End-to-End AI Consulting: From Strategy Through Production

What End-to-End AI Consulting Actually Means (and What It Isn't)

In short

End-to-end AI consulting is a single-partner engagement model: one contract, one team, one accountability line from board deck to running system to trained end users. It is not strategy-only (McKinsey/BCG), not build-only (Accenture/Deloitte), and not training-only. The defining test is whether the same senior team that briefs the C-suite in Week 1 is still on the ground in Month 9 tuning the shipped model.

The phrase gets used loosely, so it is worth naming what it actually means. End-to-end AI consulting is a delivery model where one firm owns the full lifecycle of an enterprise AI initiative — from strategy and use-case selection, through data readiness, solution design, EU AI Act risk classification, build, integration, deployment, workforce training, and post-launch operations. One contract, one team, one accountability line.

The traditional enterprise stack looks nothing like that. A typical Fortune 500 or Nordic enterprise AI programme brings in a strategy firm (McKinsey, BCG, Bain) to write the roadmap, a system integrator (Accenture, Deloitte, IBM) to build it, a training vendor to run the change-management workshops, and often a specialised MLOps contractor to run the pipeline. Four vendors, three major handoffs, no single owner of the outcome.

Gartner labelled this shift in its 2026 Generative AI Consulting & Implementation Services review a "crucible moment for services" — clients are demanding outcome-anchored delivery instead of staff augmentation, and firms that cannot own the outcome end-to-end are losing renewals. The market is repricing accordingly.

End-to-end is not a size claim. Big 4 firms have the headcount but rarely the model — partners sell, juniors deliver, and the strategy team and the build team file into separate P&Ls that do not communicate. A boutique of 30 senior operators can run a truer end-to-end engagement than a Big 4 team of 300 because the same people are physically in the room from workshop through go-live.

Alice Labs has run this model on 100+ enterprise AI engagements since 2023. The pattern below is what we ship. For a broader definition of the space and how it fits the consulting universe, our what is AI consulting guide is the entry point.

The Pilot-to-Production Cliff: Why the Handoff Model Fails

In short

Gartner's April 2026 I&O survey (n=782) found 89% of enterprise AI agent pilots fail to reach production; the 11% that do deliver 171% ROI. ISG's 2025-2026 data puts full-production rate at 31% across all AI use cases with only 25% hitting projected ROI. The root cause is rarely model quality — it is context, KPI, and accountability leakage across the strategy-to-build-to-training handoffs.

The failure statistics have gotten worse, not better, as generative AI matured. Gartner's April 2026 Infrastructure & Operations survey (n=782) reported that 89% of AI agent pilots fail to reach production. Of the surviving 11%, average ROI was 171%. In other words: the projects that survive are highly profitable, but almost none survive.

ISG's 2025-2026 cross-industry data tells the same story from a different angle: only 31% of enterprise AI use cases reach full production, and only 25% hit projected ROI. McKinsey's 2026 State of AI Trust report calls this the "scaling gap" and names it explicitly as the industry's core problem heading into the agentic era.

When Gartner drilled into the failure population, the top single cause was not technology — 73% of failed projects had no agreed definition of success before kickoff. That is a handoff artefact: a strategy deck ships to implementation with aspirational language ("transform customer experience") that cannot be measured, the SI builds against ambient interpretation, and by Phase 6 nobody can say whether the thing worked.

The second-order effect: only 6% of AI-adopting organisations qualify as high performers on Gartner's scale. The bar is not being cleared, and it is not being cleared in a very specific way — pilots land, ROI is not proven, budgets rotate, teams move on. The next pilot starts from a similar-but-different premise. The organisation ships experiments, not systems.

The end-to-end model exists precisely to close these gaps. When the same team that wrote the KPI is present when the KPI is measured, translation loss goes to zero. When the strategy team sits inside the build team, ambient interpretation cannot happen — the person who wrote "-40% cycle time" is the same person shipping the code.

