How to Hire an AI Developer in the UK (2026): Costs, Options, and What to Look For

Every business in the UK is now asking some version of the same question: how do we use AI? And the follow-up, almost immediately: who do we hire to build it?

The AI talent market in 2026 is nothing like it was two years ago. The skills that matter have shifted. The roles that existed in 2023 have splintered into new specialisms. The cost of hiring has both risen (for genuine senior talent) and fallen (for commodity implementation work that AI tools can now assist with). And the entire concept of what "hiring" means has changed, because one experienced person with AI tools can now deliver what previously required a team of five to seven.

This guide cuts through the noise. It covers what an AI developer actually does in 2026, what they cost, where to find them, how to evaluate them, and which hiring model makes sense for your business. It is written for UK founders, managing directors, and hiring managers who need practical answers, not recruitment industry jargon.

What Does an AI Developer Actually Do?

The term "AI developer" has become a catch-all that covers at least four distinct roles. Understanding which one you actually need will save you months of wasted recruitment and tens of thousands of pounds in misallocated salary.

ML Engineer (Machine Learning Engineer)

This is the traditional AI role. ML engineers build and train models from scratch. They work with datasets, design neural network architectures, tune hyperparameters, and optimise model performance. They typically hold advanced degrees in mathematics, statistics, or computer science. They write Python, use frameworks like PyTorch and TensorFlow, and spend their days in Jupyter notebooks.

When you need one: You have proprietary data and want to train a custom model that does not exist anywhere else. You are building a product where the AI model itself is the competitive advantage. Think: a medical imaging diagnostic tool, a fraud detection system trained on your transaction data, or a recommendation engine for a marketplace with unique behavioural patterns.

When you do not need one: You want to add AI features to an existing product, build a chatbot, integrate GPT-4 or Claude into your workflow, or create an AI-powered internal tool. Hiring an ML engineer for this is like hiring an engine designer when you need a mechanic.

AI Application Developer

This is the role most UK businesses actually need in 2026. An AI application developer integrates pre-trained foundation models (GPT-4, Claude, Gemini, Llama) into production software. They understand APIs, prompt engineering, retrieval-augmented generation (RAG), agent frameworks, and how to build reliable systems on top of models that are, by nature, probabilistic.

They are software engineers first, AI practitioners second. They know how to handle error states when a model hallucinates, how to structure prompts for consistent output, how to build evaluation pipelines, and how to manage the cost and latency trade-offs between different model providers.

When you need one: Almost always. If your AI project involves using existing models to solve business problems, this is your hire.

Prompt Engineer

A role that barely existed in 2024 and is already becoming less distinct. Prompt engineers specialise in crafting, testing, and optimising the instructions given to language models. In early 2024, this was a standalone discipline. By 2026, prompt engineering has become a core competency that every AI application developer is expected to have, much like how "responsive design" stopped being a specialism and became a baseline web development skill.

When you need a dedicated one: Rarely. Only if you are running a large-scale content operation where prompt quality directly drives revenue and you need someone focused exclusively on optimisation. For most businesses, your AI application developer will handle prompting as part of their broader role.

Full-Stack AI Builder

This is the most valuable and rarest profile in the UK market. A full-stack AI builder combines AI application development with traditional full-stack engineering: front-end (React, Next.js), back-end (Node.js, Python, databases), infrastructure (cloud deployment, CI/CD), and product sense (knowing what to build, not just how to build it).

They can take a business problem, design a solution, build the entire application including the AI components, deploy it to production, and iterate based on user feedback. No handoffs. No coordination overhead. No "that is not my layer" conversations.

When you need one: When you want to move fast, keep costs down, and avoid managing a team of specialists. This profile is particularly well-suited to startups, SMEs, and any business that values shipping speed over organisational complexity.

How Much Does It Cost to Hire an AI Developer in the UK?

