Helsinki (on-site) · Worldwide (remote)

AI transformation
that actually ships.

I led AI adoption across a 180-person mobile-game studio - engineering, art, design, narrative, production. Now I help product-driven companies turn AI from pilot graveyards into capability that compounds. By changing workflows and behavior, not just buying tools.

Book a discovery call → See the framework
Based
Helsinki, Finland
Engagement
3 - 6 months
Sweet spot
200 - 2,000 people
The problem

Most AI initiatives fail in predictable ways.

After watching dozens of these efforts, the failure patterns cluster into five real problems - and "we don't have the right tools" is almost never one of them.

01 - Adoption
The pilot graveyard

You ran 10-20 AI experiments. Two impressed in a demo. None shipped into the operating workflow. The reason is rarely the model. The workflow around the model never changed.

02 - Trust
Trust collapse on first failure

One hallucination can collapse a person's trust - and motivation - in the tool they're using. There's no organizational muscle for calibrated trust: reading the outputs and knowing which to verify and which to ride.

03 - People
Identity threat in senior craft

Senior creatives, designers, analysts don't resist AI because they're stupid. The tool reframes the meaning of their craft. Most "training programs" ignore this entirely.

04 - Metrics
Measurement theater

Licensed seats and login counts are easy to pull; weekly hands-on use, hours saved on real tasks, and output quality almost never are. Without those signals, it's hard to tell what's actually working.

05 - Governance
The governance & velocity false binary

Lock everything down and nothing ships; leave it open and sensitive work leaks into third-party models. And with AI's capabilities, risks and rules still shifting, governance can't be set once - it has to stay calibrated as the ground moves.

The framework

The Adoption Engine.

Your AI Adoption is a four-part engine. When one part misfires, it sputters. When two misfire, it stalls.

Most AI failures aren't model failures. They come from a broken loop between four interdependent layers - and almost no one diagnoses which one is actually broken.

The only question that matters: which part is misfiring?
CONTINUOUS FEEDBACK LOOP 01 Inputs Data · Tools Governance Knowledge 02 Workflows Where AI inserts Handoffs · Decisions Friction points 03 Humans Skills · Trust Incentives Identity 04 Signals Adoption Outcomes Learning
How I work

Three phases. Each ships something real.

Phase boundaries aren't arbitrary - each ends with a concrete artifact you own and a decision point. You can stop after any phase if it isn't working.

PHASE 01 3-4 weeks

Diagnosis

Map the engine. Find the misfiring parts.

You leave with
  • Readiness report - scorecard across the four parts, with named bottlenecks
  • Use case portfolio - 8-15 candidates, scored on impact, effort, confidence
  • 90-day roadmap - sequenced first moves, owners, success criteria
PHASE 02 8-12 weeks

Pilot

Ship 2-3 use cases that move a metric.

You leave with
  • 2-3 working AI-integrated workflows in production with real users
  • Adoption + outcome dashboard with baseline and current state
  • Scaling playbook documenting what worked and what didn't
  • Trained internal champions who can carry the work forward
PHASE 03 3-6 months

Scale

Embed it into the operating cadence.

You leave with
  • Monthly portfolio governance ritual with clear decision rights
  • Internal capability map showing who knows what, where the gaps are
  • Portfolio dashboard, fully owned and operated by your team
  • Quarterly outside-in reviews - my role drops to advisory
Inside the work

What this looked like at Metacore.

I led AI transformation and adoption across Metacore, a 180-person mobile-game studio - engineering, art, design, narrative, production, ops. No pilot, no slide deck - a real operating model the teams ran themselves. Here's how it fit together.

AI transformation & adoption at Metacore

Real engagement

A 180-person mobile-game studio with highly autonomous teams. The brief: make AI a natural part of how engineering, art, design, narrative, production and ops already worked - safely, and at scale. I built it as an operating model teams ran themselves, not a top-down rollout.

Foundation
AI strategy, governance, policy, security and literacy - the confidence to experiment safely.
Enablement
Copilots, summarization, automation and cross-craft collaboration, embedded into real workflows.
Scaling
A champions network, Show & Tells and experimentation that spread practice peer-to-peer.
AI Champions Network - the engine of scale. Champions embedded across teams carried adoption through peers, not mandates. The single biggest driver of organic uptake.
Methodology Adoption scales through enablement and trust, not mandates. Usage means nothing if the work itself hasn't changed - so I measure behavior and outcomes, never licenses.
How this is different

Built to ship, not to advise.

Plenty of good AI strategy work exists. The gap I keep seeing is between the deck and the daily workflow - a maturity model and an org-chart suggestion rarely change what a team does on Monday. Everything here optimizes for the opposite: working systems in production, and a team that can run them without me.

Strategy-deck approach
  • Strategy decks and maturity models
  • Pick one use case, build a slide, walk away
  • Tool recommendations and vendor matrices
  • Training programs that ignore identity threat
  • Track seats licensed and tools deployed
  • Org chart redrawing as the deliverable
This practice
  • Diagnose the whole engine, not one part
  • Ship working pilots in 90 days with real users
  • Workflow-level intervention, not vendor selection
  • Behavior design rooted in habit and identity research
  • Measure weekly active use and downstream outcomes
  • Internal capability building - you become independent
About

Who you'd be working with.

Bhavya Omkarappa

Bhavya Omkarappa

AI Transformation · Helsinki

I help product-driven companies make AI part of how teams actually work - a change in workflows and behavior that lasts, not another tool rollout.

I was the AI Lead at Metacore, a 180-person mobile-game studio in Helsinki, running AI strategy, governance, enablement and education across every craft - from inside the teams doing the work. Before AI, I worked in product and delivery as a product owner and technical development manager, with a focus on operational excellence. That background is why I treat adoption as an organizational problem first, and a tooling one second.

I work with a small number of teams at a time, leaders included - those ready to move from planning to adoption.

Focus Product-driven companies
Method Systems · Workflow · Behavior
Engagement Solo · Limited slots
Let's talk

If your AI program isn't shipping, let's find out why.

A 30-minute first call - not the full diagnosis, just enough to understand your situation and get a first read on where the bottleneck might be. If it's a fit, we go deeper from there.

Book a discovery call → Send a note