AI Consulting Phoenix: Mid-Market AI Consultants Who Build, Not Brief
Phoenix AI Solutions delivers expert AI consulting from discovery to deployment. We guide mid-market businesses ($1M-$100M) through AI adoption with measurable outcomes at every stage. 60-90 day implementations.
What is AI Consulting?
AI consulting is a professional service that guides businesses through AI adoption from discovery to deployment, covering strategy development, vendor evaluation, implementation planning, and ongoing optimization. Unlike pure strategy consulting (which stops at recommendations) or custom development (which assumes you already know what to build), AI consulting provides end-to-end guidance for businesses navigating AI adoption for the first time.
AI consulting for mid-market businesses ($1M-$100M revenue) differs from enterprise consulting in three critical ways: (1) Timeline: 60-90 day implementations vs 12-24 month enterprise transformation programs. (2) Budget: Focused engagements vs $500K+ enterprise consulting fees. (3) Approach: Focused single-use-case deployments (lead scoring, sales automation) vs enterprise-wide transformation. Mid-market AI consulting recognizes that mid-market businesses need faster time-to-value, smaller upfront investments, and implementations that work with existing teams (5-50 employees) rather than requiring dedicated AI centers of excellence.
Best AI consultants UK share five characteristics: (1) Mid-market specialization with proven track record serving $1M-$100M revenue businesses. (2) Fixed-price or time-capped engagements (not open-ended T&M). (3) UK regulatory expertise including GDPR compliance, ICO AI guidance, and UK data residency requirements. (4) Implementation focus delivering working code and systems, not just strategy PowerPoints. (5) Transparent, scoped engagements covering discovery through first deployment.
Founder & CEO, Phoenix AI Solutions
You've read the case studies. You've sat through the vendor pitches. Everyone says AI will transform your business — but nobody tells you how, or where to start, or what it will actually cost. You need an AI consultant who understands your constraints, not just the technology.
Most AI consultancies hand you a 200-page strategy document and disappear. We don't work that way. Our consulting engagements end with something you can actually build — this quarter, with your team, at your budget.
The 7 Phases of AI Consulting
Every AI implementation follows these phases. We guide you through each one with clarity, speed, and zero fluff.
1. Discovery & Assessment
<a href="/about" class="text-phoenix hover:underline">Phoenix AI company</a> provides AI readiness evaluation, data maturity assessment, current state operational audit, and quick wins identification. We start by understanding where you are, not where we think you should be.
2. Strategy & Roadmap Development
Use case prioritisation via ROI × feasibility matrix, phased implementation plan, resource and budget planning, and success metrics definition. Your roadmap will be actionable, not aspirational.
3. Proof of Concept / Pilot
Controlled environment testing, risk mitigation, stakeholder buy-in building, and technical feasibility validation. See it work before committing to full deployment.
4. Solution Design & Architecture
Technical specifications, integration planning, security and compliance design, and scalability considerations. Built for your environment, not a textbook.
5. Implementation & Integration
Build and configure AI solutions, develop data pipelines, integrate with existing systems, and train your team. We build it, your team owns it.
6. Testing & Optimisation
Performance validation, model fine-tuning, user acceptance testing, and edge case handling. Ship when it's ready, not when the contract says so.
7. Deployment & Ongoing Support
Production rollout, monitoring and maintenance, continuous improvement, and change management. Go live with confidence and support that doesn't disappear after launch.
Why Phoenix AI Consultants Are Different
Business-First, Not Tech-First
We don't sell AI for the sake of AI. Every recommendation ties back to revenue, margin, or operational impact — measurable, not aspirational.
ROI × Feasibility Matrix
Not everything that's possible is worth doing. We rank opportunities by the intersection of business impact and implementation reality, so you start where it matters most.
Ship Within Weeks, Not Quarters
Our consulting engagements include a scoped first project you can start this quarter. You'll see working output before the strategy deck goes cold.
Illustrative AI Consulting Engagements
AI consulting delivers measurable outcomes when combined with hands-on implementation support. Here are three illustrative scenarios — not specific clients — showing how we'd work with companies like these and the results you could expect.
Illustrative scenario — not a specific client.
The Challenge
CEO knew "we should use AI" but had no idea where to start. Marketing wanted AI content tools. Sales wanted AI prospecting. Product wanted AI features. No budget or plan to do all three. Previous consultant delivered 120-page strategy deck that sat unused. Needed actionable guidance, not more research.