The Six Phases of an End-to-End Engagement

In short

An end-to-end engagement has six phases: (1) Discovery and value mapping, 2-4 weeks; (2) Data and platform readiness, 3-6 weeks; (3) Solution design and EU AI Act risk classification, 2-3 weeks; (4) Build, integration, and human-in-the-loop testing, 6-16 weeks; (5) Deployment, change management, and workforce training; (6) Post-launch operations with quarterly optimization. Each phase has fixed deliverables and a phase-gate exit — buyers can stop after Phase 1 or Phase 3 without penalty.

The Alice Labs framework maps to the industry consensus documented in Gartner and GrowExx's AI implementation roadmap analyses. Six phases, fixed scope per phase, exit ramps at Phase 1 and Phase 3.

• Phase 1 — Discovery and value mapping (2-4 weeks). Executive workshops, use-case portfolio scored by feasibility x value, ROI model, executive readout. Output is engineering-testable: every recommended use case includes a build spec, data-source list, and acceptance criteria before Phase 2 begins.
• Phase 2 — Data and platform readiness (3-6 weeks). This is where you address the 60% of AI projects Gartner projects will be abandoned through 2026 for lack of AI-ready data. Deliverables: source inventory, lineage map, quality scorecard, access model, golden evaluation set of 100+ labelled examples per use case, target platform architecture.
• Phase 3 — Solution design and EU AI Act risk classification (2-3 weeks). Solution architecture, EU AI Act Article 6-7 risk determination, security review, and an ROI-adjusted business case that either greenlights build or triggers a phase-gate exit. Roughly one in eight Alice Labs engagements pauses at this gate — sometimes the right answer is not to build.
• Phase 4 — Build, integration, and human-in-the-loop testing (6-16 weeks). Shipped software in the client's cloud, test harness, monitoring dashboards, runbooks. Change-management leads embed here — they do not wait for Phase 5.
• Phase 5 — Deployment, change management, and workforce training. Role-based enablement curriculum built on the actual shipped workflow, champion network, adoption dashboard, cutover runbook.
• Phase 6 — Post-launch operations. Quarterly optimization reviews, model-drift monitoring, updated EU AI Act risk register, and measured KPI deltas against the Phase-1 targets. This is where 15-25% of fees are tied to outcomes.

Timelines above are for a single high-value use case. Multi-use-case portfolios run as parallel Phase 4-6 tracks over a shared Phase 2 platform layer. For deeper phase content on the strategy front end, see our AI strategy consulting guide; for the implementation side, our AI implementation consulting guide covers Phase 3-5 in detail.

Why Strategy Without Implementation Is Dead Slides

In short

Traditional strategy firms deliver roadmaps then exit. Approximately 60% of AI roadmaps are never implemented as scoped, and McKinsey's own 2026 State of AI Trust report names the scaling gap as the industry's core problem. Strategy that no team can build is not strategy — it is theatre. In an end-to-end model, the same senior team that scopes the use case ships it, which eliminates the translation loss between deck and code.

Attacking the strategy-only model requires no exaggeration — the numbers do the work. An estimated 60% of AI roadmaps produced by pure strategy firms are never implemented as scoped. Some pivot mid-flight, some are abandoned, some ship as watered-down versions of what the deck promised. In every case, the strategy fee has already been paid and the outcome has not been delivered.

McKinsey's own 2026 State of AI Trust report acknowledges the problem in plain terms. The scaling gap — the distance between AI pilots that show value in isolation and AI capabilities that show value at enterprise scale — is named as the industry's core challenge for the agentic era. When the strategy firm itself flags the scaling gap as structural, the argument for hiring only a strategy firm gets harder to defend.

The mechanism of failure is boring. A strategy team writes a deck. The deck arrives at an implementation team six months later with different personnel, different context, and often a different sponsor. Every implementation session begins with two hours of re-briefing. Ambiguities in the deck get resolved by the implementation team's best guess. By the time the first prototype ships, it addresses a different problem than the deck described — and by then the strategy team is long gone.

