AI Strategy Consulting
The complete buyer's guide for VPs and directors evaluating AI strategy consulting firms in 2026. Compare Big 4 vs boutique, pricing models, timelines, deliverables, and the 12-question scorecard every CIO should run before signing a statement of work.
AI strategy consulting is the disciplined process of identifying where AI can create measurable business value, sequencing the work into 60-90 day delivery sprints, and shipping production systems with quantifiable ROI. Unlike traditional management consulting, AI strategy consulting is engineering-first: it produces working systems, not slide decks, and the best firms guarantee outcomes.
For the broader category definition, see What Is AI-Native Consulting? — the operating model AIDOLS pioneered in 2024.
A compass, not a contractor. First results in 2-3 weeks, production in 90 days. Or you don't pay.
On this page
- What AI strategy consulting actually is
- The 4 types of AI strategy consulting firms
- What it costs in 2026
- Engagement timeline
- The 7 deliverables
- 12-question scorecard
- Industry-specific considerations
- Regulated environments
- Why most engagements fail
- AI-native vs traditional
- Case study patterns
- How AIDOLS approaches it
- Frequently asked questions
What AI strategy consulting actually is (and isn't)
AI strategy consulting is the engineering-first practice of mapping a business's highest-value AI opportunities, designing the operating model and target architecture to deliver them, and shipping the first production system within 90 days. The output is software in production, not a slide deck on a shelf.
That definition matters because the term is widely misused. Most buyers conflate four distinct categories of work, and the conflation is the single biggest reason AI engagements fail:
- AI strategy — deciding which problems to solve, in what order, with what operating model and architecture.
- AI implementation — the engineering work to build, deploy, and integrate the models, pipelines, and applications.
- AI advisory — executive coaching and education, often delivered in a few workshops and a strategy memo.
- Data science consulting — narrow, model-centric work focused on a specific dataset or analytical problem.
Real AI strategy consulting fuses strategy and implementation into a single team. Separating them — letting one firm produce the deck and a different firm do the build — is the structural failure mode in tier-1 engagements. Strategy decisions made without engineering constraints almost always require expensive rework when the build team arrives. A 2024 IBM Global AI Adoption Index study found that roughly 4 in 10 enterprises have actively deployed AI in some part of their business, yet most remain in pilot purgatory because the handoff between strategy and engineering breaks down.
The clean test for whether you are buying AI strategy consulting or something else: ask the firm what artifact appears at the end of week 12. If the answer is "a strategy document" or "an executive readout," you are buying advisory work dressed up as strategy. If the answer is "a production inference endpoint, an evaluation harness, and a live KPI dashboard," you are buying real AI strategy consulting.
The category also has a hard E-E-A-T requirement: the people writing the strategy must have personally shipped AI systems to production. This sounds obvious. It is not how the consulting industry operates. Tier-1 firms routinely send senior partners to sell, mid-level managers to write the deck, and contracted engineers to do the build. The strategist often has never deployed a model. AI-native firms invert this: the same person who interviews your VP of Engineering also writes the deployment pipeline.
The 4 types of AI strategy consulting firms (and when to choose each)
The market is not monolithic. Four distinct firm archetypes compete for AI strategy work, each with structural advantages and structural blind spots. Match the archetype to the engagement, not to the brand.
1. Big 4 & tier-1 strategy firms
The named brands of management consulting now have AI practices of 5,000-30,000 people each. They win on board credibility, global delivery muscle, and the ability to staff a 200-person rebuild across 14 countries simultaneously. They lose on speed, price, and engineering depth. Average time from kickoff to first production deployment: 9-18 months.
Choose when: regulated, multi-country enterprise modernization with heavy change management and you have $2M+ to spend.
2. Boutique AI-native firms
Specialist firms (15-150 people) that exist only because of AI. Engineering-first by construction: every senior on staff has shipped models to production. They standardize on outcome-based pricing, fixed-fee sprints, and 60-90 day delivery cycles. They lose on global scale and on industries where you need 50 consultants in the room. They win on time-to-value, ROI, and honesty about what AI cannot do.
