01
Leadership
Change begins with leadership, not with IT. We help boards and executive teams make AI decisions that show up in the results — and stay in the 30 % that succeed.
Every process has steps that must stay predictable. And steps where AI brings more than rules can.
We help you draw the line before it forms by accident — so the transformation delivers outcomes, not one of the 70 % that fail.
Starting point
70%
of AI transformations fail.
The reason is rarely the technology itself. It’s the conflation of a deterministic process with non-deterministic AI — and the wrong tool ending up in the wrong place.
- 74 % trust human judgement over automation
- 68 % keep processes under ten steps
- — reliability is the main challenge, not the model
Source: UC Berkeley, survey of 306 AI professionals
How we work
Leadership, technology, people — all three at once.
AI transformation is not a technology project, an IT project or an HR project. It is all three, running in parallel. When one falls behind, the whole thing collapses with it.
The model in full: AI-Driven Company — book, in Finnish, free under CC BY 4.0.
02
Technology
A deterministic process layer and a non-deterministic AI layer, connected under one architectural view. Not a set of point tools — a managed whole that scales into production.
03
People
Skills and culture at the centre. Technology alone is not enough — you need the people who can use it the way the architecture intends.
Where architecture fits
The line runs through the middle of your stack.
Without an architectural view, the boundary between predictable process and AI judgement gets drawn by accident — and the wrong things end up on the wrong side.
Outcome Business outcomes
01 · Deterministic Process layer Workflows · rules · audit trail · same input → same output
— The line Latentti draws —
02 · Non-deterministic AI layer Agents · LLMs · judgement · probability · human-in-the-loop
Foundation Data & integrations
Both layers exist. The question is always per step: which side does this belong on?
How an engagement runs
Six steps, no surprises.
- 01 Identify processes and decision levels
- 02 Set business goals
- 03 Map data sources and integrations
- 04 Redesign processes
- 05 Implement automation and AI agents
- 06 Monitor and iterate continuously
Framework
AEAF — enterprise architecture for the blended workforce.
A TOGAF-class enterprise-architecture framework for the enterprise where humans and AI agents work as one workforce. It keeps TOGAF's governed baseline → target spine and rebuilds what a non-human actor breaks. Two volumes.
AEAF · Book 1 PDF · ~400 PP
AEAF: The Framework
The foundation. The thesis, the meta-model, the five domains (BDAT + Intelligence & Agency), dual-audience principles compiled into runtime guardrails, the method, and continuous design-time-plus-runtime governance.
Read PDF AEAF · Book 2 PDF · ~375 PP
AEAF in Practice
The adoption guide. Tailoring, adoption archetypes, the playbooks, a worked end-to-end walkthrough, the nine artifact templates, and a traceability spine back to Book 1.
Read PDFAEAF answers and extends TOGAF; it is independent, original work, neither derived from nor endorsed by The Open Group. TOGAF is a registered trademark of The Open Group.
Writing & open work
The thinking, the templates, the foresight. We publish ours.
Open artefacts we use in client work — a book, two templates, two foresight pieces. CC BY 4.0 and MIT. Free to read, fork, adapt and contribute back.
BOOK ai-driven-company CC BY 4.0
The Tri-Stream model in book form — for boards and executive teams.
Why 70 % of AI transformations fail and how leadership, technology and people run in parallel. Finnish. Free. Adapt and reuse under CC BY 4.0.
TEMPLATE business-context-ai-template MIT
TOGAF-based business context for AI development tools.
Three tiers — Quick Start (4–6h), Full (8–16h), Enterprise (16–40h). Includes a Claude Code agent for guided completion. Describe your business once, reuse across every AI project.
TEMPLATE ea-ai-context-template MIT
Enterprise architecture rules for AI tools.
Architecture principles, patterns and ADRs as YAML. Tells AI agents how your organisation builds software — so the output matches your standards, not the model’s defaults.
PAPER future-of-enterprise-architecture-2026-2031 PDF · 27 PP
The Future of Enterprise Architecture 2026–2031
A board briefing on how the firm itself reshapes — capability portfolios, determinism routing, knowledge stratification, the two-speed enterprise. Anchored in Stack Overflow, JetBrains, McKinsey, Forrester and the EU AI Act timeline. Six bets, each with a falsifier.
PAPER operational-worker-2026-2031 PDF · 24 PP
The Operational Worker in the Three-Threads Transition
A companion to the board briefing: six workers, two snapshots each, told as people rather than skill matrices. Sales, dev, customer service, retail, production, claims. Some make the cognitive turn cleanly. One leaves the field. The shape of the transition where it actually lives — at the desk, on Tuesday morning.
Pull requests welcome on the templates; the foresight pieces each carry a 6-month watch-list of falsifiable indicators. Longer technical reading: executive read (44 pp, 1.9 MB).
Talk to us
You meet the people who do the work.
No pyramid of analysts, no pre-sales layer. For implementation we partner with your teams or our trusted network.