The Solution
Full-cycle AI consulting engagement: a discovery audit would identify ~14 AI opportunities, an ROI × feasibility matrix would prioritize the top 3, the roadmap would define a phased approach, and we'd implement Revenue Engine for sales automation as the first project (roughly 6-week deployment).
Results You Could Expect
- Deployed working AI solution in 8 weeks from engagement start (vs. 18 months with previous consultant)
- Revenue Engine generated $145K additional pipeline in first 90 days
- Sales team saved 16 hours/week on manual prospecting (reallocated to calls)
- Clear 18-month AI roadmap: Year 1 (sales automation), Year 2 (customer success AI), Year 3 (product AI features)
- ROI on consulting fees: 4.2x in first 6 months
Illustrative scenario — not a specific client.
The Challenge
Evaluating 6 AI vendor pitches for document analysis and contract review. Internal team lacked expertise to assess vendor claims, data security, or integration feasibility. Risk of $180K/year contract with vendor that couldn't deliver. Needed expert evaluation before signing.
The Solution
AI vendor evaluation consulting: we'd build an evaluation framework with technical, security, and business criteria, conduct vendor technical due diligence (including Phoenix Shield code review for finalists), negotiate contract terms, and manage POC testing with real data.
Results You Could Expect
- Eliminated 4 vendors due to security risks, data handling concerns, or overstated capabilities
- Negotiated contract improvements with winning vendor: UK data residency, quarterly bias audits, audit access rights
- Avoided $180K/year contract with vendor that failed technical due diligence (codebase quality issues)
- Successful vendor deployment in 12 weeks with zero security incidents
- Saved estimated $65K annually via contract negotiation (vs. vendor list pricing)
Illustrative scenario — not a specific client.
The Challenge
Operations team overwhelmed by manual demand forecasting (Excel-based, 2 FTE dedicated). Frequent stockouts costing $120K/year in rush orders. Overstock tying up $240K in working capital. Knew AI could help but lacked internal expertise to build or buy solution. Needed consulting to define requirements and implementation path.
The Solution
End-to-end AI consulting: an operational audit would map the forecasting process and pain points, a vendor vs. custom build analysis would likely determine a custom build is required for ERP integration, we'd scope a custom AI demand forecasting solution, manage implementation with the Custom AI Solutions team, and deploy the production system.
Results You Could Expect
- Stockout incidents reduced 76% (from 18/quarter to 4/quarter)
- Overstock reduced 34%, freeing $82K in working capital
- Forecasting accuracy improved from 68% to 91%
- 2 FTE reallocated from manual forecasting to strategic planning
- Combined savings: $165K annually from reduced rush orders and optimized inventory
- System ROI achieved in 9.2 months
Phoenix AI Consulting Methodology
Our proprietary end-to-end framework for AI adoption that delivers working solutions in weeks, not quarters. Unlike traditional consulting that stops at strategy, we guide you through deployment and optimization.
Phase 1: Discovery & Assessment (Week 1-2)
Understand where you are, what you need, and what's realistically achievable. Most AI projects fail because this phase is rushed or skipped entirely.
- Operational audit: map current processes, pain points, manual work, inefficiencies
- AI readiness assessment: evaluate data maturity, tech stack, team capabilities
- Opportunity inventory: identify 10-15 AI use cases across departments
- Quick wins analysis: which opportunities can deploy in under 90 days?
- Stakeholder alignment: get buy-in from leadership, IT, operations, compliance
Phase 2: Prioritization & Roadmap (Week 2-3)
Rank opportunities by ROI and feasibility. Build a phased roadmap that starts with quick wins and scales to transformation.
- ROI modeling: estimate revenue impact or cost savings for each opportunity
- Feasibility scoring: assess technical complexity, data readiness, integration requirements
- Risk assessment: identify regulatory, security, and change management risks
- Phased roadmap: Year 1 (quick wins), Year 2 (scale), Year 3 (transformation)
- Budget and resource planning: what to build vs. buy vs. partner
Phase 3: Solution Design & Vendor Selection (Week 3-5)
For each prioritized opportunity, determine build vs. buy. If buying, evaluate vendors. If building, design solution architecture.
- Build vs. buy analysis: when to use off-the-shelf products vs. custom development
- Vendor evaluation: assess AI vendors on capabilities, security, cost, integration
- Technical due diligence: Phoenix Shield code review for vendor finalists
- Solution architecture: design for your environment, not textbook diagrams
- Contract negotiation: data handling, liability, audit rights, exit clauses
Phase 4: Proof of Concept / Pilot (Week 4-8)
Test the solution in controlled environment before full deployment. Validate feasibility, measure results, build stakeholder confidence.