AI developer compensation in the UK has settled into clearer bands as the market has matured. The wild speculation of 2023-2024, when companies were offering GBP 200,000+ for anyone who could spell "transformer," has given way to more rational pricing based on actual capability and demonstrated output.

Here are the current market rates as of April 2026:

Full-Time Salaries (Annual, GBP)

Level	Salary Range	Total Cost to Employer	Typical Experience
Junior AI Developer	£45,000 - £65,000	£55,000 - £80,000	0-2 years, bootcamp or CS degree, can implement with guidance
Mid-Level AI Developer	£65,000 - £90,000	£80,000 - £115,000	2-5 years, can design and build independently, some production experience
Senior AI Developer	£90,000 - £130,000	£115,000 - £170,000	5+ years, system design, production-hardened, can lead projects
Lead / Principal	£120,000 - £160,000	£155,000 - £210,000	8+ years, sets technical direction, mentors team, business acumen
ML Engineer (Specialist)	£80,000 - £150,000	£100,000 - £195,000	MSc/PhD, model training, research background

Total cost to employer includes employer National Insurance (13.8% above the threshold), pension contributions (typically 3-5%), benefits, equipment, office space, and recruitment fees. As a rule of thumb, add 25-30% on top of base salary to get the true cost of a full-time hire.

Contractor Day Rates

Specialism	Day Rate (Outside IR35)	Day Rate (Inside IR35)
AI Application Developer (Mid)	£500 - £700	£400 - £550
AI Application Developer (Senior)	£700 - £1,000	£550 - £800
Full-Stack AI Builder	£800 - £1,200	£650 - £950
ML Engineer / Data Scientist	£700 - £1,100	£550 - £850
AI/ML Architect	£1,000 - £1,500	£800 - £1,200

Inside IR35 rates are lower because the contractor bears employment-equivalent tax deductions. For medium and large companies, IR35 status determination is your responsibility since April 2021. More on this in the UK-specific considerations section below.

Alternative Engagement Models

Model	Monthly Cost	What You Get
Fractional AI Partner	£2,500 - £3,000	Senior-level AI capability, ~1 day/week, ongoing strategic relationship
AI Agency (Project)	£5,000 - £20,000	Full team, defined deliverables, project-based engagement
AI Agency (Retainer)	£5,000 - £15,000	Ongoing support, multiple specialists, account management overhead
Offshore AI Team	£3,000 - £8,000	2-4 developers, timezone challenges, management overhead

Full-Time Hire vs Contractor vs Agency vs Fractional: Which Model?

The hiring model matters as much as the individual. Getting this wrong is one of the most expensive mistakes UK businesses make with AI talent.

Full-Time Hire

Best for: Companies with a continuous, long-term AI roadmap spanning 12+ months. You need someone embedded in the team, building institutional knowledge, and iterating on AI features as a core part of the product.

Advantages:

• Deep product and domain knowledge over time
• Available every day, no scheduling friction
• Builds internal AI capability and culture
• Can mentor other team members

Disadvantages:

• 2-4 months to recruit, plus notice period
• High total cost (salary + NI + pension + benefits + equipment + office)
• Risk of hiring the wrong person (recruitment fees, severance, lost time)
• AI moves fast; skills can become stale if the person does not continuously upskill
• Difficult to scale down if priorities change

Contractor

Best for: Defined projects with clear deliverables and a known end date. Building an MVP, migrating to a new AI provider, or implementing a specific AI feature that does not require ongoing development.

Advantages:

• Can start within 1-2 weeks
• No long-term commitment
• Access to specialist skills for a defined period
• Easy to scale up or down

Disadvantages:

• Expensive on a per-day basis (GBP 500-1,200/day)
• IR35 complexity for medium and large businesses
• No long-term continuity; knowledge walks out the door
• Availability can be unpredictable (good contractors are always in demand)
• Less invested in your business outcomes

Agency

Best for: Businesses that need a full team (design, development, QA, project management) for a complex build, and do not have the internal capability to manage individual specialists.

