Build the workflow. Govern the rollout.
Where the workflow map becomes a working integration. A 4–8 week embedded build that ships with the governance to keep it running.
For functional leaders ready to embed AI into a specific workflow.
Integration scope and pricing locked before the build starts. No overruns mid-engagement.
The Integration engagement is where the workflow map becomes a working integration — the build, the governance, and the change leadership work that turn a diagnosed workflow into an operating one. Built to survive contact with finance, IT, and risk. Tools selected for the work, not for the demo.
Brynjolfsson, Li and Raymond's 2025 RCT (n=5,172, Quarterly Journal of Economics, Vol. 140) found AI delivered a 30% productivity gain at the bottom of the skill distribution, no measurable gain at the top, and a measurable quality decline among the most experienced.7 Integration design accounts for that distribution — not against it.
Two scopes
Single-workflow integration
Four weeks. One workflow, one adoption arc. For teams that know which workflow they want to change first and want to prove the model before expanding. The governance and change work is scoped to the workflow — contained, fast to deploy, easy to evaluate.
4 weeks · single workflow
Multi-workflow integration
Six to eight weeks. Two or three workflows with a shared governance layer. For organizations rolling out AI across multiple functions in a single sequence. The governance layer is built once and applied across all workflows in scope — reducing redundancy and giving IT and risk a single audit surface.
6–8 weeks · multi-workflow + governance
What's inside
Tool selection — which AI fits the work, not just the budget or the vendor relationship. Prompt and context engineering — how the tool is configured to behave reliably on your team's actual tasks. Integration with the systems your team already uses, so the AI fits into the workflow rather than running alongside it. Governance and risk controls — spending limits, output checks, a clear record of what the AI decided and who owns that decision if something goes wrong. Built against international AI risk and management system standards — covering risk identification, control design, and operational accountability.
Standards we work to
NIST AI RMF — The international reference standard for identifying, assessing, and controlling AI risk across the full system lifecycle.
ISO/IEC 42001 — The international management system standard for AI governance. Provides the audit-ready structure organizations need to demonstrate responsible AI use to regulators, clients, and boards.
Manager cascade and role-based training — so the people doing the work know how to use it, and the people managing the work know how to oversee it. The full sequence from "what should we build" to "the team is using it."
Frameworks applied
ADKAR — The individual-readiness layer of the rollout — tracking where each person sits in the change journey as the integrated workflow goes live.
Kotter's 8-Step Model — The organizational layer — sequencing the rollout so momentum builds, blockers clear, and the change holds before the engagement closes.
Diffusion of Innovations — The adoption-curve layer — sequencing rollout cohorts so innovators and early adopters become the social proof the majority needs, rather than dropping the whole organization in at once.
7Brynjolfsson et al., Quarterly Journal of Economics 2025 — field experiment, n=5,172 customer service agents. AI assistance produced +30% productivity for low-skill workers, ~0% for high-skill workers, and a measurable quality decline at the top of the skill distribution.