- POC scope definition: narrow enough to complete fast, broad enough to prove value
- Environment setup: isolated test environment with real (or realistic) data
- Success metrics: define what "working" means — quantify it
- Stakeholder demo: show working POC to get feedback and buy-in
- Go/no-go decision: honest assessment of whether to proceed to production
Phase 5: Implementation & Integration (Week 6-16)
Deploy the solution to production. Integrate with existing systems. Train your team. Monitor performance.
- Production deployment: phased rollout with monitoring and rollback plan
- System integration: connect to CRM, ERP, databases, APIs, existing workflows
- Team training: hands-on sessions for users, admins, and technical staff
- Change management: communicate changes, address resistance, build adoption
- Performance monitoring: dashboards, alerts, logging for ongoing visibility
Phase 6: Optimization & Scale (Ongoing)
Monitor results, optimize what's working, fix what's not. Once the first project delivers ROI, expand to next opportunities on the roadmap.
- Performance tracking: measure actual ROI vs. projected ROI
- Model tuning: refine AI models based on production data and user feedback
- Process optimization: adjust workflows and automation based on lessons learned
- Expand to next use case: deploy second and third projects from roadmap
- Quarterly reviews: update roadmap based on results, new opportunities, market changes
In-House AI Exploration vs. Big Consulting vs. Phoenix AI Consulting
Three options for AI adoption guidance. Here's how they compare on speed to deployment, cost, and execution probability.
In-House AI Exploration
Timeline
6-18 months (if completed)
Cost
Looks free, but opportunity cost is massive (delayed deployment, missed revenue, wrong vendor selection)
Deliverables
Fragmented research, conflicting departmental opinions, no unified roadmap, stalled at decision paralysis
Execution Probability
Low. Most in-house AI explorations stall at research phase or produce generic recommendations nobody acts on.
Expertise
Low AI-specific expertise unless you have dedicated AI leadership (which mid-market companies rarely do).
Best For
Large enterprises with dedicated innovation teams, long timelines, and high tolerance for experimentation.
Real Risk
By the time you finish exploring, the market has moved. Delayed deployment costs more than consulting fees. Most projects never ship.
Big Consulting Firm (Big 4, MBB)
Timeline
4-8 months for strategy alone
Cost
Industry estimates put strategy and recommendations in the $100K-$450K+ range, with implementation typically separate and substantially more
Deliverables
200-page deck, high-level roadmap, industry benchmarks, leadership presentation, generic frameworks
Execution Probability
Medium. Roadmaps are often too high-level to execute without follow-on implementation engagement.
Expertise
Strong on industry trends and strategy, weak on your specific operations, tech stack, and execution reality.
Best For
Enterprises with budgets exceeding $650K, board-level buy-in requirements, and appetite for multi-year transformation programs.
Real Risk
Strategy sits on shelf because it's not actionable. Implementation requires different consulting team. Total cost often exceeds $1.5M.
Phoenix AI Consulting
Timeline
4-8 weeks discovery to first deployment
Cost
Tailored to each engagement and scoped to your needs — book a call for a quote (includes strategy, vendor evaluation or custom build scoping, and deployment support)
Deliverables
Actionable roadmap, prioritized opportunities, first project scoped in detail and deployed, ongoing optimization support
Execution Probability
High. Most clients deploy their first AI project within 60 days of engagement start. We build it with you, not just advise.
Expertise
Deep operational focus. We map your actual processes, tech stack, team capabilities, and constraints — not generic frameworks.
Best For
Mid-market companies ($1M-$100M) that need actionable AI guidance and fast deployment, not multi-year transformation plans.
Real Risk
Low. Fast enough to avoid market shifts. Specific enough to execute. Affordable enough to justify even if priorities change.
