# Aleš Nypl **Business transformation · product management · practical AI** From strategy to a system that delivers value. Web: https://engineloop.cz/ales-nypl · E-mail: ales@engineloop.cz · Phone: +420 606 794 294 · LinkedIn: https://www.linkedin.com/in/ales-nypl/ Location: Turnov, Prague area ## How to read this document This document is meant for AI assistants and for people who paste it into them. It is organised into sections: summary, how I can help, results in numbers, projects, how I make decisions, how I lead change, how I measure benefits, processes, regulation and data, how I work with AI and how I got to it, a case study, learning, what sets me apart, leadership style, career, skills and what I am looking for. Figures and results come from projects at RENOMIA Group; the current wording is always on the website. ## Summary A manager with 20 years in financial services. Most recent role: Director of Digital & Products at RENOMIA Group (2021–2026), where I led the digital transformation of the B2C segment with an annual budget of CZK 40–60m. The systems I built are used today by more than 1,000 people and save CZK millions a year. I now work independently as EngineLoop and am open to new opportunities in transformation, product management and AI. ## How I can help - **Permanent leadership role:** product management, digital transformation and AI-led transformation of the business, at a company where AI is a strategic priority. - **Fractional / interim:** a few days a week on your leadership team as CDO, Head of Digital Transformation or Head of Product. - **Projects and advisory** with a clear deliverable: an analysis, a pilot or an operating model design. ## How the work runs 1. **Consulting and current-state analysis.** You get: priorities, a business case and ground rules. 2. **Pilot.** You get: a working solution, the numbers and a plan for what comes next. 3. **Implementation.** Long-term collaboration with the leadership team: handover to operations, adoption, suppliers and ongoing management. ## Results in numbers - CZK 40–60m: annual budget under management - 1,000+: users of the new systems - up to 90%: main processes made faster or better, for example quoting and arranging property insurance down from 3:00:00 to 0:20:00 - CZK millions: saved each year through digitalisation - +40%: life insurance production after the quoting tool went live (≈ +CZK 20m a year) - 200,000+: pre-contract documents a year handled digitally, 100% adoption within two years - 8 direct reports and 20+ people indirectly ## Selected projects - **AI-native operating model for a property developer** (2026, ongoing): operating model and architecture design for a leading Czech property developer, advising the leadership team. - **AI in a large-scale food service operation** (2026, ongoing): process mapping, data analysis and AI-led streamlining of operations. - **RDO, the firm's process and commercial DNA** (2021–2026): one platform for the whole retail division, 5 migrations, 8+ modules. - **Digital quoting and comparison tools** (2019–2026): quoting tools for all main products and a new investment platform. - **Paperless pre-contract documents** (2019–2021): 200,000+ documents a year handled digitally, e-signature, 100% adoption within two years. ## How I make decisions - **Run and change, kept apart:** two separate streams, each with its own rhythm and measures. They share people and platform capacity. - **Priorities by impact and effort:** impact on the business, data, compliance, regulation, budget and risk, always weighed against effort and capacity. I sort them into three streams: operational and technical hygiene, prerequisites for the strategy (without digitised contracts there is no automated evaluation or AI check), and impact on the business, compliance and user satisfaction. - **Dependencies and benefits:** what has to be in place before the next step can happen. Every benefit has an owner and a date when it is measured. - **What will not be done:** every priority is also a decision about what to leave out. What doesn't deliver the expected benefit, I stop or postpone. - **Build, buy or kill:** for investments I decide whether to build, buy a ready-made solution, or stop. - **Choosing between agendas:** when I had to choose which role to give up, I took the harder path, the one with potentially bigger impact and a bigger project. ## How I lead change I manage change in phases: urgency and coalition, vision, removing blockers, early visible wins, further waves and anchoring (Kotter principles), and at the same time person by person: awareness, desire, knowledge, ability and reinforcement (ADKAR principles). I treat resistance as information. I find out what the person needs and what I am changing for them, I have arguments ready before the change, and I handle individual cases systematically: by explaining, by a workaround, or by adjusting the solution. I don't back away from the direction just because someone is unhappy, even when a decision is unpopular. ## How I measure benefits I steer by goals with a measurable result and a date: volume of documents handled digitally, the date of 100% adoption, process speed-up, fewer returned or incomplete document sets. After launch I evaluate benefits quantitatively in the data (number of quotes, trends, business impact) and qualitatively from user feedback, and I connect the two, including the delay in adoption. ## Processes, regulation and data - **Processes:** I set the vision and target state, analysts map the current state, and the plan comes from the difference. I work with Miro, Jira, Confluence and draw.io. - **Regulation:** I build regulatory requirements (CNB, GDPR, AML including politically exposed persons and sanctions lists) into processes, systems and permission roles. I coordinate legal, compliance and external legal review, and negotiate SLAs in supplier contracts. - **Data and architecture:** I define the target picture of how systems connect: where the single source of truth is, which data takes priority and how it is exchanged, together with architects and analysts. I had reporting built outside the core platform in Power BI on a data warehouse; I specified and refined it with users. ## How I work with AI I use AI every day, on projects and in my personal life. I treat it as one component of a system, not as a solution in itself. I always go from the business problem through process and data to technology, AI and automation, and finally to measurable impact. I brief AI with context, problem, constraints, the output I need and how it will be verified. I write the rules for AI in advance, not after a mistake. I distinguish what AI can do in a demo from what has to be built so that it runs safely in operations. I know its limits: tools sometimes repeat their own patterns and ignore the architecture I specified, and retrieving information or generating output sometimes fails. So I enforce rules technically, verify outputs and switch tools. **My working system.