Advantages:

• Full team, managed for you
• Broader skill set than a single hire
• Established processes and delivery track record
• Shared risk on delivery

Disadvantages:

• Expensive: GBP 5,000-20,000 per month or GBP 30,000-80,000+ per project
• You are paying for account managers, project managers, and overhead
• The senior person who pitched is rarely the person who builds
• Agency priorities may not align with yours (utilisation targets, upselling)
• Knowledge stays with the agency, not your business

Fractional AI Partner

Best for: SMEs and founder-led businesses that need senior AI capability without the overhead of a full-time hire. You want someone who thinks about your business regularly, knows the codebase, and can both strategise and execute.

Advantages:

• Senior-level capability at a fraction of full-time cost
• Continuity: same person, building on previous work each month
• Strategic input alongside hands-on delivery
• No recruitment fees, no notice periods, no employer NI
• Can start within days

Disadvantages:

• Limited hours per week (typically 1 day/week at the GBP 2,500-3,000/month level)
• Not suitable if you need full-time, daily availability
• Single point of dependency (mitigated by documentation and code quality)

Comparison Summary

Factor	Full-Time	Contractor	Agency	Fractional
Monthly cost	£9,500-14,000	£10,000-24,000	£5,000-20,000	£2,500-3,000
Time to start	2-4 months	1-2 weeks	2-4 weeks	1 week
Continuity	High	Low	Medium	High
Strategic input	Varies	Rare	Varies	Included
Risk if wrong	High (severance)	Low (end contract)	Medium (sunk cost)	Low (monthly)
Best for	12+ month roadmap	Defined projects	Complex builds	Ongoing, strategic

What Skills Should I Look For in 2026?

The AI skills landscape has shifted dramatically. Two years ago, recruiters were filtering for TensorFlow certifications and PhD qualifications. Today, the most valuable skills are practical, product-oriented, and focused on building with existing models rather than creating new ones.

Essential Skills (Non-Negotiable)

• LLM API integration: Hands-on experience with OpenAI, Anthropic (Claude), and Google (Gemini) APIs. Understanding of token economics, rate limiting, streaming responses, and model selection trade-offs.
• Prompt engineering: Not just writing prompts, but designing prompt architectures: system prompts, few-shot examples, chain-of-thought reasoning, output formatting, and evaluation frameworks to measure prompt quality.
• RAG (Retrieval-Augmented Generation): Building systems that ground LLM responses in your organisation's actual data. This includes vector databases (Pinecone, Weaviate, pgvector), embedding models, chunking strategies, and retrieval pipeline design.
• Full-stack development: TypeScript or Python, React or Next.js, databases (PostgreSQL, Supabase), API design, authentication, and deployment. AI features do not exist in isolation; they need a production application around them.
• Production engineering: Error handling for probabilistic outputs, fallback strategies, cost monitoring, latency optimisation, and logging/observability for AI systems.

Highly Valuable Skills

• Agent frameworks: Experience building AI agents that can use tools, make decisions, and execute multi-step workflows. Frameworks include LangChain, LangGraph, CrewAI, and custom agent architectures.
• Voice AI: Building conversational voice agents using platforms like Gemini Live API, ElevenLabs, LiveKit, or Twilio. Real-time audio processing, turn-taking, and telephony integration.
• Fine-tuning: Adapting base models to specific domains using techniques like LoRA, QLoRA, and RLHF. Less common than API integration but valuable for businesses with unique data.
• Evaluation and testing: Building systematic evaluation pipelines for AI outputs. LLM-as-judge patterns, regression testing for prompts, and A/B testing frameworks for AI features.
• MCP (Model Context Protocol): The emerging standard for connecting AI models to external tools and data sources. Understanding MCP servers, tool registration, and agent-to-agent communication.