Alice Labs discovery outputs are engineering-testable by design. Every use case in a Phase 1 deliverable includes: a build spec, a data-source list, acceptance criteria, and a golden evaluation set. The reason is not process purity — it is that the same senior engineers who write Phase 1 will be shipping Phase 4. There is no throw-over-the-wall handoff to translate.

The pattern is unremarkable when described. It is nearly impossible for a Big 4 pyramid model to execute, because the person selling the strategy work is not the person available to build. For a comparative view against the pure-strategy shops see our best AI strategy firms 2026 comparison.

Why Implementation Without Strategy Ships the Wrong Thing

In short

Gartner's 2026 CFO survey found 45% of CFOs report AI spend is going toward the wrong outcomes; 57% of failed AI projects cited 'expecting too much, too fast' as the primary cause. Build-first vendors optimise for hours billed, not KPI shift. End-to-end consulting enforces a value hypothesis in Phase 1 that build in Phase 4 must serve. Shipping fast without a hypothesis just industrialises the wrong bet.

The mirror problem to strategy-without-implementation is implementation-without-strategy. A build shop with no strategic ownership will ship exactly what you specify, faster than you thought possible — and it will be the wrong thing, because you specified it in the absence of a validated value hypothesis.

Gartner's 2026 finance leadership survey reported that 45% of CFOs believe their AI spend is going toward the wrong outcomes. The AI platforms market hit $64B in 2026, and roughly half of the finance function directly responsible for that spend does not believe the outputs justify the inputs. That is a demand-side problem, not a supply-side one — the technology is fine; the use-case selection is broken.

The Gartner April 2026 I&O survey (n=782) drilled deeper. 57% of organisations reporting AI failure named "expecting too much, too fast" as the primary cause — meaning the KPI was misspecified in the first place, and no amount of excellent implementation could rescue it. Build vendors have no incentive to catch this. Their revenue is billable hours; a bad KPI ships as many billable hours as a good one.

End-to-end consulting is structurally protected against this failure mode. When the same team owns Phase 1 (value hypothesis) and Phase 4 (build), the team suffers the pain of a bad KPI directly. Their Phase 6 fee is at risk. This is why Alice Labs ties 15-25% of engagement fee to Phase-6 measured outcomes — it makes ambient over-scoping in Phase 1 an anti-incentive.

The observation compounds: build-only vendors optimise for hours; strategy-only firms optimise for deck acceptance; only end-to-end firms are structurally aligned with the client's actual outcome. Everyone else is optimising for a proxy.

Training Is Not a Post-Launch Add-On: The 80/20 Rule of Adoption

In short

Roughly 90% of AI usage failures trace to change management, not technology. Technology accounts for approximately 20% of initiative success; the remaining 80% is redesigning how work gets done. Training belongs inside the engagement, not after it — Alice Labs embeds enablement leads inside the build team from Week 1 so training reflects the actual shipped workflow, not a generic curriculum written against yesterday's mockups.

The training-as-afterthought pattern is the most common single reason we see enterprise AI go live and then quietly stop being used within 90 days. The pattern: strategy and build finish, someone remembers training, a separate vendor is engaged, they write a curriculum against a version of the system that is already two sprints out of date, and the training class produces polite nods with no behavioural change.

The literature has been consistent for a decade: 90% of AI usage failures trace to change management, not tech. Technology accounts for roughly 20% of initiative success in enterprise transformation; the remaining 80% is the work of redesigning how work gets done and equipping the people who will do it. AI initiatives are not exempt from this — if anything, they amplify it, because the change is more invasive than a typical software rollout.

End-to-end AI consulting treats enablement as a Phase 1 workstream, not a Phase 5 deliverable. Alice Labs embeds enablement leads inside the build team from the first sprint. They observe the actual workflow being shipped, they write the training against real screens and real prompts, and they iterate the curriculum as the product iterates. By the time Phase 5 lands, the training material describes the exact system the end users will touch — not a generic AI literacy curriculum, not a mockup.

