AI Implementation Strategist
Exploring AI is good. Shipping it is better.
Move beyond pilots and implement agentic systems that transform your business. With built in governance, observability, and team enablement from day one.
AI Implementation Strategist
Move beyond pilots and implement agentic systems that transform your business. With built in governance, observability, and team enablement from day one.
Digital Transformation delivered
10–30%efficiency gains by digitizing existing workflows.
AI Transformation has the potential for
60–80%cost reduction on cognitive work by replacing process steps entirely.
The technology to deliver already exists. The bottleneck is process redesign and organizational willingness.
of executives now name organizational data quality as the #1 barrier to GenAI goals, up from 56% just six months earlier.
KPMG Executive Pulse · 2025
of firms have a mature management infrastructure capable of orchestrating and governing AI agents effectively.
Everest Group · 2025
of IT & security leaders have no formal policy for AI agents even though 96% say agents are a rising risk.
SailPoint · 2025
of agentic initiatives never reach enterprise scale. Most stall as expensive proofs-of-concept that demos well but ships nothing.
Accenture · Wipro · 2025
Most failed deployments share one root cause: AI was layered on top of broken workflows, with no data foundation, no governance, and no plan for how the system would integrate with the company.
I work with a trusted network of specialist professionals to help you design an operating layer where agents, automations, and people share work, instrumented from the first sprint and governed from the first agent.
We start with a structured discovery across revenue, cost, and operations, sizing where agents will move the needle, where they won't, and where the highest-ROI / lowest-risk wedge lives. The output is a prioritized portfolio, not a hopeful backlog.
82% of executives now name data quality as their top barrier. We assess your data maturity, ontology, and real-time access and tell you honestly which workflows are ready for agents now versus which need foundation work first.
Agents are only as useful as the systems they can read and write to. We map the CRM, ERP, ticketing, comms, and data warehouse surface, define the interoperability protocols, and design the integration layer your agents will live on.
The #1 differentiator of AI high-performers is workflow redesign. We don't build a faster version of your current process; we re-design the workflow around what agents and humans each do best. Parallel where it used to be sequential. Self-correcting where it used to be brittle.
Policy-as-code, role-based access, audit trails, human-in-the-loop checkpoints for the moments that matter, wired in from day one, not retrofitted after the first incident. Every agent is observable, every decision is traceable, every action is reversible.
A system no one understands becomes shelfware in 90 days. We deliver tailored enablement for the operators, owners, and reviewers so your team can extend agents, write new prompts, audit outcomes, and ship the next workflow without us.
Strategy and delivery tailored to your context.
Full operational accountability across discovery, design, governance, and stabilization. The build runs with a curated team of forward-deployed engineers, data scientists, and AI specialists embedded alongside your people in your stack, not handed off over the wall. The team is sized precisely to the scope, transfers full ownership the moment your team is ready, and remains available for training and upskilling.
Three operating models. Pick the path that fits your team and shift between them as your team grows into the work.
Most back-office work today is about communicating where the work is: up the chain, down the chain, across the seams between teams. Agentic systems absorb that layer. People focus on the strategic, creative, and high-judgment work instead.
Half the week answering "where is this?" and "who's blocked on what?"
The system already knows.
People ask better questions.
Context drops at every boundary — Sales → CS, Product → Marketing, Hiring → Onboarding.
Agents carry context across the seams. Humans handle the judgment calls.
Project management, scheduling, follow-up, summarising — the work about the work.
People reclaim the hours
and spend them on the work itself.
Issues surface late. Teams scramble after the fact.
Agents flag and draft responses before problems hit a human's inbox.
Slides, status decks, weekly updates — work about what work was done.
Leadership queries the system directly. Insight, not theatre.
Borrowed from YC's AI-native playbook: AI shouldn't be a tool your company uses. It should be the substrate work flows through — making every action queryable, every decision traceable, every loop closeable.
A faster bad process is still bad. We use process intelligence to understand current state, then redesign — borrowing from the parallel, agent-led patterns that the McKinsey high-performers are running.
Brand, legal, comp, customer-facing decisions — humans approve. Routine triage, drafting, scheduling, monitoring — agents act. The boundary is a design decision, not an afterthought.
Policy-as-code, least-privilege access, observability, adversarial testing — the same principles as DevSecOps, applied to agents. Trust isn't built post-incident; it's engineered before deploy.
Multi-agent systems deliver 60% fewer errors and 40% faster execution. We design fleets of focused agents that coordinate through orchestration — not one heroic prompt trying to do everything.
MIT data is unambiguous: external solutions ship 2× more reliably than internal builds. We bring the platforms and patterns; you bring the context. The moat is your data and your design — not the framework.
A first conversation is 60 minutes, focused, and no obligation. We'll talk about where you are, what you're trying to achieve, and whether working together makes sense.