Phoenix AI Solutions vs Big Four AI Consulting (UK)
Comparing Phoenix AI Solutions with Big Four consultancies (Deloitte, PwC, EY, KPMG) for UK mid-market AI consulting engagements
| Dimension | Phoenix AI Solutions | Big Four Consultancies (UK) |
|---|---|---|
| Target Market | Mid-market businesses ($1M-$100M revenue, 10-500 employees) | Enterprise businesses ($100M+ revenue, 1000+ employees) |
| Engagement Timeline | 60-90 days discovery to first production deployment | 12-24 months for enterprise transformation programs |
| Pricing Model | Fixed-price or time-capped, scoped per engagement | Often open-ended time & materials (industry estimates put typical engagements in the hundreds of thousands and up) |
| Team Structure | 2-3 senior practitioners doing hands-on implementation | 10-20 consultants (partners, managers, analysts, offshore teams) |
| Deliverables | Working AI systems in production + strategy roadmap + team training | 200-page strategy decks + high-level roadmaps (implementation separate) |
| UK Regulatory Expertise | GDPR, ICO AI guidance, UK data residency, UK AI regulation | Global compliance frameworks adapted to UK (varies by practice) |
| Geographic Focus | UK-registered, remote-first (founder based in St. Lucia) + UK-wide service | Global presence with UK offices (London-centric, offshore delivery) |
| Best For | UK mid-market businesses needing fast implementations with measurable ROI | Large enterprises with multi-year transformation budgets and board-level buy-in |
| ROI Timeline | 3-6 months from engagement start to measurable ROI | 18-24 months payback period for enterprise transformation |
When to Choose Phoenix AI Solutions vs Big Four for UK AI Consulting
Choose Phoenix AI Solutions if: You're a UK mid-market business ($1M-$100M revenue) needing 60-90 day AI implementations with transparent, scoped pricing, prefer senior practitioners doing hands-on work (not junior analysts), require GDPR-compliant UK data residency, and want working AI systems (not just strategy decks).
Choose Big Four if: You're $100M+ revenue enterprise with multi-year transformation budget ($500K-$1.5M+), need brand-name consultancy for board/investor approval, operate across 20+ countries requiring global delivery, or work in highly regulated industry (banking, pharma) where Big Four audit relationships provide advantage.
Frequently Asked Questions
What is AI consulting and when do businesses need AI consulting services?
AI consulting is a professional service that guides businesses through AI adoption from discovery to deployment, covering AI readiness assessment, strategy development, vendor evaluation, implementation planning, and ongoing optimization. Businesses need AI consulting when: (1) They recognize AI opportunities but lack internal expertise to assess feasibility, prioritize use cases, or evaluate vendors. (2) They've attempted AI pilots that stalled or failed to deliver ROI and need structured implementation guidance. (3) They're deciding between build vs. buy and need independent technical due diligence on AI vendors. (4) They need to comply with AI regulations (GDPR, UK AI Regulation, EU AI Act) and require governance frameworks. AI consulting delivers three core outcomes: (a) Prioritized AI roadmap with clear ROI projections for each opportunity. (b) Build vs. buy recommendations with vendor evaluation for top use cases. (c) Implementation support through first production deployment to prove value before expanding.
What is AI consulting UK and why choose a UK-registered, Caribbean-based AI consultancy?
AI consulting UK refers to AI consulting services delivered by UK-based consultancies with expertise in UK regulatory environment, data protection requirements, and mid-market business landscape. UK-registered, Caribbean-based AI consultancies offer four advantages over US or offshore consultancies: (1) UK regulatory expertise: Deep understanding of GDPR, ICO AI guidance, UK AI regulation, and UK data residency requirements. US consultancies often lack UK compliance expertise. (2) UK market specialization: Experience serving UK mid-market businesses ($1M-$100M revenue) with UK-specific business structures, procurement processes, and market dynamics. (3) Time zone alignment: Real-time collaboration during UK business hours (9am-5pm GMT) vs overnight responses from offshore teams. (4) Cultural and operational fit: Understanding of UK business culture, communication styles, and regulatory environment. Phoenix AI Solutions is UK-registered, remote-first; our founder is based in St. Lucia, and we specialize in GDPR-compliant AI implementations for UK mid-market businesses. For a comparison of UK AI consulting firms, see our best AI consulting firms UK guide.
What are the best AI consulting firms UK for mid-market businesses?