The compounding effect: because the enablement leads are inside the build team, they also feed usability observations back into build. This closes a loop that training-as-vendor arrangements simply cannot close — the trainer has no channel back to engineering, so friction identified in a training session becomes a JIRA ticket that sits in a backlog for a year.

For deeper coverage of the enablement side of transformation, our AI automation consulting guide covers the workflow-embedded delivery model in detail.

EU AI Act: Why Compliance Must Live Inside the Build, Not Bolted On

In short

August 2, 2026 marked binding enforcement of Articles 9-17 (provider obligations), Article 26 (deployer obligations), and Article 50 (transparency) for high-risk AI systems, with fines up to 7% of global turnover or EUR 35M. As of Q2 2026, 78% of organisations had not taken meaningful compliance steps. Retrofitting compliance onto an already-built model typically costs 3-5x more than compliance-native design. End-to-end firms bake risk classification into Phase 3 and audit hooks into Phase 4.

August 2, 2026 has now passed. That date matters because it moved the EU AI Act from legislative artefact to actively enforceable law for high-risk systems. Articles 9-17 (provider obligations), Article 26 (deployer obligations), and the Article 50 transparency obligations are now under active enforcement across all 27 member states. The regulatory calendar is no longer hypothetical.

The penalty structure is punitive. Maximum fines run to 7% of global annual turnover or EUR 35M, whichever is higher — a materially larger ceiling than GDPR, which capped at 4% or EUR 20M. For a mid-sized European enterprise, that puts individual non-compliance events in eight-figure fine territory before any legal costs or reputational damage.

The compliance-readiness statistics are alarming. As of Q2 2026, an estimated 78% of organisations had not taken meaningful compliance steps. Many of these organisations already have high-risk AI systems in production. The catch-up cost of retrofitting compliance onto a shipped model typically runs 3-5x the cost of designing it compliance-native from Phase 3 — because retrofitting means re-testing, re-documenting, re-approving, and in many cases re-architecting.

End-to-end firms bake risk classification into Phase 3 and audit hooks into Phase 4 as table stakes. Alice Labs is EU AI Act-native, meaning: risk determination under Article 6-7 happens in Phase 3 before build starts; conformity assessments, technical documentation, and CE marking (where required) are Phase 4 deliverables, not Phase 6 remediation; and the same team responsible for the build is responsible for the compliance evidence.

For a full working checklist see our EU AI Act compliance checklist 2026. Always consult qualified legal counsel for compliance determinations specific to your jurisdiction and system.

Data Readiness Is Phase Two, Not an Afterthought

In short

Gartner projects 60% of AI projects without AI-ready data will be abandoned through 2026. Typical readiness gaps include fragmented data ownership, missing lineage, no evaluation dataset, and no golden set for the target use case. An end-to-end engagement dedicates 3-6 weeks in Phase 2 to closing these gaps before build begins — output includes a source inventory, quality scorecard, access model, and evaluation set with 100+ labelled examples per use case.

The single-most-cited external cause of AI project failure is data. Gartner projects that 60% of AI projects launched without AI-ready data will be abandoned through 2026. This is not a moral judgement on the projects — the abandonment is often the correct decision. But it happens after budget has been spent and momentum lost, which is the expensive way to arrive at the right answer.

The gaps are consistent across enterprises: fragmented data ownership across three or four business units with no single data owner; missing lineage — nobody knows which upstream system feeds which downstream aggregate; no evaluation dataset — teams cannot measure whether a model is good because they never assembled a labelled test set; and crucially, no golden set for the target use case, meaning any accuracy claim is anecdotal.