Nice-to-Have Skills

• Computer vision: Image analysis, OCR, document processing using models like GPT-4V or Claude Vision.
• MLOps: Model deployment pipelines, A/B testing infrastructure, model versioning, and monitoring.
• Data engineering: ETL pipelines, data cleaning, and preparation for AI training or RAG systems.
• Domain expertise: Understanding of your specific industry (healthcare, legal, finance, property) combined with AI knowledge.

Where Do I Find AI Developers in the UK?

The best AI developers are not sitting on job boards refreshing Indeed. Finding genuine talent requires a more targeted approach.

LinkedIn

Still the most effective channel for UK AI talent, particularly for full-time and senior roles. The key is specificity. Do not post "AI Developer Wanted" and wait. Search for people who have built the type of thing you need. Look at their posts, their projects, their GitHub profiles. Direct outreach from a founder or hiring manager converts far better than a recruiter InMail.

Search terms that actually work: "LLM integration," "RAG pipeline," "AI agent," "Gemini API," "Claude API," combined with "UK" or specific cities. Filter for people who are posting about their work, not just listing skills on their profile.

Specialist Platforms

• Toptal: Pre-vetted freelancers, higher rates (GBP 700-1,500/day) but consistent quality. Good for senior contractor engagements.
• Gun.io: Similar to Toptal, strong vetting process, skewed towards US but with growing UK presence.
• Turing: AI-focused talent platform with a large pool of developers. Quality varies; their matching algorithm has improved significantly since 2024.
• Arc.dev: Remote developer marketplace with AI specialisation filters. Good for finding developers who work across time zones.

AI Studios and Agencies

For fractional partnerships and project work, AI-focused studios offer a middle ground between hiring someone full-time and using a traditional digital agency. Look for studios that build AI-native products (not agencies that have added "AI" to their service page). Ask to see live products they have built, not pitch decks.

Referrals and Network

The highest-quality AI talent in the UK consistently comes through referrals. Ask your existing technical team, advisors, and peers. Attend AI meetups in London, Manchester, Edinburgh, and Bristol. The London AI community (meetups, conferences, Slack/Discord groups) is particularly active. AI Camp, London AI, and the various LangChain community events are good starting points.

University Pipelines

For junior roles, UK universities with strong AI programmes include Imperial College London, UCL, University of Edinburgh, University of Cambridge, University of Oxford, and University of Manchester. Their career services can connect you with recent graduates. Note: academic AI skills (research, papers) differ significantly from production AI skills (shipping, scaling). Budget for 6-12 months of on-the-job learning.

Where Not to Look

General job boards (Indeed, Reed, Totaljobs) will flood you with unqualified applications. Recruitment agencies that "also do AI" but primarily place Java and .NET developers will waste your time. Fiverr and Upwork can work for very small, well-defined tasks, but the quality variance is extreme for anything beyond basic prompt writing.

How to Evaluate AI Talent

Traditional hiring processes are poorly suited to evaluating AI developers. A standard technical interview that tests algorithmic knowledge and whiteboard coding tells you almost nothing about whether someone can build a reliable AI product.

Portfolio Over CV

The single most important evaluation criterion: show me what you have shipped. Not what you have researched, not what you have studied, not what you have prototyped in a notebook. What is running in production, serving real users, and generating real value?

Ask candidates to walk you through a shipped AI product. What were the requirements? What models did they evaluate? Why did they choose the architecture they chose? What broke in production that they did not anticipate? How do they handle hallucinations? What does their evaluation pipeline look like?

A candidate who can answer these questions from experience is worth more than a candidate with a PhD from a top university who has only published papers.

Paid Trial Project

The most reliable evaluation method for AI talent is a paid trial project. Give the candidate a small, real problem (2-3 days of work), pay them their standard rate, and evaluate the output. This reveals:

• How they approach ambiguous problems
• Whether they ask the right clarifying questions
• Code quality, documentation, and communication
• How they handle the AI-specific challenges (prompt design, error handling, evaluation)
• Whether they ship something that actually works, or something that only works in a demo

The cost of a paid trial (GBP 1,500-3,500) is trivial compared to the cost of a bad hire (GBP 30,000-80,000 in recruitment fees, salary, and lost time).

