# AI Acceleration

_From AI curiosity to measurable gains_

> We help teams find the AI use cases worth funding, prove them quickly and roll them out safely across the business.

- Tags: AI, Consult, Service
- Source: https://www.insidethebox.dev/services/ai-acceleration

## Overview

Most companies have tried AI; fewer can point to what it changed. In McKinsey's 2025 State of AI survey, 88% of respondents said their organisation used AI regularly in at least one business function, but only 39% reported any effect on EBIT at the enterprise level. We close that gap: find the work where AI pays, prove it on real tasks, measure it honestly, and roll it out with the rules and training that make it stick.

Our guide [From AI pilots to real gains](/insights/from-ai-pilots-to-real-gains) covers the approach in detail.

## Find the use cases

We start where the time goes. Workshops with each team collect the tasks that repeat, the documents people re-key and the questions they answer again and again. Each idea is scored for value, the hours saved multiplied by how often it happens, and for feasibility: whether the data is ready, how costly a mistake would be and how much effort it takes. The top-right corner becomes the first pilots.

## Redesign the work

AI pays when the workflow changes around it, not when a chatbot is added to the old one. In the claims example, AI reads the email and attachments and fills in the claim, a handler checks it, and the acknowledgement is drafted: 25 minutes becomes 6, with a person still deciding.

## Prove it in a pilot

A pilot is a working prototype in real hands, on real work, measured against a baseline taken before it starts. Each week we track how many people use it, how much time it saves and how often its output is accepted without changes.

People's sense of speed can mislead. In METR's 2025 study, experienced developers expected AI tools to make them 24% faster and turned out 19% slower, while still believing they had sped up. So we measure the work itself.

## Measure quality

Before anything scales, it runs a test set of real cases with known answers. Each model and prompt is scored for accuracy, critical errors, cost and speed, and only the options that clear the gate go forward. The cheapest model is often good enough; sometimes it isn't, and the test set shows which.

## Governance

Clear rules help people use AI openly instead of quietly. We write a short usage policy, classify your data from public to restricted, match each class to approved tools, and set up a simple way to request new tools and record every use in an AI register.

In the EU, the AI Act has required organisations using AI to support their staff's AI literacy since 2 February 2025. For a recognised structure, ISO/IEC 42001 sets out an AI management system and the NIST AI Risk Management Framework maps the risks.

## Enablement

Adoption is a people project. We train leaders first, then a network of champions who help their colleagues, then each team with examples from its own work, and track weekly active use team by team. The teams whose work changed most use it most.

## Process

Map, prove, measure, roll out: a first pilot in real use in about six weeks, then team by team.

1. **Map** — Score candidate use cases by value, feasibility and risk.
2. **Prove** — Build two or three working prototypes against real data.
3. **Measure** — Evaluate quality and cost before anyone scales anything.
4. **Roll out** — Policy, training and integration into daily tools.

## Deliverables

An AI opportunity map, working prototypes, an evaluation harness, a usage policy and data classification, a rollout plan and training for every team. You keep the prompts, the code and the test sets.

- AI opportunity map
- Working prototypes
- Evaluation harness
- Usage policy
- Rollout plan
- Team training

## Stack

Enterprise AI assistants for everyday work, models from several providers for custom tools, retrieval over your documents, evaluation and monitoring tooling, and your existing identity and data-protection controls.

## FAQ

Answers to the questions we hear most often about AI programmes.

### We have no AI experience. Is that a problem?

No. Most engagements start with teams that have tried a chatbot and little else. We begin with the work, not the technology: where the time goes, what repeats and what good looks like.

### What about data privacy?

We classify your data first and match each class to the tools allowed to handle it: business terms that keep your data out of model training, EU hosting where you need it, and nothing confidential in consumer tools.

### How do you measure success?

Every pilot has a baseline and a target agreed before we build, such as minutes per task, error rate or response time. We measure the work itself, because people's sense of speed can be wrong: in METR's 2025 study, experienced developers expected AI tools to make them 24% faster and were 19% slower.

### Why do so many AI pilots stall?

Rarely because of the model. A 2025 MIT report found that most generative AI pilots it looked at had no measurable effect on profit and loss, while the few that worked were tied to a specific workflow and built into how people work. That's the approach we take.

### Which models do you use?

Whichever scores best on your test set at an acceptable cost and speed. We try several on the same cases, and the choice can change as models improve.

### Do we have to train our staff?

In the EU, the AI Act has required organisations using AI to support their staff's AI literacy since 2 February 2025; 2026 amendments softened the wording from ensuring it to supporting it. Beyond the law, training is what turns a tool into a habit.

### What does AI governance involve?

A short usage policy, a data classification, a list of approved tools, a simple way to request new ones and a register of AI uses. ISO/IEC 42001 and the NIST AI Risk Management Framework give it a recognised structure when you need one.

### How long does it take?

About six weeks to a first pilot in real use: two weeks of discovery and scoring, two to three weeks to build and a week to measure. The rollout then goes team by team.

### Will AI replace our people?

In the pilots we run, it takes over the repetitive parts of a job, such as re-keying and first drafts, and leaves the judgement with people. A large study of customer-support agents found the biggest gains among newer staff.

## Capabilities

- Use-case discovery (Strategy) — proficiency 5/5
- Rapid prototyping (Engineering) — proficiency 5/5
- Evaluation (Quality) — proficiency 4/5
- Governance (Risk) — proficiency 4/5
- Team enablement (People) — proficiency 3/5

## In numbers

- Use cases assessed: 20 +
- Time to first prototype: 2 wks