Klose Coaching

AI in your organization

Implement AI in your organization effectively, not haphazardly.

You want AI to make your business faster, cheaper to run or better for customers, and you want to know where to start, what it will cost and how to keep it safe. We help business owners and leadership teams pick the right first problem, run a small test that proves value, and build the decision-making around AI so the gains reach the bottom line.

All you need to start is a business problem you want solved, and we bring the technical knowledge.

What stalls AI projects after the pilot

AI makes the doing faster, and that moves the pressure onto the deciding. Someone still has to choose what the AI is for, review what it produces, own its mistakes and decide what happens next. When those steps stay as they were, the extra output piles up in front of the same few people and the business sees little of the gain.

A weak decision system: AI amplifies the mess A weak decision system, drawn as a tangle, feeds an AI amplifier, and what comes out is a bigger tangle of arrows pointing in different directions. A weak decision system AI amplifies the mess AI
A strong decision system: AI amplifies the results A strong decision system, drawn as orderly lines, feeds the same AI amplifier, and what comes out is a set of strong arrows pointing the same way. A strong decision system AI amplifies the results AI
AI makes the doing faster; how you decide determines whether the gain reaches the business.

One place this has been measured is software delivery. The DORA research programme found in 2024 that as AI adoption rose across software teams, measures like code review speed improved while overall delivery throughput and stability dipped. Its 2025 report summed it up: “AI doesn’t fix a team; it amplifies what’s already there.”

Economists describe a similar lag across whole economies: returns from new technology arrive late because organizations first have to build the processes and ways of working that let them use it. In our experience, a central part of that work is the organization’s decision system: who decides what, with what information, and how fast mistakes get caught.

What we help you do with AI

  1. See where AI fits in your business.

    AI is much wider than chatbots. It includes large language models, predictive analytics, automation and AI agents, and a growing range of ready-made tools. We help you work out which of these fit your business, where AI does not belong, and whether to buy, configure or build.

  2. Speed up the whole flow of work.

    AI can make one part of the business much faster while making another part worse. We follow how work flows through your organization, find where it queues, and point AI at the places where it improves the flow instead of generating more work for someone else.

  3. Set clear rules for business data.

    Decide what information may go into which AI tools, check how business AI products differ from consumer ones, and set access, retention and permissions deliberately, so customer information, confidential documents and intellectual property are not exposed unnecessarily.

  4. Start with a small, bounded test.

    A six-month transformation programme is not required to get started. We help you turn a real business opportunity into a small, testable experiment with a defined problem, clear boundaries and a way to measure whether it helps.

  5. Decide who owns what the AI produces.

    Someone has to decide what the AI is for, who owns its output, who may overrule it, and how you find out when it is confidently wrong. We help you write those rules down before the rollout, while they are still cheap to set.

Questions we work through with leadership teams

  • Where can AI create meaningful value in your business?
  • How do you avoid creating new bottlenecks while solving old ones?
  • What is the smallest useful thing you can try?
  • How do you turn an experiment into something that works in the real business?
  • Who in your organization decides how AI is used, and who is accountable for what it produces?

To talk about AI in your organization, write to lukas@klose-coaching.com.

Questions about AI in your organization

Where should a business start with AI?

Start with a business problem. Pick one process where work queues, errors are costly or customers wait, check whether an AI tool fits it, and run a small experiment with clear boundaries and a measure of success. Before you scale it, decide who owns the AI’s output and how you will notice when it is wrong. Klose Coaching helps leadership teams choose that first problem and run the test.

Why do AI projects fail to deliver business value?

An AI tool can work as intended while the organization around it stays the same. AI speeds up the doing, so more work arrives at the points where people review, approve and decide, and those points can become the new bottleneck. Research by the DORA programme describes AI as an amplifier of what a team already does. Getting value means designing the decision-making around AI as well as deploying the tool.

How do I keep business data safe when using AI?

Decide in advance what kinds of information may go into which AI tools. Business-grade AI products usually offer different data handling from consumer apps, so check how each tool treats access, retention and training on your data. Set permissions deliberately, and keep customer information, confidential documents and intellectual property out of tools that do not meet those rules.

Does my organization need to be ready for AI first?

You can start small at any stage. How far AI takes you depends largely on the organization it works within: clear decision rights, good information flow and fast error correction. Organizations with a strong decision system are likely to get far more from AI than those without one, so it pays to work on both together. What a strong decision system looks like.

Led by Lukas Klose.