# The Thinking Company

> The AI transformation firm combines productized Runstate™ AI Agents, Transformation Leads, and FDEs to turn AI ambition into EBITDA.

The Thinking Company is a productized AI transformation firm founded in 2026 in Warsaw, Poland. It works with enterprises, PE-backed companies, and growth mid-market organizations across Europe, the United States, and the Middle East.

TTC exists for organizations whose AI work is between ambition and production: board mandates without operating rhythm, experiments that have not reached production, engineering teams using AI informally, governance gaps that make the board uneasy, and product ideas that need a senior team to build.

## Founder

Founder & CEO: Bartek Pucek — Stanford GSB and INSEAD IDP alumnus; creator of the AI Manager and AI Enterprise programs, whose participants include 1,000+ leading Polish companies and their leaders; angel investor in 20+ AI companies including ElevenLabs. About: https://thinking.inc/about/

## Positioning

Production systems over experiments.

TTC does not offer open-ended advisory retainers, generic AI training, or bodyshop engineering capacity. It offers scoped transformation work that produces operating models, production systems, governance structures, and measurable outcomes.

## Delivery Model

Every engagement brings together:

- Runstate™ AI Agents: extend the team through TTC's agentic system.
- Transformation Leads: own the mandate, board interface, and strategic direction.
- Forward-Deployed Engineers: build production systems inside the business.

Runstate™ is the agentic system behind TTC. It understands the company in context: its stakeholders, processes, data, decisions, and value targets. Approved methods and delivery learning carry forward across teams and processes, so the impact compounds across the organization.

## Product Catalog

TTC offers six products. Each product can be shaped into one or more formats: Diagnostic, Sprint, Programme, Product Build, Operate Retainer, or Custom Engagement. The standard entry point is a Briefing.

### AI Transformation

AI Transformation is the umbrella product for board-led, multi-workstream operating-model rewires. It fits when the problem is not one use case, but how AI moves through the business.

Typical triggers:

- Board mandate for measurable AI outcomes.
- New CEO, CDO, or transformation leader in the first 90 days.
- PE post-acquisition value-creation plan.
- Stalled AI experiments that have not reached production.

Engagement shapes:

- AI Transformation Audit: scopes the operating-model rewire.
- AI Transformation Workstream Sprint: ships one production workstream.
- AI Transformation Programme: rewires several workstreams.
- AI Transformation Office: ongoing steering, KPI tracking, governance, and board reporting.

### AI Capability

AI Capability is a single-purpose readiness diagnostic for organizations that know they need to act on AI but do not know where the leverage is.

Output:

- Eight-dimension AI readiness score.
- Opportunity map of candidate use cases ranked by value and feasibility.
- Recommended entry product and format.
- Sequencing plan and board summary.

### AI Governance & Trust

AI Governance & Trust answers the board question: how do we govern AI?

It covers AI inventory, risk classification, board reporting, control design, approval workflows, monitoring hooks, and evidence mapped to applicable regimes such as the EU AI Act, ISO 42001, and sector-specific rules.

Engagement shapes:

- AI Governance Diagnostic: inventory, scorecard, gap analysis, risk register, board pack.
- AI Governance Setup Sprint: operational framework, register tooling, workflows, reporting cadence.
- AI Governance Office: ongoing oversight, monthly board pack, reviews of new AI systems, incident response.

### AI Engineering

AI Engineering transforms the client's software delivery lifecycle with AI.

It is for CTOs, VP Engineering leaders, and Heads of Platform whose teams use AI informally but have not embedded AI into the SDLC.

Practice areas:

- Claude Code and GitHub Copilot rollouts.
- Agentic CI/CD.
- AI for QA.
- AI code review.
- Dev-productivity measurement and onboarding updates.

Engagement shapes:

- AI Engineering Diagnostic: SDLC audit, integration map, expected productivity uplift.
- AI Engineering Sprint: one AI-in-SDLC integration shipped.
- AI Engineering Programme: multiple integrations across teams.
- AI Engineering Office: ongoing productivity review, tool evaluation, coaching, and scorecard.

### AI Product

AI Product designs, builds, and deploys custom AI-native products or systems the client owns.

The client owns the IP: source code, infrastructure, prompts, evals, agent orchestration, design assets, documentation, and operating playbook.

Typical buyers:

- PE venture studios.
- Corporate innovation teams.
- Product companies expanding their AI surface.
- Funded founders commissioning the first production version.

Engagement shapes:

- AI Product Feasibility: validates technical approach, build-versus-buy, problem fit, ROI, and build scope.
- AI Product Build: working AI-native product in production with source and IP transferred.
- AI Product Operate: optional run and iteration while the client team ramps.

### AI Diligence

AI Diligence is for private equity teams assessing a live target or portfolio asset.

Output:

- IC-ready commercial DD memo on AI capability.
- AI value-creation thesis tied to the return model.
- Risk register with deal-relevant items flagged.
- Portfolio-rollout recommendation.
- 100-day post-close AI plan.

## Named Frameworks & Methodologies

The Thinking Company's proprietary frameworks, each defined in a canonical guide:

- AI Maturity Model — five stages from Ad Hoc to Transformational. https://thinking.inc/en/pillar-pages/ai-maturity-model/
- Eight-Dimension AI Readiness Score — the diagnostic behind AI Capability. https://thinking.inc/en/pillar-pages/ai-readiness-assessment/
- AI ROI Model — cost-and-return methodology for the AI business case. https://thinking.inc/en/pillar-pages/ai-roi-calculator/
- AI Governance Framework — inventory, risk classification, controls, and board reporting mapped to the EU AI Act and ISO 42001. https://thinking.inc/en/pillar-pages/ai-governance-framework/
- Agentic AI Architecture — patterns for multi-agent production systems. https://thinking.inc/en/pillar-pages/agentic-ai-architecture/
- EU AI Act Compliance — risk classification, obligations, and deadlines. https://thinking.inc/en/pillar-pages/eu-ai-act-compliance/

## Voice

TTC voice is direct, evidence-based, and allergic to corporate speak. Preferred language:

- Helping your company get from AI ambition to a new operating model.
- Production systems over experiments.
- Working systems.
- In production.
- Knowledge transfer.
- Transformation Lead.
- Forward-Deployed Engineer.
- AI agents as first-class team members.

Avoid:

- Generic "AI strategy" if it implies a deck.
- Vendor-neutral claims. TTC is specific and stack-aware, with a Claude-first and OpenAI-aligned approach where appropriate.
- Legacy sprint or build labels as current public offer names.
- "Workshop" as a standalone SKU.

## Entry Point

The first conversation is a Briefing with a Transformation Lead. The goal is to confirm fit, identify the correct product, and determine whether the next step is a Diagnostic, Sprint, Programme, Product Build, Operate Retainer, or no engagement.

Contact: https://thinking.inc/contact/

Last updated: 2026-08-13