Choose when: you need a production system in under 6 months, a named senior to own the build, and you value engineering rigor over deck polish.
3. Independent AI consultants
Solo or 2-person operators, often ex-FAANG ML engineers or ex-Big-4 senior managers. Hourly rates of $250-$600 with no firm overhead. Excellent for narrow technical scopes (a single model, a specific data problem). Inappropriate for rebuild work because they cannot run change management, cannot stand up an MLOps platform, and cannot hold a 12-person engineering team accountable.
Choose when: a single, well-scoped technical problem; or fractional CTO-style guidance for a small team.
4. Pure in-house build
Hiring senior AI architects, MLOps engineers, and a head of data directly. Total cost of $1.5M-$3M annually for a credible team of 5-8 people, with 6-9 months to fill the roles and 12 months before the first production system. Right answer for companies with permanent AI as a moat. Wrong answer for companies that need a system in production this quarter.
Choose when: AI is the product, you have an 18-month runway, and you have $2M+ in annual AI headcount budget.
Side-by-side comparison
| Dimension | Big 4 / Tier-1 | Boutique AI-native | Independent | In-house |
|---|---|---|---|---|
| Hourly rate | $400-$1,000+ | $150-$400 | $250-$600 | $200-$350 fully loaded |
| Time to first production system | 9-18 months | 60-90 days | 3-5 months | 12+ months |
| Engineering depth on senior staff | Variable | High (every senior has shipped) | High | Builds over time |
| Outcome-based pricing | Rare | Standard | Sometimes | N/A |
| Multi-country delivery | Yes | Limited | No | Limited |
| Engagement floor | $500K+ | $25K-$50K | $10K-$25K | $1.5M+ /year |
| Best for | Multi-country rebuild | 60-90 day production sprints | Narrow technical scopes | AI as the product |
What AI strategy consulting costs in 2026
Pricing in AI strategy consulting splits across three models, and the model you negotiate matters more than the headline rate. The same engagement can cost $250,000 or $1.4 million depending on structure.
Hourly billing: $150-$1,000+
The legacy model. Big 4 partners bill at $800-$1,000+ per hour; senior managers $400-$600; consultants $200-$350. Boutique AI-native senior staff bill at $250-$400. Independents at $250-$600. Hourly engagements are appropriate for diagnostic work with unclear scope, but they shift all delivery risk to the buyer and create incentives for scope inflation.
Fixed-fee per sprint: $25K-$400K
The default for AI-native firms. The work is broken into 2-12 week sprints with a defined deliverable and a fixed price. Risk shifts to the consultant. Buyers get a predictable budget and can stop after any sprint without penalty.
Outcome-based: a fraction up front, the rest tied to KPI
The newest model and the strongest signal of consultant confidence. A portion of fees (often 20-40%) is paid only if the engagement hits a defined outcome: cost reduction, revenue uplift, cycle-time reduction, or model accuracy threshold. AIDOLS, for example, structures its 90-day sprint with a 100% ROI guarantee. Few traditional firms will take this kind of structure because their cost base does not allow it.
Sample 2026 engagement budgets
- AI readiness assessment (2-4 weeks): $25,000-$75,000. Diagnostic only, no build.
- Production POC (4-8 weeks): $50,000-$250,000. Working system on a single use case with limited scale.
- 90-day enterprise sprint: $150,000-$400,000. End-to-end engagement: assessment, architecture, build, deploy, train, handoff.
- Multi-quarter rebuild program: $500,000-$2,000,000+. 3-7 production systems, target architecture rollout, embedded MLOps capability, change management.
- Big 4 enterprise engagement: $1,500,000-$10,000,000+. Same scope as above with 3-4x the headcount and 12-18 month timeline.