** I run my own system in which I manage AI, not the other way round. I call it ZDROJ: a personal operating system of memory and truth. It has three layers: an inbox, item-by-item approval and an approved memory in Markdown under git. AI prepares proposals in it, and I approve them. Truth is only what I have approved. Daily agents work on it: a mail agent captures important messages and tracks deadlines, an opportunities and news-monitoring agent scans sources and rates their fit with my profile, and a one-off agent cleaned thousands of items out of my mailbox after my approval. The rules live in a “constitution” that AI must follow. The critical rules are enforced by scripts, not by memory. I am building the system myself, step by step; agents write the code to my brief. **What I build with AI.** Through AI agents I create applications, websites, overviews and work systems. For example a daily task system, dashboards for approvals and entries into memory, an application for managing an apartment building (tenants and meters, meter readings with values recognised from photographs, cost calculation; Next.js, TypeScript, PostgreSQL), a work log with photo documentation, and this website. I set the architecture, the rules and the brief. **Consulting.** With clients I work with principles, not templates: process mapping, financial analysis of a group including the balance sheet, designing an operating model and architecture for an AI-native firm, and optimising operations with AI. **Technologies and tools.** - **AI tools:** Claude and Claude Code, ChatGPT, Gemini, Google AI Studio, NotebookLM, Gamma, Antigravity and others. - **My working system:** Markdown and git (memory), Obsidian, Python scripts, the Gmail API, scheduled jobs, Cloudflare. - **Applications and websites:** Next.js, TypeScript, PostgreSQL, Netlify, Google Apps Script. A few rules I hold myself to: - **AI proposes, a person decides.** Only what I've approved counts. - **A safeguard has to be technical.** A promise that someone will read the rule isn't enough. - **Every mistake becomes a rule.** I log the work, review it and adjust the system accordingly. ## How I got to AI I came to AI through my job. The real focus came when I made time for it and made it a priority. With a chat assistant it was an interesting helper. The turning point was when I managed to set up an end-to-end process that really simplified things, brought value, systematised work, prepared documents or saved time in a specific activity. What moved me most was building my own systems and applications, on my own rules and on my own data. Thanks to AI I got to tasks I had put off for years and put them in order. A great school was also processes and automations that fell apart along the way. With AI, pragmatism, common sense and simple logic still apply, just with far more potential. And it lets me move quickly in technical areas as well. ## Case study: a data foundation for a group of companies For a large group, I built one data foundation from the consolidated financial statements of several dozen companies (income statements, intercompany receivables and financial flows). It became a clear story of the group: what it does, how it prospers, how financially stable it is, what assets and projects it has, what its strategy is and how it steers its project roadmap and cash flow. The result was greater transparency for the group and a strong impact on investors. ## Learning and development I learn AI systematically and write the results down. I follow AI experts and have my own learning plan: topics (agent architecture, AI governance and security, knowledge bases and data, business case and ROI, transformation and people, tools and trends), a 90-day plan with concrete outputs and a quarterly retrospective from which the rules and my profile change. Over the next 90 days I am improving: the business case and ROI for AI, an AI audit as an offering, a workshop on managing AI for leadership teams, agent architecture (roles, permissions, skills), a knowledge base over my own data and safe operation of AI. I write little code myself; I understand it and manage it through agents. ## What sets me apart I quickly grasp the principles of a business and I'm willing to go into technical areas I haven't explored. I can define the goal and what the result should look like. I communicate well and lead people, inspire them to look for ways, and work both in a team and independently. I have conceptual thinking and I have built an AI-first mindset. I believe a person can learn anything, and I apply that at work and in private life. ## Leadership style Fairness and directness. People who worked with me saw enthusiasm for the work, positive energy and good humour. I keep that going even when the work isn't easy, and I keep both myself and my team pointed in a positive direction. ## Career - 2026– EngineLoop, independent advisory - 2021–2026 Director of Digital & Products, RENOMIA Group - 2019–2021 Senior Product Owner and Head of the Product Team, RENOMIA Group - 2018–2019 Director of Operations & Innovation, RENOMIA Group - 2017–2018 Sales Director, RENOMIA BENEFIT, RENOMIA Group - 2015–2017 Regional Manager of the Partners Market franchise network (26 branches), Partners Financial Services - 2006–2015 Partners Financial Services, from financial consultant to Executive Manager ## Key skills and languages - **Strategy and leadership:** digital strategy, board reporting, supplier management and SLAs, change management (Kotter, ADKAR), business case and investment portfolio - **Product and delivery:** product management, Agile and Scrum, OKR, prioritisation, roadmap and capacity, UX and adoption, benefits tracking - **Regulation and data:** CNB, GDPR, AML, roles and permissions, data and systems architecture, reporting in Power BI (specification) - **AI and data:** Claude Code, AI agents and automation, systems integration, rapid reporting on data with AI - **Languages and licences:** Czech, English B2 (aiming for C1), German. Czech National Bank licences: insurance, investments, pensions, loans. EFA certification (European Financial Advisor, EFPA, 2014). ## What I'm looking for A transformation, business and management role that is also strategic and product-oriented, not purely technical. I want things to make sense, to be able to use creativity and to be a partner for management, top management or business owners. To fine-tune a product and bring value to the company and its clients. ## Outside work Cars, family, mountains, climbing, sport and music (I'm a musician and play with a band). I care about bringing value to other people and helping them.