Business Understanding

The best AI developers understand that the technology is a means to a business end. Ask: "If I gave you this business problem, how would you decide whether AI is even the right solution?" A good answer demonstrates that the candidate thinks about ROI, user experience, and feasibility before reaching for the latest model. A bad answer jumps straight to technical implementation without questioning whether the approach makes sense.

What to Test (and What Not To)

Do test:

• System design for AI applications (how would you architect this?)
• Prompt engineering skills (give them a real prompting challenge)
• Production thinking (what happens when the API is down? When the model hallucinates?)
• Cost awareness (what would this cost to run at 10,000 users per day?)
• Communication (can they explain technical decisions to a non-technical stakeholder?)

Do not test:

• Whiteboard algorithms (irrelevant to AI application building)
• Academic knowledge of transformer architecture internals (unless you are training models)
• Memorised framework APIs (every good developer uses documentation)
• Trick questions designed to make candidates feel stupid (this tells you nothing and repels good people)

The AI-Native Builder: Why One Person Can Replace a Team

This is the most important shift in the UK tech hiring market, and most businesses have not caught up to it yet.

In 2023, a typical digital product build required a team:

Role	Days	Day Rate	Cost
Front-end developer	8	£800	£6,400
Back-end developer	10	£900	£9,000
UX designer	4	£750	£3,000
Visual designer	4	£750	£3,000
QA engineer	3	£600	£1,800
Project manager	6	£700	£4,200
Account manager	4	£650	£2,600
Total	39		£30,000

In 2026, one experienced full-stack AI builder with the right tools (Cursor, Claude Code, GitHub Copilot, v0, Figma AI) can deliver that same output in 8-10 days. Not because the work is simpler, but because AI-assisted development has fundamentally changed the economics of software building.

The implications for hiring strategy are significant:

• You need fewer people. One senior AI-native builder can replace a team of five to seven specialists. The coordination overhead, the briefing documents, the handoff meetings, the "can you jump on a quick call" interruptions: all eliminated.
• Seniority matters more than headcount. One person with 15-20 years of experience using AI tools will outperform five juniors every time. The experience is not just about knowing how to code; it is about knowing what to build, what not to build, and where the real complexity sits.
• Speed is the competitive advantage. A project that took 8 weeks with a team now takes 8 days with one person. For businesses in competitive markets, this speed advantage is worth more than any marginal quality improvement from a larger team.
• The cost equation has flipped. Instead of GBP 30,000-80,000 for a project, you are looking at GBP 3,000-15,000 for the same output. This makes AI product development accessible to businesses that could never have afforded a full agency engagement.

This does not mean teams are obsolete. Large-scale systems, products with millions of users, and organisations with complex compliance requirements still benefit from team-based development. But for the vast majority of UK SMEs, startups, and founder-led businesses, the "team of seven" model is an expensive relic.

Red Flags When Hiring AI Talent

The AI talent market is noisy. For every genuinely capable developer, there are dozens who have completed an online course and updated their LinkedIn headline. Here are the warning signs:

Overemphasis on Credentials

A PhD from a top university is impressive. But if the candidate cannot point to a single production system they have built, the PhD tells you they are good at research, not at shipping products. In 2026, the market values delivery over pedigree. Ask: "What have you shipped?" If the answer is only papers, conference talks, and Kaggle competitions, they are not ready for a production AI role.

Cannot Show Shipped Work

This is the biggest red flag. If a candidate claims three years of AI experience but cannot demonstrate a single live product, something is wrong. Either they have only worked on internal tools that never launched, or they have been in "AI strategy" roles that do not involve actually building. Neither is what you need.

No Business Context

Ask why they made a particular technical decision. If every answer is purely technical ("because it had better F1 scores," "because the architecture is more elegant"), without reference to business impact, user experience, or cost, the candidate will build technically impressive systems that do not solve your actual problem.