The cost question is the wrong question. The right question is cost-per-production-system. A $400,000 boutique engagement that ships 2 production systems in 90 days costs $200,000 per system. A $2,000,000 Big-4 engagement that ships 1 system in 14 months costs $2,000,000 per system. The unit economics are not close.
For a deeper breakdown of pricing by firm tier and engagement type, see the full AI consulting cost guide.
The AI strategy consulting engagement timeline
Two timelines dominate the market. The traditional 6-18 month engagement runs strategy, architecture, and implementation sequentially with separate teams. The 90-day AI-native sprint runs them concurrently with one team. The difference compounds: the sprint typically reaches week-12 production while the traditional engagement is still in the architecture review phase.
Traditional engagement (6-18 months)
- Months 1-2: Discovery, stakeholder interviews, current-state mapping
- Months 2-4: Strategy formulation, use-case prioritization, executive workshops
- Months 4-6: Target architecture design, vendor evaluation, board approval
- Months 6-9: Implementation team handoff, contract negotiation, kickoff friction
- Months 9-15: Build, integration, change management
- Months 15-18: Production deployment, training, go-live support
Failure mode: by month 6, the strategy team has rotated off and the implementation team inherits decisions they did not make.
90-day AI-native sprint
- Weeks 1-2: Operations mapping, AI readiness scoring, top-5 opportunity shortlist
- Weeks 3-4: Use-case selection, target architecture, build plan, ROI model
- Weeks 5-8: Engineering build: data pipelines, model training, evaluation harness
- Weeks 9-10: Production deployment, monitoring, MLOps handoff
- Weeks 11-12: User training, KPI validation, scale plan for the next 2-3 systems
Same team from week 1 to week 12. Strategy decisions are made with engineering reality already on the table.
The 7 deliverables every AI strategy consulting engagement should produce
Every legitimate AI strategy engagement produces these 7 artifacts. If your SOW omits any of them, you are buying advisory work, not strategy. Use this list as the artifact checklist when reviewing a proposal.
1. AI operating model
How AI work flows through the organization: who proposes use cases, who funds them, who builds them, who runs them in production. Names roles, RACI, intake process, and the relationship between central AI team, business units, and IT.
2. Target-state reference architecture
The system design: data layer, feature store, model registry, training infrastructure, inference layer, evaluation, observability, and the integration points to existing applications. Drawn at component level with named technologies.
3. Prioritized use-case backlog
Ranked list of 8-15 AI opportunities scored on estimated ROI, implementation feasibility, data availability, and strategic alignment. Each entry has a one-page brief with hypothesis, success metric, and engineering effort estimate.
4. Quantified ROI model
A spreadsheet that finance can audit. Baseline metrics, projected improvement, total cost of ownership (build, run, change management), payback period, and 3-year NPV. Sources and assumptions documented; vendor claims excluded unless validated.
5. Governance and risk plan
Data classification, model risk tiers, approval workflow, monitoring and drift detection, incident response, and the regulatory mapping (GDPR, HIPAA, EU AI Act, AIDA, Law 25, sector regulations). Owned by a named risk lead, not a footnote.
6. Talent and capability plan
Current-state skills inventory, target-state roles, hire-vs-train decisions for each capability, and a 12-month hiring plan. Includes a knowledge-transfer plan from consultant to client team — without this, you are building a permanent dependency.
7. 90-day implementation roadmap
Week-by-week plan for the first production system: who is doing what, the dependencies, the exit criteria for each milestone, and the named success KPI. Not a Gantt chart you will ignore — a working document that the build team uses every day.
Notice what is not on this list: a 200-page strategy document, a five-year vision deck, or a brand-name advisor's signature. None of those produce value on their own. Every artifact above is an input into a working system.
How to evaluate an AI strategy consulting firm: 12-question scorecard
Print this list and run it through every firm on your shortlist. Score each answer 0-2 (0 = vague, 1 = adequate, 2 = specific and backed by evidence). Anything below 18 out of 24 is a hard pass. Engineering-first firms answer all 12 in a single 30-minute call, with documents.