An end-to-end engagement addresses these in Phase 2, before build. Alice Labs' Phase 2 deliverables:

• Source inventory — every system, every field, every access pattern relevant to the target use case, mapped to a data-product owner.
• Quality scorecard — completeness, accuracy, timeliness, consistency, and uniqueness scored per field, per source.
• Access model — how the model will read the data in production, including latency SLAs, retention windows, and PII scrubbing rules.
• Golden evaluation set — a minimum of 100 labelled examples per use case, curated with domain experts, that becomes the acceptance test for Phase 4.
• Gap-fill plan — where the data is not ready, an explicit remediation scope. Where the gap is unbridgeable inside budget, an honest recommendation to pick a different use case.

That last bullet is the trust-building move. Sometimes the answer at end of Phase 2 is "this use case is not ready; here is a smaller one that is." A build-shop with no strategic ownership will build the original spec anyway. An end-to-end firm exits at Phase 3 gate — a small revenue hit now beats a large abandonment later.

The Success Metric Discipline: Define It Before You Build It

In short

Projects with quantified success metrics defined upfront show a 54% success rate; projects without show 12%. An end-to-end contract fixes the target KPI per use case in Phase 1 — cycle-time -40%, deflection +25pts, revenue-per-rep +8%, whatever the case demands — and Phase 6 measures the delta quarterly, not on vibes. This discipline is easier to enforce when one firm owns the whole arc; it is nearly impossible across four vendors.

Folio3's industry aggregate on AI project failure rates puts the number in the plainest possible form: projects with quantified success metrics defined upfront show a 54% success rate; projects without show 12%. Four-and-a-half-times better odds for the discipline of writing down what success means before you start building.

The reason it does not happen at 100% adoption is organisational. Writing a quantified KPI is a commitment device — it means someone must be accountable for hitting it, and it means the project can be judged against it. Ambient goals ("improve customer experience," "accelerate transformation") protect everyone involved from being embarrassed. Quantified KPIs put reputations on the line.

End-to-end AI consulting removes the escape hatch. The Alice Labs contract format fixes a target metric per use case in Phase 1, agreed by the client business sponsor and Alice Labs delivery lead in writing. Example KPI shapes:

• Customer support triage agent: cycle-time -40% for tier-1 tickets within 90 days of go-live, measured against a 30-day pre-launch baseline.
• Sales copilot: revenue-per-rep +8% in the first full quarter post-training, controlled for headcount and pipeline changes.
• Contract review agent: deflection rate +25 percentage points — percentage of contracts that never touch a paralegal, measured monthly.

Phase 6 reviews measure the delta quarterly, not on vibes. Where the KPI is missed, the fee-at-risk portion (15-25%) is not paid; where the KPI is beaten, the delivery team is materially incentivised. This is impossible to enforce across four vendors — each vendor claims their piece hit spec, everyone points at the seams for the aggregate failure.

Human-in-the-Loop by Default: How Senior-Only Delivery Changes the Math

In short

Big 4 pyramid staffing is 1 partner : 3 managers : 15 juniors — the person you thought was doing the work is not. Alice Labs runs senior-only delivery: no juniors, no offshore handoffs, workflow-embedded consultants who sit with end users during build. On 100+ engagements since 2023, this pattern has produced production-ship rates materially above the industry baseline reported by Gartner and ISG.

The Big 4 pyramid staffing model is well-documented and largely misunderstood by buyers. A typical Big 4 AI engagement structure: 1 partner (who sold the work), 3 managers (who run the workstreams), and 15 juniors (who do the actual work). The partner appears at kickoff and readouts. The senior consultant you met in pitch appears in one status call per week. Everyone else on the delivery team is 12-24 months out of undergraduate.

This is not a problem in commodity engagements. It is a problem in AI, because AI engagements need judgement calls at every layer — which use case to prioritise, when data is good enough, whether a retrieval pattern will hold under adversarial input, how to trade off latency against accuracy. Junior consultants cannot make these calls reliably. Managers cannot make them at the frequency the work demands. Partners are not on the project enough to make them at all.