Only Knows One Framework or Model

The AI landscape changes every few months. A developer who only knows OpenAI, or only uses LangChain, or only works in Python is limiting your options. The best developers are model-agnostic and framework-pragmatic. They choose the right tool for the job, not the one they happen to know.

Overpromises on AI Capabilities

"We can build a fully autonomous agent that handles everything." "AI will replace your entire customer service team." "This will be 99% accurate." These claims signal either ignorance or dishonesty. Genuine AI practitioners talk about trade-offs, limitations, error rates, and the specific conditions under which a system performs well or poorly. They set realistic expectations, not fantasy ones.

No Understanding of Cost and Latency

Every AI API call costs money and takes time. A developer who cannot estimate the running cost of their solution, or who does not consider latency when designing user-facing features, will build systems that are either unaffordable to scale or too slow to use. Ask: "If this handles 10,000 requests per day, what will the API costs be?" A blank stare is a red flag.

Excessive Jargon Without Substance

"We will implement a multi-modal, agentic, RAG-enhanced, fine-tuned, multimodal pipeline with reinforcement learning from human feedback." If a candidate uses every buzzword in a single sentence but cannot explain what problem it solves in plain English, they are performing, not engineering.

UK-Specific Considerations

Hiring AI talent in the UK comes with regulatory and practical considerations that differ from the US and other markets.

IR35

The elephant in every UK contractor conversation. Since April 2021, medium and large businesses (not small businesses, as defined by the Companies Act) are responsible for determining whether a contractor engagement falls inside or outside IR35.

Inside IR35: The contractor is treated as an employee for tax purposes. The client (or agency) deducts PAYE, NI, and pension. The contractor's take-home drops by 20-30%. Day rates for inside-IR35 work are typically lower to account for this.

Outside IR35: The contractor operates through their own limited company, pays corporation tax and dividends, and is responsible for their own tax. Higher take-home for the contractor, but the client carries the risk of an incorrect determination.

Practical advice: Use HMRC's CEST (Check Employment Status for Tax) tool for an initial assessment, but do not rely on it exclusively. CEST has a documented history of producing "unable to determine" results for legitimate contractor engagements. Get specialist IR35 advice for any engagement over GBP 50,000. Many AI contractors now operate through umbrella companies, which simplifies compliance for both parties but reduces the contractor's net income.

If you are a small business (fewer than 50 employees, under GBP 10.2M turnover, under GBP 5.1M balance sheet), the contractor determines their own IR35 status. This significantly simplifies the process.

London vs Rest of UK

London AI developer salaries run 15-30% higher than equivalent roles in Manchester, Bristol, Edinburgh, Leeds, or Birmingham. However, the remote work shift since 2020 has narrowed this gap considerably. Many senior AI developers now live outside London and work remotely, pricing based on skill level rather than postcode.

If you are hiring full-time and insist on office presence, expect to pay the London premium. If you are open to remote or hybrid, the entire UK talent pool opens up at more competitive rates.

Visa Requirements for Overseas Talent

If you cannot find the right UK-based talent (a real possibility for very specialist AI roles), you can sponsor overseas workers through the Skilled Worker Visa. AI developer roles (SOC code 2134, Programmers and Software Development Professionals) are on the eligible occupations list.

Requirements include: a minimum salary of GBP 38,700 (or the going rate for the role, whichever is higher), a valid sponsor licence (application fee GBP 536-1,476), and the ability to demonstrate that the role is genuine. Processing time is typically 3-8 weeks.

For senior AI roles commanding GBP 90,000+, the salary threshold is not an issue. For mid-level roles, the GBP 38,700 minimum is comfortably met. The administrative overhead of sponsorship is the main barrier, not the salary requirements.