- 1.
How many AI systems have you shipped to production in the last 12 months?
What good looks like: Look for a number, not a range. Acceptable answers cite named systems and named clients (under NDA, anonymized is fine). "Many" is not an answer.
- 2.
Who personally will write the production code on this engagement?
What good looks like: You want named senior engineers, not staffing slots. Ask for their LinkedIn profiles and recent commits. If the senior who sells the work disappears at kickoff, walk.
- 3.
What is your average time from kickoff to first production deployment?
What good looks like: Anything over 6 months for a single use case is a structural problem. Top-tier: 60-90 days. Acceptable: 4-5 months for complex regulated environments.
- 4.
Do you offer fixed-fee or outcome-based pricing on this scope?
What good looks like: If the only option is hourly, the firm is not confident in its delivery. AI-native firms quote fixed-fee per sprint as the default; outcome guarantees are an upgrade.
- 5.
What compliance frameworks have you operated under in production?
What good looks like: Name the frameworks: SOC 2, HIPAA, GDPR, EU AI Act, PIPEDA, Quebec Law 25, sector regulations. Ask for evidence: audit reports, signed BAAs, DPIAs.
- 6.
What happens contractually if the project misses ROI targets?
What good looks like: Read the contract. Most traditional firms disclaim outcome responsibility. AI-native firms increasingly tie a defined fraction of fees to a measurable KPI.
- 7.
What is your client retention rate for follow-on engagements?
What good looks like: Above 70% suggests clients see real value. Below 40% suggests a sales-led firm whose engagements do not survive contact with the operator team.
- 8.
What does post-deployment support look like for the first 6 months?
What good looks like: Models drift, data shifts, edge cases surface. Acceptable answers include named SLAs for incident response, retraining cadence, and escalation paths.
- 9.
Who owns the IP, the model weights, and the training data?
What good looks like: The client should own all of it. Some firms try to retain rights to the model or fine-tuning data. That is a vendor lock-in trap. Decline.
- 10.
Can I speak to 3 reference clients in my industry, on the record, this month?
What good looks like: Strong firms put you on the phone with reference clients within a week. Weak firms produce a slide of logos and never deliver a live call.
- 11.
What tools, models, and platforms do you standardize on, and why?
What good looks like: Look for opinionated answers, not generic 'we are tool-agnostic' boilerplate. Tool-agnostic in practice usually means undifferentiated. AI-native firms have a default stack and explain when they deviate.
- 12.
What is your stance on open-source versus proprietary foundation models?
What good looks like: There is no single right answer, but the firm should have one. Beware firms that resell a single proprietary model regardless of the use case — the incentive structure is broken.
Want this as a downloadable scorecard?
Reach out and we will send the printable PDF version with a scoring rubric and a comparison sheet for your top 3 firms. Request the scorecard PDF.
Industry-specific AI strategy considerations
AI strategy consulting is not vertical-agnostic. The data, the regulatory load, the buyer behavior, and the highest-value use cases differ enough across industries that a generalist firm will underdeliver in any single one of them. The signal: your shortlist should include at least one firm with named production work in your vertical.
Financial services
Highest-value use cases: real-time fraud detection, credit underwriting, AML/KYC automation, trading-desk research assistants, claims triage. The strategy work has to start with model risk management (SR 11-7 in the US, OSFI E-23 in Canada, EBA guidelines in Europe). Firms that arrive without a named MRM lead are not credible. Production cycles are longer here: 90-120 days realistic, with a separate validation track running in parallel to development.
Read the financial services AI guide →Healthcare
Highest-value use cases: ambient clinical documentation, clinical decision support, patient flow and bed management, prior authorization automation, CME and medical education automation, drug discovery acceleration. PHI handling is non-negotiable: HIPAA in the US, PHIPA in Ontario, equivalents elsewhere. Strategy work has to map every PHI touchpoint and document the data classification before architecture starts. The right firms have signed BAAs in production and can produce SOC 2 Type II reports.