Alice Labs runs the opposite model:

• Senior-only — every engagement is staffed with senior engineers and applied scientists. No juniors, no offshore build-out, no pyramid.
• Workflow-embedded — consultants sit with end users during build, not just at kickoff and readout. This is where usability friction gets caught in time to fix, not after go-live.
• Founders client-facing — Eric Lundberg and Linus Ingemarsson remain on the engagement, not just in the pitch. The MSA binds named individuals.

The economics work because senior operators ship 3-5x faster on knowledge work than junior operators do, and the compounding on getting Phase 3 decisions right saves 2-3x the cost of Phase 4-5 rework. The billing rate is higher; the total invoice is lower; the outcome ships.

On 100+ engagements since 2023, this pattern has produced production-ship rates materially above the Gartner and ISG baselines cited earlier in this article. For the broader boutique-vs-Big-4 argument see our boutique AI consultancies vs Big 4 comparison.

What Deliverables Look Like Across the Full Lifecycle

In short

Phase 1: value map, use-case portfolio, ROI model, executive readout. Phase 2: data source inventory, evaluation dataset, quality scorecard, platform architecture. Phase 3: solution design doc, EU AI Act risk classification, security review, ROI-adjusted business case. Phase 4: shipped software in client cloud, test harness, monitoring dashboards, runbooks. Phase 5: enablement curriculum, role-based training, champion network, adoption dashboard. Phase 6: quarterly optimization review, model drift monitoring, updated risk register.

A concrete artefact list per phase is what separates the RFP that filters real end-to-end firms from the RFP that gets marketing responses. Below is the Alice Labs standard deliverable set — you should be able to demand equivalent artefacts from any firm claiming end-to-end delivery.

End-to-end AI engagement deliverables by phase

Every deliverable has a template and an acceptance criterion. The Phase 6 KPI delta report is the artefact that closes the loop with Phase 1 — the same team that committed the KPI is the team that reports against it, without translation loss.

When End-to-End Is Wrong: Three Scenarios to Buy Point Solutions Instead

In short

End-to-end is not the right buy for everyone. Three scenarios where you should not buy it: (1) you have a mature in-house ML platform team — you need targeted expertise, not full-stack delivery; (2) you are testing a single narrow use case under $150K total contract value — a specialised build shop is cheaper; (3) your regulatory posture is already solved by an existing vendor stack — bringing compliance work in scope creates redundancy.

This section exists because the honest self-negation matters. Not every buyer needs end-to-end AI consulting, and firms that pretend otherwise are optimising for their revenue at the expense of your fit. Three scenarios where you should buy something else.

Scenario 1 — you have a mature in-house ML platform team. If your organisation has a functional MLOps team, a data platform that meets AI workload requirements, and product engineers who ship AI features regularly, an end-to-end engagement duplicates capability you already have. What you probably need is targeted senior expertise on specific gaps — LLM prompt engineering, agent architecture, EU AI Act risk classification — bought as short engagements or advisory retainers. Alice Labs does this format too, but it is not end-to-end and should not be sold as such.

Scenario 2 — you are testing a single narrow use case under $150K total contract value. At small TCV, the fixed overhead of a six-phase MSA (kickoff workshops, MSA negotiation, governance ceremony) is a materially large fraction of the budget. A specialised build shop with a narrow product focus — say, a customer support agent vendor — can ship faster and cheaper for a small pilot. If the pilot succeeds and you want to scale, that is the point at which end-to-end becomes the right vehicle.

Scenario 3 — your regulatory posture is already solved by an existing vendor stack. If you have an established EU AI Act compliance function, a working model-risk-management framework, and vendor-supplied audit tooling, bringing compliance into an end-to-end engagement creates redundancy and coordination overhead without adding safety. In this case, buy build capacity and let your existing compliance function absorb the deliverables.

The honest rule: end-to-end is right when the client organisation cannot credibly own more than one or two of the six phases in-house. If you can staff four or more phases with existing capability, buy point solutions for the rest and keep the coordination work inside the client organisation, where the accountability naturally lives.

