AI strategy and roadmap work decides where artificial intelligence creates real return, what to build versus buy, which models or vendors to commit to, and how to sequence adoption so investment risk is contained at every stage. The output is written: opportunity map, phased roadmap, risk register, executive memo — owned by you.
Enterprise AI strategy is not a slogan — it is a small number of specific decisions, each with technical, regulatory, and organizational consequences. Strategy work resolves those decisions with written evidence the executive team can act on and the board can defend.
The decisions below are the ones an AI strategy consulting engagement at G.E.T AI Labs is designed to answer. Each one is technical at its core; treating it as purely organizational is where most AI adoption programs quietly fail.
Most organizations have more candidate AI use cases than budget. Strategy work separates the use cases where AI delivers durable operational advantage from the ones where it produces a demo, a press release, and a quietly abandoned system 18 months later. The output is a ranked opportunity map with technical feasibility, expected impact, and confidence levels per item.
Build-versus-buy decisions in AI carry asymmetric consequences. Buying a vendor platform is fast but constrains the system to that vendor's assumptions and pricing curve. Building is slower but produces durable IP and full control. Strategy work resolves each candidate use case to a specific recommendation — buy, build, or hybrid — with the rationale documented.
Model and vendor selection happens before evaluation evidence exists in most organizations. The result is a commitment made on marketing claims that breaks under load. Strategy work pairs vendor and model selection with a Systems Evaluation (SEV) plan — so the commitment is grounded in benchmarked behaviour on representative data, not on the procurement deck.
Healthcare, financial services, insurance, critical infrastructure, and defense-adjacent work all carry regulatory regimes that constrain what AI systems can do, where data can sit, and how decisions must be explained. Strategy work treats regulatory fit as a first-class engineering constraint and scores each candidate against the specific regime that applies.
Most AI initiatives fail not on the model but on the data pipelines, the integration surfaces, the operational handoffs, and the staffing required to keep a deployed system reliable. Strategy work produces an honest assessment of where the organization sits today and what has to be in place before each phase of the roadmap can be executed.
AI strategy work at G.E.T AI Labs runs against a fixed set of operating principles. They are why the written outputs hold up against engineering reality, regulatory review, and competitive pressure — not because the principles are clever, but because they are non-negotiable on every engagement.
Every engagement begins with study of the client's domain — the operating environment, the data sources, the regulatory regime, the failure modes that already exist before any AI is added. Strategy that skips domain study produces recommendations that look reasonable on paper and break on contact with the business.
Each phase produces a written artifact that can stand on its own. An opportunity map, a technical landscape analysis, a roadmap document, a risk register, an executive memo. Engagements can pause, hand off to another partner, or be picked up by the in-house team without losing prior work.
Model recommendations are grounded in benchmark data on representative inputs, not in vendor pitch decks. Where evidence does not yet exist, the strategy says so explicitly and routes to a Systems Evaluation (SEV) or Prototype Development Program (PDP) to produce the evidence before commitment.
The person writing the strategy is a senior practitioner who will be accountable for the recommendation. There is no account team, no sales handoff to junior consultants, and no incentive to recommend a larger engagement than the question requires. Strategy work is scoped to answer the specific question and stops when the question is answered.
Every written artifact, every benchmark dataset, every architectural decision document, and every prototype produced during a strategy engagement is owned by the client at handoff. There is no platform dependency, no proprietary toolchain the client cannot leave, and no IP held back. The client can take the work to any partner and continue from where the engagement ended.
An AI Adoption Strategy (AAS) engagement at G.E.T AI Labs produces a defined set of written artifacts — the same set regardless of industry. Each one is structured so the in-house team or any other partner can pick up the work where the engagement ended.
The artifact list below is the spine of an AAS deliverable. A shorter Technology Opportunity Mapping (TOM) engagement produces the first two; a longer Deep Domain Research Sprint (DRS) adds specialized technical landscape work for a single problem area.
Ranked inventory of candidate AI use cases across the organization, with technical feasibility, expected operational impact, data and infrastructure requirements, and a confidence level per item. Produced during a Technology Opportunity Mapping (TOM) or as the opening artifact of an AI Adoption Strategy (AAS).
Written analysis of the model architectures, frameworks, infrastructure options, and vendor products relevant to the prioritized use cases. Covers strengths, limitations, deployment posture, and known failure modes — separating what is genuinely production-ready from what is still research-grade.
Phased adoption roadmap with sequenced initiatives, decision gates between phases, dependency mapping, and explicit budget and staffing assumptions. Designed so each phase produces evidence that informs the next, rather than committing the organization to a single multi-year plan.
Structured risk register covering technical risk (model failure modes, data quality, integration risk), regulatory risk (specific to the applicable regime), operational risk (staffing, MLOps, vendor lock-in), and strategic risk (competitive timing, IP exposure). Scored by severity and likelihood with mitigation recommendations.
A short, written executive memo — typically 4 to 8 pages — that summarizes the recommendation, the rationale, the key trade-offs, and the asks. Designed for the CEO, the executive committee, or the audit committee to read in one sitting and act on without further translation.
A board-ready presentation deck built around the executive memo. Designed for a 20 to 40 minute slot at a board meeting or technology committee, with appendix material that supports the technical and regulatory claims in the body of the deck.
AI strategy work is not always the right next step. The scenarios below are the ones where written strategy artifacts produce more value than another internal workshop or another vendor pitch. If one of these matches, the engagement is likely the right shape.