Read the healthcare AI guide →Manufacturing
Highest-value use cases: predictive maintenance, quality inspection (vision), supply-chain demand forecasting, dynamic scheduling, energy optimization. Strategy work has to bridge OT and IT — the data lives in PLCs, historians, and SCADA systems that most consulting firms have never connected to. Industrial connectivity, edge inference, and integration with MES/ERP systems are pre-requisites that often eat 30-40% of the schedule. Choose firms with documented brownfield deployments, not greenfield demos.
Read the manufacturing AI guide →Retail
Highest-value use cases: demand forecasting, dynamic pricing, personalization, inventory optimization, store-level labor scheduling, conversational commerce. Retail wins on velocity — short cycles, fast feedback loops, clean revenue attribution. Strategy work prioritizes use cases where you can attribute dollars within 30 days of deployment. Beware firms that lead with abstract personalization narratives but cannot produce a named retailer reference with quantified GMV uplift.
Read the retail AI guide →SaaS
Highest-value use cases: in-product AI features (copilots, search, summarization), product intelligence on customer usage data, churn prediction, support deflection, sales coaching from call transcripts. SaaS strategy work splits cleanly: AI embedded in the product (revenue impact) vs AI in the back office (cost impact). Firms that conflate the two under-deliver. The right shortlist includes at least one firm with named in-product AI launches and named cost-side deployments.
AI strategy consulting in regulated environments
The 2024-2026 regulatory cycle has changed AI strategy consulting permanently. A strategy that does not name the regulations, map the AI risk tier, and embed the audit trail into the architecture is not a strategy — it is a liability. Five regulatory regimes now dominate buyer requirements:
- EU AI Act: entered into force in August 2024 with phased obligations through 2026-2027. Risk-tier classification (unacceptable, high, limited, minimal) drives almost every architecture decision. High-risk AI systems require conformity assessments, fundamental rights impact assessments, post-market monitoring, and registration in the EU database.
- GDPR: the long-running European data baseline. AI strategy work has to produce a DPIA for any system processing personal data, document the legal basis, and design data minimization into the model lifecycle.
- PIPEDA & Canada AIDA: Canada's federal privacy law (PIPEDA) and the proposed Artificial Intelligence and Data Act (AIDA) under Bill C-27. AIDA introduces obligations on high-impact AI systems. Strategies for Canadian operations have to anticipate the regime even where the final regulations are still being finalized.
- Quebec Law 25: the most prescriptive privacy regime in North America since September 2023. Automated decision-making notices, mandatory privacy impact assessments, and a designated Chief Privacy Officer are required. AI strategy work in Quebec has to embed Law 25 from week 1, not as a downstream check.
- HIPAA: still the US standard for PHI handling. Any healthcare AI strategy needs signed BAAs with every model provider in the chain — including foundation model APIs.
Regulated firms typically pair their AI strategy with our 32-page AI Governance Charter 2026 — board-ready policy templates with EU AI Act and NIST AI RMF mapping built in.
For a deeper Canada-specific compliance walkthrough covering PIPEDA, AIDA, and Quebec Law 25 in production AI deployments, see our research report on Canada AI compliance: PIPEDA, AIDA, and Law 25.
The single most expensive mistake in regulated AI strategy consulting is treating compliance as a downstream review. By the time the security and risk team sees the architecture, the data flow has already been decided and re-architecting costs 5-10x what it would have cost to design correctly in week 2. Engage a named risk lead from kickoff or do not engage.
Why most AI strategy consulting engagements fail
Industry research from McKinsey, BCG, IBM, and Gartner converges on a hard finding: a majority of enterprise AI initiatives never reach production, and the fraction that do often miss their business case. The 2024 IBM Global AI Adoption Index put active deployment at roughly 42% of surveyed enterprises, with most of the rest stuck in extended pilots. Five failure modes account for almost all of it:
1. Slide-deck deliverables
The strategy is delivered, applauded, and never built. By month 9 the deck is referenced in a quarterly review and then forgotten. Mitigation: refuse to sign an SOW that does not include code.