How to Contract an End-to-End Engagement (Terms That Actually Protect You)

In short

Five contract terms that separate real end-to-end firms from bundled resellers: (1) single MSA covering all phases with no undisclosed subcontracting; (2) phase-gate exits after Phase 1 and Phase 3 with no penalty; (3) KPI-linked pricing tying 15-25% of fee to Phase 6 measured outcomes; (4) named senior team in the MSA with substitution requiring client approval; (5) IP terms assigning all trained models, prompts, evaluation sets, and code to the client on delivery.

Procurement is where end-to-end engagements are won or lost before Phase 1 begins. The following five terms are the minimum a real end-to-end MSA should contain. Firms that resist any of them are protecting themselves at your expense.

• Single MSA across all phases. One master agreement, one statement of work per phase. Reject arrangements where the strategy work sits under one MSA and the build work sits under a separately-negotiated MSA with a partner. Reject subcontracting to third-party build shops without written disclosure.
• Phase-gate exits at Phase 1 and Phase 3, no penalty. You must be able to stop after discovery or after design without paying for phases you did not consume. This is the term that turns end-to-end from a scary commitment into a risk-managed one.
• KPI-linked pricing. A minimum of 15-25% of total fee tied to Phase 6 measured outcomes, with the KPI written into the SOW. This is the term that aligns delivery incentives with client outcomes — without it, end-to-end has the same optimise-for-hours failure mode as any staff-augmentation contract.
• Named senior team. The specific engineers, applied scientists, and delivery lead named in the MSA. Substitution requires client approval. This is the clause that prevents the classic bait-and-switch where the person you met in pitch is replaced by a junior team.
• IP terms assigning outputs to client. All trained models, prompts, evaluation sets, hooks, and code assigned to the client on delivery. Reject "background IP" carve-outs that would leave a firm owning the model your data trained. Retain rights are fine; ownership is not negotiable.

For a full request-for-proposal template that operationalises these clauses see our AI consulting RFP template. For the broader pricing landscape by scope and geography see our AI consulting pricing guide 2026 and AI consulting rates 2026.

Alice Labs' End-to-End Model in One Diagram

In short

Alice Labs delivers end-to-end AI consulting from Stockholm with international reach. Senior-only staffing, EU AI Act-native design, 100+ production AI implementations since 2023. Founders Eric Lundberg and Linus Ingemarsson remain client-facing on every engagement. Fixed-scope proposals, transparent day rates, phase-gate exits, KPI-linked pricing. Book a discovery workshop to receive a Phase-1 value map within three weeks.

The Alice Labs delivery model, distilled:

• Stockholm-headquartered, international reach. Delivered across the Nordics and broader Europe, with EU AI Act as first-class scope on every high-risk engagement.
• 100+ production AI implementations since 2023. Not pilots — shipped systems in customer production environments across strategy, build, compliance, and enablement.
• Senior-only delivery. No offshore juniors, no pyramid staffing. The engineers, applied scientists, and consultants named in the MSA are the ones who show up.
• Founders client-facing on every engagement. Eric Lundberg (Co-Founder, AI strategy) and Linus Ingemarsson (Co-Founder, engineering) remain embedded, not just in pitch.
• Fixed-scope proposals, transparent day rates. No back-loaded scope creep, no billable-hours dependency on ambiguous instructions.
• KPI-linked pricing. 15-25% of fee tied to Phase-6 measured outcomes, with the KPI contracted in Phase 1.
• Phase-gate exits at Phase 1 and Phase 3. Stop after discovery or after design with no penalty.

The next step is a Phase 1 discovery workshop — 2-4 weeks, ending with an engineering-testable use-case portfolio and an ROI model. Delivered by the same senior team that would run Phase 4 build if you choose to proceed.

For related reading on the enterprise side see our enterprise AI consulting guide; for the Nordic-specific view see AI consulting Nordics and AI consulting Stockholm. For the decision between hiring an external partner versus building in-house see our AI consulting vs in-house AI comparison.