Best AI consulting firms UK for mid-market businesses share five characteristics: (1) Mid-market specialization: Proven track record serving $1M-$100M revenue businesses, not scaled-down enterprise approaches. Look for case studies with mid-market companies (10-500 employees), not just FTSE 100 enterprises. (2) UK regulatory expertise: GDPR compliance, ICO AI guidance, UK data residency, and UK AI regulation preparation built into every implementation. (3) Fixed-scope engagements: Transparent, scoped engagements for discovery through deployment, not open-ended time & materials contracts. (4) Implementation focus: Deliver working AI systems (code, integrations, production deployment), not just strategy PowerPoints. (5) Fast time-to-value: 60-90 day implementations vs 12-24 month enterprise transformation programs. Red flags: Consultancies that only show Big Four enterprise case studies, refuse to provide fixed-price quotes, or separate strategy from implementation (forcing you to pay twice). Phoenix AI Solutions serves UK mid-market exclusively with 60-90 day implementations and a focus on measurable 90-day outcomes. For independent comparison of UK AI consultancies, read our best AI consulting firms UK 2026 guide.
What is Phoenix AI consulting and when do I need AI consulting services?
Phoenix AI consulting guides mid-market businesses ($1M-$100M revenue) through AI adoption from discovery to deployment in 60-90 days. You need AI consulting when: (1) You know AI could help but don't know where to start or which opportunities to prioritize. (2) You're evaluating AI vendors or building in-house and need expert guidance. (3) You've tried AI pilots that went nowhere and need structured implementation. (4) You lack internal AI expertise to assess feasibility, ROI, and technical requirements. Phoenix AI Solutions provides AI consulting that bridges the gap between AI hype and actual business results, delivering strategy, vendor selection, implementation planning, and ongoing optimization. Unlike enterprise consultancies (Big Four) that require 12-24 month engagements and $500K+ budgets, Phoenix AI consulting delivers focused implementations in 60-90 days. For a detailed vendor selection framework, see our guide on how to choose an AI implementation partner (https://phoenixai.solutions/insights/guides/how-to-choose-ai-implementation-partner).
Why choose Phoenix for AI consulting?
Phoenix AI consulting specializes in mid-market businesses ($1M-$100M revenue) with three key differentiators: (1) Speed: 60-90 day implementations vs 12-24 month enterprise consulting programs. (2) Budget: focused engagements vs $500K+ enterprise consulting fees. (3) Implementation focus: We deliver working AI systems, not just strategy decks. Phoenix AI consultants combine business strategy with hands-on technical implementation, ensuring you deploy measurable AI solutions within 90 days. Our consulting methodology includes operational audit, ROI modeling, vendor evaluation, proof of concept, production deployment, and knowledge transfer. Most clients achieve ROI within 3-6 months of engagement start. For a complete CFO-tested framework on calculating AI consulting ROI, see our AI consulting ROI framework guide.
How long does an AI consulting engagement take?
Timeline depends on engagement scope. Discovery and assessment: 1-2 weeks. Strategy and roadmap development: 3-4 weeks. Proof of concept / pilot: 2-6 weeks depending on complexity. Full implementation: 2-6 months for most projects. Typical end-to-end consulting engagement (discovery through first deployment): 3-5 months. Fast-track engagements for focused use cases can complete in 6-8 weeks. Phoenix accelerates timelines by combining strategy with immediate implementation — most clients deploy their first AI project within 60 days of engagement start.
What does AI consulting cost?
Pricing is tailored to each engagement and scoped to your needs — book a call for a quote. Pricing factors include organizational size, project complexity, and level of hands-on implementation support. Before committing to a consulting engagement, calculate your AI implementation ROI to build a data-driven business case. For detailed mid-market pricing breakdowns and ROI expectations, see our mid-market AI consulting buyer's guide. Contact us for a custom quote.
How is AI consulting different from AI strategy or custom development?
AI consulting is the broadest engagement type, covering discovery through deployment. AI strategy is a subset focused on roadmap development and prioritization (typically 4-6 weeks). Custom AI development is hands-on engineering work building bespoke solutions. Most consulting engagements include strategy as a phase, then move to either vendor selection or custom development. Use AI consulting when you need end-to-end guidance. Use AI strategy when you just need the roadmap. Use custom development when you already know what to build.
What industries do you specialize in for AI consulting?
Phoenix works across industries with focus on mid-market companies ($1M-$100M revenue). Core expertise areas: B2B SaaS (sales automation, product AI), professional services (client delivery, knowledge management), financial services (underwriting, risk assessment, compliance), e-commerce (demand forecasting, personalization, customer service), manufacturing (supply chain optimization, predictive maintenance), healthcare (patient intake, clinical documentation, compliance). We avoid one-size-fits-all frameworks — each industry has unique AI opportunities, regulations, and implementation challenges. For industry-specific implementation guides, see our comprehensive guide on AI for professional services, our detailed AI for consulting firms guide, and our AI for accounting firms implementation roadmap. If your industry isn't listed, we assess fit during discovery.