2. Strategy without engineering
Strategists make architecture decisions without ever having shipped a model. By the time the build team arrives, half the design is unbuildable. Mitigation: insist the strategist personally writes production code on the engagement.
3. Misaligned KPIs
The pilot succeeds on accuracy but never moves the business KPI it was meant to move. Mitigation: tie the success metric to a finance-owned number from week 1, not an AI-team metric.
4. Scope creep
What started as a 90-day pilot becomes a 14-month rebuild as new stakeholders attach unrelated wishes. Mitigation: fixed-fee per sprint with explicit out-of-scope clauses; new requests become a new sprint, not an extension.
5. No production deployment
The model lives forever in a notebook on a data scientist laptop. There is no deployment pipeline, no monitoring, no on-call. Mitigation: production deployment is a milestone in week 10 of every engagement, not an aspiration.
6. Permanent vendor dependency
The consulting firm structures the engagement so the client team never inherits the system. The retainer becomes the architecture. Mitigation: every engagement contract names a knowledge transfer milestone with a named client engineer.
For a longer treatment of failure patterns and the structural reforms needed inside enterprise AI programs, see our analysis on why AI projects fail.
AI-native vs traditional AI strategy consulting
The cleanest framing for the 2026 buying decision: are you hiring an advisory firm with an AI practice, or an engineering firm with an AI strategy practice? The two are structurally different businesses optimizing for different outcomes. Both have a place; confusing them is what kills budgets.
| Dimension | Traditional AI strategy consulting | AI-native strategy consulting |
|---|---|---|
| Primary deliverable | Slide deck, strategy document | Working production system + roadmap |
| Pricing model | Hourly billing, time & materials | Fixed-fee per sprint or outcome-based with guarantee |
| Engagement length | 6-18 months | 60-90 days per system |
| Strategy & engineering | Separate teams, sequential handoff | Single team, concurrent delivery |
| Senior staff background | MBA, ex-strategy | Engineering, has shipped models in production |
| Default success metric | Strategy approved by board | Business KPI moved (cost, revenue, cycle time) |
| Code ownership | N/A — no code is produced | Client owns 100% of code, weights, data |
| Knowledge transfer | Implicit, often retained as IP | Named milestone with client engineer |
| Risk allocation | Borne by client | Shared via fixed-fee or outcome guarantee |
| Standard headcount on-site | 12-50 consultants | 3-8 senior engineers |
| Best for | Multi-country regulated change programs | Production AI in 60-90 days with measurable ROI |
Neither model is universally better. Traditional firms exist because some buyers genuinely need a 200-person, 14-country, 18 -month program. The mistake is hiring a traditional firm for a problem that is actually a 90-day engineering sprint — and paying 5-10x for the privilege.
Case study patterns: what AI strategy consulting outcomes look like
Anonymized but representative. Each pattern below describes the kind of result a well-scoped 90-day AI strategy engagement produces when the buyer-side conditions hold (executive sponsor, accessible data, named operational owner). All numbers are real, drawn from AIDOLS engagements; client names are withheld at the operator's request.
Pattern A — Manufacturing, North American mid-market
A Toronto-based industrial manufacturer compressed quoting cycle time by 40%
The brief: a 9-day average quote-to-customer cycle bleeding winnable revenue to faster competitors. The strategy work identified that 70% of the cycle was internal handoffs between sales engineering, materials, and finance. The 90-day build shipped a quoting copilot trained on 4 years of historical quotes plus current materials cost feeds. Outcome: 40% reduction in average cycle time, an estimated 8-figure annualized revenue uplift from improved win rate, and a production system handed to the in-house team in week 12.
Engagement type: 90-day AI-native sprint. Total fees: mid six-figures. ROI in year one: greater than 10x.