UK AI Safety and Regulation

The UK's approach to AI regulation is lighter than the EU AI Act but still evolving. The AI Safety Institute (AISI) was established in 2023, and the government's pro-innovation approach means fewer mandatory compliance requirements compared to Europe. However, existing laws apply: GDPR covers AI processing of personal data, the Equality Act covers algorithmic discrimination, and sector-specific regulators (FCA, ICO, CQC) have their own AI guidance.

Your AI developer should understand the regulatory landscape relevant to your industry. If you operate in healthcare, finance, or legal services, AI compliance is not optional. Look for developers who proactively raise regulatory considerations rather than treating them as an afterthought.

Remote Work and Timezone

The UK sits in a favourable timezone for AI development. UTC/BST overlaps comfortably with European work hours and has several hours of overlap with US East Coast. This makes UK-based AI developers particularly valuable for businesses with international operations or clients.

Remote-first AI development is now the norm in the UK, not the exception. Over 70% of UK AI roles advertised in 2026 offer remote or hybrid working. If you require full-time office presence, you are limiting your talent pool significantly and will pay a premium for it.

Timeline: From Hiring to First Delivery

One of the most underestimated factors in AI hiring is time-to-value. The gap between deciding to hire and getting meaningful output varies dramatically depending on your hiring model.

Full-Time Hire

Phase	Duration	Notes
Write job description and post	1 week	Longer if you need internal approvals
Source and screen candidates	2-4 weeks	AI roles attract high volume, low relevance
Interview process	2-3 weeks	Technical assessment, culture fit, paid trial
Offer, negotiation, acceptance	1-2 weeks	Good candidates have multiple offers
Notice period	4-12 weeks	Senior candidates often have 3-month notice
Onboarding	2-4 weeks	Systems access, codebase familiarisation, domain knowledge
First meaningful delivery	2-4 weeks	After they understand the domain and codebase
Total: 3-7 months

Contractor

Phase	Duration	Notes
Source and evaluate	1-2 weeks	Faster with referrals or specialist platforms
Contract and IR35 determination	1 week	Faster with umbrella company
Onboarding	2-3 days	Good contractors are self-directed
First meaningful delivery	1-2 weeks	Experienced contractors ship quickly
Total: 3-5 weeks

Fractional AI Partner

Phase	Duration	Notes
Initial conversation and scoping	1-2 days	Both parties assess fit
Agreement and start	2-3 days	Simple engagement terms
First meaningful delivery	1 week	Senior partner can add value immediately
Total: 1-2 weeks

Frequently Asked Questions

Next Steps

If you are actively looking to bring AI capability into your business, here is what we would suggest:

1. Clarify what you actually need. Are you building a specific AI product, adding AI features to an existing system, or exploring what AI could do for your business? The answer determines whether you need a builder, a strategist, or both.
2. Choose your hiring model before you start recruiting. Full-time, contractor, agency, or fractional. Base the decision on your timeline, budget, and how much continuous AI work you have. Do not default to full-time just because it feels familiar.
3. Define success before you start. What does a good outcome look like in 3 months? In 6 months? What business metric will improve? If you cannot articulate this, you are not ready to hire; you are ready for a strategy conversation.
4. Prioritise shipped work over credentials. Ask every candidate to show you something live. If they cannot, move on.
5. Consider starting fractional. A fractional AI partner at GBP 2,500-3,000 per month lets you test the relationship, validate the approach, and start getting value immediately, with no long-term commitment and no recruitment risk. If the work justifies a full-time hire, the fractional period gives you the clarity to make that decision with confidence.

We work with UK businesses as a fractional AI partner. One experienced builder, working with AI tools, delivering what used to require a team. No agency overhead, no junior developers, no account managers. Just direct, senior-level AI capability applied to your business every month.

If that sounds like what you need, get in touch. We will have a straightforward conversation about your requirements, give you an honest assessment of what is feasible, and let you decide whether the fit is right.

You can also explore our case studies to see what we have built, or read our AI development cost guide for more detail on project-based pricing.