomasz Smolarczyk
Director of Artificial Intelligence at Spyrosoft, helping enterprise leaders build AI that delivers from strategy through to production.
About Tomasz Smolarczyk
“A well-made AI model can solve a problem an infinite number of times, never getting tired nor losing the quality of the results.”
I’m the Director of Artificial Intelligence, leading Spyrosoft’s AI practice – including strategy, delivery, and capability building – and working directly with clients to move their AI ambitions from concept to production.
My path into AI is rooted in a combination of technical and commercial disciplines. I studied Computer Science before completing postgraduate management studies with an MBA-level structure. Early in my career I joined a major consulting firm, where I delivered data analytics projects for clients across multiple sectors.
Before joining Spyrosoft, I built and led the Data Science function at a healthcare startup developing an AI-powered early diagnostics system. I have also worked with aerospace and rail manufacturers, developing predictive maintenance tools for traffic management systems, and completed a Big Data placement at a financial institution. Across these roles, a single thread connects everything: making AI work in the real world.
At Spyrosoft, I have led the development of AI deployments in financial services, insurance, and logistics – including document processing automation and AI agents that execute end-to-end workflows. I have spoken at international events and regularly contribute thought leadership on the business implications of AI transformation.
Spyrosoft technology ecosystem
Who I work with
I work with VP- and Director-level leaders in technology, operations, and digital functions at mid-market and enterprise organisations – typically in financial services, insurance, healthcare, logistics, or manufacturing.
Most are at a similar point: they’ve started their AI journey, invested in pilots, and aren’t seeing the returns they expected. They’re ready to change that.
What we can discuss
From AI pilots to enterprise value
- Where AI changes the economics of your business, and where it doesn’t
- Which workflows should be redesigned
- Evaluating build vs. buy vs. partner options
- Turning a portfolio of experiments into an executable roadmap
- Making the business case for AI investment at board level
Agentic AI for real operations
- How agents interact with enterprise systems without requiring new interfaces
- Engineering governance, observability, and human control into agent workflows
- Moving from a proof of concept to a production-grade agentic system
- What it takes to deploy agents responsibly in regulated industries
Building the capability to scale
- AI architecture and SDLC for teams building beyond the first project
- Designing the operating model that makes AI repeatable
- What AI roles and competencies you actually need in-house
- Governance & adoption: the things that determine whether the system gets used
- Building internal AI literacy without slowing delivery down
Explore the Spyrosoft AI Practice
What’s your challenge?
If any of the following sound familiar, let’s talk.
Struggling to show the board a credible path from AI pilots to measurable business value?
Not sure whether to build something tailored or buy off the shelf – and what the real trade-offs are?
Looking for a way to automate complex, document-heavy workflows without replacing every tool your team already uses?
Not sure how to structure an AI team, or what capabilities you actually need in-house vs. what to partner for?
Wondering whether AI agents are ready to take on real operational work, or still too unreliable for your risk tolerance?
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Let’s talk about what AI can do for your business
Tomasz Smolarczyk
Director of Artificial Intelligence
FAQ
An AI strategy consultant helps organisations move beyond isolated tools and pilots to define where and how artificial intelligence can create durable business value. The work typically involves assessing current capabilities and data infrastructure, identifying high-impact use cases, evaluating build-versus-buy decisions, and designing a roadmap that connects AI investment to measurable outcomes. A good AI strategy consultant bridges technical depth and commercial thinking. For enterprises, this often means working across functions to get alignment on priorities.
Chatbots answer questions. AI agents execute work. The distinction matters enormously for enterprise adoption. An AI agent can plan a sequence of tasks, retain context across a workflow, use tools and APIs, and produce structured outputs – without requiring a user to interact with it step by step. In well-defined workflows with clear inputs and outputs (such as insurance underwriting or document classification), AI agents are already delivering production-grade results. In high-stakes and heavily regulated contexts, careful design of human oversight and exception handling is essential.
The most common reason is that AI gets added on top of existing workflows rather than embedded into them. AI projects that succeed tend to be integrated into core processes, with clear ownership of outcomes, real change management, and a design that makes the AI path the path of least resistance for the people doing the work.
Beyond technical capability, the most important things to evaluate are production experience and business alignment. Ask for evidence of production deployments in your sector. Equally important is whether the vendor understands your business context. An AI partner who can connect data science decisions to operational and commercial outcomes is more valuable than one who optimises for model performance in isolation.
AI is creating the most measurable value in industries with high volumes of structured decision-making, document processing, or predictive tasks. Financial services and insurance are leading adopters – using AI for underwriting, fraud detection, credit risk, and customer operations. Healthcare organisations are applying AI to diagnostics support, patient triage, and clinical workflow optimisation. Manufacturing and logistics benefit significantly from predictive maintenance and demand forecasting.
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