Pattern B — Financial services, Canadian regional institution
A Bay Street financial institution cut compliance review backlog by 60%
The brief: a regulatory document review queue growing faster than analyst capacity, with a 90-day SLA risk. The strategy work mapped the review workflow, identified the 30% of cases that could be safely auto-classified, and designed a human-in -the-loop review pattern with named MRM controls. The build shipped a classification system with a full audit trail and passed a second-line model risk review at deployment. Outcome: 60% backlog reduction within 90 days, analyst capacity redirected to high-judgment cases, zero regulatory findings in the first post-deployment audit.
Engagement type: 120-day regulated sprint. Total fees: low seven figures. Compliance frameworks: OSFI E-23, PIPEDA, Quebec Law 25.
Pattern C — Healthcare, multi-site clinical network
A Canadian clinical network reduced documentation burden by ~70% per provider
The brief: provider burnout driven by 2-3 hours of after-hours documentation per shift. The strategy work prioritized ambient clinical documentation over alternative copilot use cases on the grounds that the time-savings translated directly to retention economics. The 90-day build shipped an EHR-integrated documentation system under a signed BAA with the foundation model provider, structured note-generation in real time, and a clinical evaluation harness with named accuracy thresholds. Outcome: roughly 70% reduction in documentation time per shift, measurable retention impact on a 12-month rolling basis, and full PHIPA alignment in deployment.
Engagement type: 90-day healthcare sprint. Compliance frameworks: HIPAA, PHIPA, SOC 2 Type II.
How AIDOLS approaches AI strategy consulting
AIDOLS is an AI-native strategy consultancy. Every senior on staff has personally shipped AI systems to production. Every engagement produces working code, not slide decks. Every contract is fixed-fee per sprint or outcome-based — never hourly.
Our methodology is four phases: Architecture → POC → Validation → Scale. Architecture maps your operations and produces the target-state design in 2 weeks. POC ships the first production system in 2-3 weeks against a named KPI. Validation runs the system against real production traffic for 4-6 weeks with continuous evaluation. Scale extends to the next 2-3 systems and embeds MLOps capability in your team.
Two non-negotiables that shape everything we do:
- First results in 2-3 weeks, production in 90 days. Or you don't pay. The first working result is live within 21 days of kickoff and the full production system by day 90, or fees are refunded.
- 100% ROI guarantee. The 90-day engagement hits the agreed business KPI or fees are refunded. The KPI is named in writing on day 1, signed by a finance-side executive, and tracked weekly.
Read the full methodology and the engineering principles behind it on the methodology page, and our governance posture (model risk, data handling, compliance mapping) on the governance page. If you need a full service catalogue, the services index covers every named engagement we run, and the locations page shows where we deliver in person.
For an end-to-end framework view that pre-dates and informs how we structure consulting work, see the 5-phase AI adoption framework. And to compare the named engagements we deliver, the 90-day AI readiness sprint is the most-bought package.
A compass, not a contractor.
Frequently asked questions
The 12 most common questions VPs and directors ask before commissioning an AI strategy engagement. Answers reflect 2026 market conditions.
Sources cited
- IBM Global AI Adoption Index 2023 — enterprise AI deployment rates and pilot-to-production conversion.
- McKinsey, "The state of AI" — high-performing AI adopter EBIT contribution and adoption benchmarks.
- Stanford AI Index Report 2024 — global AI adoption, investment, and policy trends.
- Deloitte State of AI in the Enterprise — enterprise AI investment patterns and ROI expectations.
- Gartner AI Insights — analyst guidance on AI strategy and consulting market structure.
- BCG, AI Insights — AI value creation patterns across industries.
- EU Artificial Intelligence Act — regulatory text and phased obligations through 2026-2027.
- Commission d'accès à l'information du Québec — Law 25 — Quebec privacy modernization and automated decision-making provisions.
- ISED Canada — Artificial Intelligence and Data Act (AIDA) — proposed obligations on high-impact AI systems under Bill C-27.