A board meeting is on the calendar and the directors want a written view of how the organization plans to respond to the AI shift. An executive memo and presentation deck grounded in actual technical and regulatory analysis lands better than a high-level slideware response.
Engineering or product wants a meaningful AI budget approved and finance wants evidence that the spend will produce durable advantage. A ranked opportunity map and phased roadmap with decision gates gives finance a structure to approve against.
A regulator — OSFI, a provincial privacy commissioner, the EU AI Act, a sector-specific authority — is requiring a written position on AI use, model governance, or risk management. A risk register and policy-aligned roadmap is the evidence that the organization has done the work.
A competitor has launched an AI-powered product, raised on an AI story, or repositioned against the organization. The right response is not a reactive AI launch — it is a written strategy that decides where to compete on AI capability and where not to.
Product leadership is considering a new line whose viability depends on AI capability. Before committing engineering headcount and roadmap quarters, a Technology Opportunity Mapping or Deep Domain Research Sprint resolves the technical feasibility question with evidence.
An in-house AI program or a prior vendor engagement has stalled, missed its targets, or produced systems that are not in use. Independent strategy work re-bases the program with an honest assessment, a revised roadmap, and a Systems Evaluation of what was built so far.
Big-firm management consulting and applied-AI-lab strategy consulting are different products. Both produce written outputs. The work behind those outputs, the people doing it, and what an organization can do with the result the day after handoff are not the same. The dimensions below are the practical difference.
| Dimension | McKinsey-style engagement | G.E.T AI Labs engagement |
|---|---|---|
| Depth of technical analysis | High-level — model categories, vendor short-lists, ROI estimates from analogs | Architecture-specific, with named models, benchmark data, and failure-mode analysis on representative inputs |
| Hands-on prototype work | Out of scope — implementation handed to a separate systems integrator | Available in the same engagement family via Prototype Development Program (PDP) — same practitioner, same written artifacts |
| Named practitioner accountable | Engagement partner names the team; juniors do the analysis under partner oversight | A named senior practitioner writes and signs the strategy. No analyst layer, no offshored research |
| Evaluation discipline | Qualitative — relies on vendor claims, industry analyst reports, and reference checks | Quantitative — Systems Evaluation (SEV) on representative data before commitment, with documented benchmarks |
| Ongoing technical leadership option | Not offered — engagement ends, client looks for an interim CTO elsewhere | Fractional CTO retainer available at 1-2 days per week for 3-12 months following strategy handoff |
| IP ownership of strategy outputs | Engagement deck and frameworks typically remain firm IP, re-used across clients | Every artifact, dataset, benchmark, and prototype is client-owned at handoff with no platform dependency |
We work with organizations beyond Canada, delivered remote-first: AI consulting in the United States, the United Kingdom, the Netherlands, and India.
Direct answers about scope, deliverables, timeline, pricing, and how AI strategy consulting from an applied AI research lab differs from a management consulting engagement.
AI strategy consulting is the work of deciding where artificial intelligence creates operational advantage inside a specific organization, what to build versus buy, which models or vendors to commit to, and how to sequence adoption against regulatory, technical, and organizational constraints. The output is a written set of artifacts — an opportunity map, technical landscape analysis, AI roadmap, risk register, and executive memo — that a board, executive team, or operating committee can act on with evidence rather than market narrative.
Management consulting firms approach AI primarily as an organizational and change-management problem, building decks that recommend AI adoption at a high level. AI strategy consulting from an applied AI research lab is grounded in the underlying technology: model architectures, evaluation evidence, infrastructure requirements, deployment failure modes, and regulatory fit. At G.E.T AI Labs the same team that writes the strategy is capable of building the prototype that tests it. The result is a roadmap that survives contact with the engineering reality, not a deck that has to be rewritten by the implementation team.
A typical AI Adoption Strategy (AAS) engagement delivers six client-owned written artifacts: an opportunity map across products and workflows, a technical landscape analysis of relevant model architectures and tooling, a phased AI roadmap with sequenced initiatives and decision gates, a risk register with severity and likelihood, an executive memo summarizing the recommendation, and a board-ready presentation deck. All artifacts are owned by the client and structured so future work — prototype, evaluation, deployment — can be picked up by the in-house team or any qualified partner.
Engagement length depends on the question. A 2-week Technology Opportunity Mapping (TOM) identifies where AI could create operational advantage across the organization. A 3-6 week Deep Domain Research Sprint (DRS) studies a single technical area in depth. A 4-8 week AI Adoption Strategy (AAS) produces the full adoption roadmap and risk register. For organizations that need ongoing senior technical judgement after the strategy is set, a Fractional CTO retainer runs 3-12 months at 1-2 days per week.
G.E.T AI Labs prices per scope, not per hour. A Technology Opportunity Mapping is a fixed fee against a written 2-week scope. An AI Adoption Strategy is a fixed fee against a 4-8 week scope. Fractional CTO is a monthly retainer. Every engagement is quoted in writing after a free initial conversation, with the scope, deliverables, and timeline defined before any commitment. Pricing varies with domain complexity and the depth of technical analysis the question requires.
Yes. G.E.T AI Labs works across 15 high-stakes industry domains including healthcare under provincial privacy regimes, financial services under OSFI prudential expectations and the federal Bank Act, insurance, defense-adjacent work, critical infrastructure, automotive, and geospatial. Strategy artifacts are produced with the relevant regulatory regime as a first-class constraint — not as a footnote — and risk registers explicitly score regulatory exposure alongside technical and organizational risk.
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