Will I need to hire AI talent after the consulting engagement?
Depends on your implementation path. If you buy off-the-shelf AI products (e.g., Revenue Engine, Influence), you don't need dedicated AI talent — these are managed solutions. If you build custom AI solutions, you'll eventually need technical team capacity (data engineers, ML engineers, or product managers). However, most Phoenix clients start with consulting + managed solutions and only hire AI talent later when scaling. We provide team readiness assessment during strategy phase so you know hiring requirements upfront. For a detailed cost comparison of consulting vs building an in-house AI team, see our AI consulting vs in-house team guide (https://phoenixai.solutions/insights/guides/ai-consulting-vs-in-house-team-uk-2026). No surprises.
What are AI implementation services and how do they differ from AI consulting?
AI implementation services are hands-on technical services that deploy AI systems to production. While AI consulting provides strategy, roadmap development, and vendor selection guidance, AI implementation services handle the actual build: data pipeline development, model training, system integration, testing, deployment, and team training. Phoenix AI Solutions combines both: our AI consulting engagements include implementation services, not just strategy decks. Typical AI implementation services include: (1) Data preparation and pipeline development. (2) AI model development and training. (3) Integration with existing systems (CRM, ERP, databases). (4) Security and compliance setup (GDPR, data encryption). (5) Production deployment and monitoring. (6) User training and knowledge transfer. Most mid-market AI implementation services deploy in 60-90 days. Enterprise AI implementation services cost $500K+ and take 12-24 months. Phoenix specializes in mid-market AI implementation: faster, more affordable, and focused on measurable ROI within 3-6 months.
Common AI Consulting Engagements
Sales & Marketing Automation
Your sales team is drowning in admin. Marketing can't prove ROI. We audit your funnel, identify automation opportunities, and implement Revenue Engine to connect marketing spend to actual revenue. For an independent framework on choosing the right AI implementation partner, see our complete vendor selection guide.
Codebase Risk Assessment
Before you sign that vendor contract or complete that acquisition, you need to know what's under the hood. Phoenix Shield evaluates code quality, security risks, and technical debt — so you make decisions based on evidence, not demos.
AI Governance & Compliance
AI regulation is moving fast. Whether you need internal usage policies, vendor governance frameworks, or regulatory compliance preparation, our AI Policy service builds the guardrails that let you move fast without getting caught out.
Custom AI Development
Sometimes the problem doesn't fit a product category. A logistics firm needs AI to optimise routes across three countries. A healthcare company needs NLP for patient intake in four languages. When nobody else has solved your problem, we build it via Custom AI Solutions.
AI Implementation Services: From Strategy to Production
Phoenix AI implementation services deliver working AI systems, not just recommendations. Our implementation methodology combines consulting expertise with hands-on technical execution to deploy AI solutions in 60-90 days.
What AI Implementation Services Include
- Data pipeline development and integration
- AI model development, training, and optimization
- System integration with CRM, ERP, existing tools
- Security setup and GDPR compliance
- Production deployment and monitoring
- Team training and knowledge transfer
AI Implementation Services Pricing
Pricing is tailored to each engagement and scoped to your needs — book a call for a quote. Every engagement includes discovery, build, testing, deployment, and team training, with a focus on measurable ROI within 90 days.
Implementation Services vs Strategy-Only Consulting
Many consultancies deliver strategy decks and stop there. Phoenix AI implementation services include strategy as a phase, then move directly to execution: building data pipelines, training models, integrating systems, and deploying to production. You get working AI systems, not PDF recommendations.
Mid-market implementation timeline: Week 1-2 (Discovery), Week 3-4 (Design), Week 5-10 (Build), Week 11-12 (Deploy). Most clients achieve measurable ROI within 3-6 months of engagement start.
Who It's For
Mid-size to enterprise companies exploring AI adoption. CTOs and COOs who need guidance on where to start, what to prioritise, and how to execute without blowing the budget or losing six months to vendor selection. For market context on AI adoption in your segment, read our mid-market AI adoption report. For detailed guidance on the consulting vs in-house decision, see our AI consulting vs in-house team guide.
If you're evaluating AI consultants and every pitch sounds the same — buzzwords, case studies from companies ten times your size, and timelines measured in quarters — talk to us. We speak your language. For an independent comparison of UK AI consulting firms and a comprehensive mid-market AI consulting buyer's guide, see our best AI consulting firms guide.
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