Isaak Lagerman — AI & Digital Consulting

AI & digital consulting

Hi! I am Isaak

From Stockholm, I help organisations put AI to work where it actually moves the needle — not where it sounds good on a slide.

I hold an M.Sc. in Industrial Engineering & Financial Mathematics from KTH and have worked with strategy and modelling across energy, private equity and consulting. I founded an independent AI collective.

Isaak L.

Why Me

BUSINESS REALITYUSEFUL FIRST

Most smaller businesses do not need convincing that AI might be useful. The harder questions are where to start, whether it is actually working, what happens to the data, how much to trust the output, and how to bring the team along without breaking what already works.

You do not know where to start.
Every answer online is either a forty-page enterprise framework or a sales pitch for one specific tool. Nobody has told you what your business should actually do first.
You have tried something, but cannot tell if it works.
A subscription here, a free trial there, and now you are either paying for software nobody opens anymore or you genuinely cannot see whether it is making you money.
You do not fully trust what it gives you.
If AI gets something wrong in front of a client or feeds a bad number into a decision, there is no legal department to absorb that. It is just you.
You are not sure where your data goes.
Once information is pasted into a tool, it is hard to know who can see it, where it is stored, or whether it is quietly training someone else's model.
Your team is not exactly on board.
Some people are quietly worried this is step one toward replacing them. Others have decided to ignore it and hope it blows over.
The thing you have now mostly works.
The real fear is not whether AI could help. It is whether plugging it in will break the workflows currently keeping the lights on.

How I help

Start where the risk is low and the value is visible.

I do not hand you a tool and disappear. I start with the one or two workflows that can move a number you care about, build in a human check on anything customer-facing or risky before it ever goes live, and stay involved until your team is actually using it, not just paying for it.

In a free 30-minute call, we can figure out where AI should start in your business, what should stay untouched for now, and what a useful first outcome would look like.

Book a free 30-minute call

Vertical image for Isaak Lagerman's approach section

My approach

1.Audit before anything else
Before I suggest a single tool, I look at what you're already paying for, what's actually being used, and where the real bottleneck is. No recommendations until I understand the problem - not the other way around.
2.Start with two or three workflows, not a transformation
We don't "do AI" across the business. We pick the specific, high-volume tasks where a win is measurable in weeks, and we prove it there first.
3.Oversight built in, not bolted on
Anything customer-facing, financial, or compliance-related gets a human checkpoint by design. AI supports the decision - it doesn't get the final word.
4.I know which data actually matters
Not all data carries the same risk. I identify what is actually sensitive - client records, financials, regulated information - and make sure it is handled properly.
5.Your team comes with us
I talk to the people actually doing the work, not just the owner. Most AI rollouts fail because staff feel like it was done to them, not with them.
6.We agree on what "working" means - before we start
Clear numbers, set in advance, so nobody's guessing later whether the spend was worth it. If something isn't working, I'll tell you, not dress it up.
7.Nothing you can't walk away from
If a tool changes its pricing or stops being the right fit, you keep your data and your workflow. No being boxed in.

Services

Case studies

BUILT TO REASONBUILT TO USE

These projects span consulting engagements, industry partnerships and independent product work.

Rejlers · Consulting engagement

Forecasting without friction.

A self-service electricity-demand model for Energy Markets Inspectorate reporting, designed around official scenarios and two easily sourced public inputs.

~25%of Swedish grid operators adopted it in year one

Rejlers electricity-demand forecast model engagement

Forefront Consulting · Product engagement

From reviews to product.

A prototype and delivery plan for digitizing Forefront's five-area career-development framework and supporting continuous, AI-assisted employee development.

44functional and non-functional requirements defined

Product engagement addressing fragmented employee review processes

Rejlers · Simulation project

Make the transition playable.

A four-stakeholder simulation that replaced a conventional conference presentation with coordinated decision-making under uncertainty.

4+ yrsreused at industry conferences and events

Rejlers energy-transition simulation game

Rejlers · Solo strategy engagement

AI without the theatre.

An internal capability assessment paired with an external scan of technologies, vendors and likely market direction.

200+person division assessed across 19 units

Explore all case studies

Start here

Not sure yet?

Talk to my AI rep for a quick read on fit, relevant work and the most useful next step.

Talk to my AI rep

You do not know where to start.

You have tried something, but cannot tell if it works.

You do not fully trust what it gives you.

You are not sure where your data goes.

Your team is not exactly on board.

The thing you have now mostly works.

Start where the risk is low and the value is visible.

My approach

1.Audit before anything else

2.Start with two or three workflows, not a transformation

3.Oversight built in, not bolted on

4.I know which data actually matters

5.Your team comes with us

6.We agree on what "working" means - before we start

7.Nothing you can't walk away from

AI Strategy & Readiness

Data-Driven Products

AI Tooling & Automation