AI Consultancy UK | Strategy, Build & Deployment

Artificial intelligence has stopped being a board-level talking point and started becoming a board-level expectation. Executives are being asked, every quarter, what AI is doing for revenue, cost and risk. The honest answer for most UK organisations is: not enough yet. That gap – between the appetite for AI and the operational capability to deliver it – is exactly what a good AI consultancy is built to close.

iCentric Agency is a UK AI consultancy that helps mid-market and enterprise organisations move from ambition to working systems. We are not a deck-only strategy firm and we are not a body-shop. We sit between the two: senior consultants who understand commercial outcomes, working alongside engineers who ship production code. This page explains how we work, the engagements we offer, the technology we use and the results our clients see.

If you already know what you need, you can book a discovery call or jump to our AI strategy, generative AI development and AI automation service pages. If you want to understand what you should expect from an AI consultancy before you commit budget, read on.

What an AI consultancy actually does

The phrase 'AI consultancy' is used loosely. A Big Four partner means something very different by it than a two-person Shoreditch shop, and both mean something different again from a hyperscaler's professional services arm. To make a sensible buying decision you need a clearer definition.

A modern AI consultancy does five things, and you should be able to point at evidence of each before you sign a statement of work:

1. Opportunity discovery. Translating business problems into AI-shaped problems. Not every inefficiency is a model problem; many are a process or data problem with an AI accelerator on top. Good consultants tell you when the answer is not AI.
2. Data and platform readiness. Auditing what data exists, where it lives, how clean it is, who owns it and what governance applies. Without this step, every downstream estimate is fiction.
3. Solution design and build. Choosing models, architectures and integrations; building, testing and hardening the system. This is where many strategy-only firms hand off and where most of the risk actually lives.
4. Deployment and adoption. Integrating with existing systems, managing change with users, training teams and measuring real-world impact rather than lab benchmarks.
5. Governance and assurance. Putting in place the controls, documentation and monitoring that a regulator, auditor or insurer will actually accept.

The difference between a generalist management consultancy, a systems integrator and a boutique AI firm is mostly about which of those five they do well. Management consultancies tend to be strong on 1 and weak on 3–4. Large SIs are strong on 3 once they have a spec, but heavy on process. Boutique firms like iCentric are typically chosen when an organisation wants senior thinking and shipped software from the same team, on a sensible timeline.

A useful litmus test: ask a prospective AI consultancy to walk you through a production system they built, who uses it, what it cost to run last month, and what the current evaluation scores look like. If they can't answer all four, you are talking to a strategy house, not a delivery partner.

When you should (and shouldn't) engage an AI consultancy

The most expensive AI projects are not the ones that fail technically – they are the ones that should never have started. Before you brief a consultancy you should be honest about a small number of preconditions.

Signals you're ready to engage. You have an executive sponsor with budget authority. You can name a specific process, product or P&L line you want to move. Someone in the organisation can take you to the underlying data and broadly describe its shape and quality. There is a willingness to change how work gets done, not just to bolt AI onto the existing workflow.

Signals you're not ready. The conversation is dominated by 'we need an AI strategy' with no underlying business question. Data lives in personal SharePoint sites and nobody owns it. There is no nominated product owner who will be accountable for adoption. The political answer to 'who saves the headcount' is unresolved.

If you recognise yourself in the second list, the right first engagement is a short, low-cost diagnostic – typically two to three weeks – rather than a multi-hundred-thousand-pound programme. We will tell you that. We turn down work that isn't ready more often than we win it, because nothing damages an AI programme like a high-profile failure in month four.

Genuine AI opportunity vs workflow automation. A useful frame is: if the work involves judgement under uncertainty, unstructured inputs, or generating novel content, AI is probably part of the answer. If it involves moving structured data between two systems on a fixed rule set, you want workflow automation or RPA, not a model. Many of our most successful engagements combine both: deterministic automation for the spine of a process and AI for the messy bits at either end.

Build vs buy. Most organisations will end up with a mix. Off-the-shelf tools (Microsoft Copilot, Salesforce Einstein, vertical SaaS) are excellent for commoditised tasks. Custom builds make sense where the workflow is a competitive differentiator, where data sensitivity rules out third-party tools, or where the unit economics of a SaaS licence don't work at scale. An honest consultancy will help you draw the line, even when 'buy' means less revenue for them.

Our AI consultancy services

We organise our work into eight services. Most engagements blend several of them, but it is useful to see them set out separately so you can map them to your own roadmap.

AI strategy and opportunity assessment

A structured programme to identify, score and sequence AI opportunities across the business. We use a value-vs-feasibility scoring model that takes into account data readiness, integration complexity, regulatory exposure and change management effort. Output is a prioritised roadmap with cost ranges, expected returns and a recommended first build. Read more on our AI strategy consulting page.

Data readiness audits and architecture

Before models, data. We assess the quality, lineage, governance and accessibility of the data needed for your priority use cases, and design the lakehouse, warehouse or feature store architecture required to support them. Where appropriate, we partner with data engineering specialists to deliver the build.

Generative AI and LLM application development

From internal knowledge agents to customer-facing copilots, this is the largest single category of work we ship. We design retrieval-augmented generation (RAG) systems, fine-tune where it actually helps, and put rigorous evaluation harnesses around every release. More detail on our generative AI development page.

Agentic AI and workflow automation

Multi-step agents that plan, call tools and complete real work – not chatbots that quote your FAQs back at you. We design these systems with strict tool boundaries, human approval steps where the risk demands it, and observability so you can see what the agent did and why. Our AI automation service goes deeper.

Predictive analytics, ML and computer vision

Classical machine learning is not glamorous in 2025, but it still wins on a long list of problems: forecasting, churn, fraud, quality inspection, demand sensing. We build, deploy and monitor traditional ML systems alongside the generative work, and we are happy to tell you when an XGBoost model will beat a frontier LLM at a tenth of the cost.

MLOps, evaluation and observability

A model is not a product. We put in place the CI/CD, evaluation pipelines, drift monitoring and incident response that turn experiments into systems your operations team can actually run. This is where many in-house data science teams stall, and where a focused engagement from us pays for itself within months.

AI governance, risk and compliance advisory

We help legal, risk and compliance functions get comfortable with what is being built. That covers EU AI Act classification, ICO data protection impact assessments, model cards, audit logs, red-team testing and the policies your auditors will ask to see. We work alongside, not against, your second line.

Training, enablement and team augmentation

For organisations building internal AI capability, we run executive briefings, hands-on workshops for product and engineering teams, and embedded pods that work with your people for three to six months. The goal is always to hand over a working system and a team that can extend it, not to make you dependent on us.

AI consulting engagement models

There is no single right way to buy AI consultancy. The right shape depends on how mature your organisation is, how clearly you've scoped the problem, and how risk-tolerant your leadership is. We offer five core engagement models.

Discovery sprint (2–4 weeks). A fixed-fee diagnostic. We interview stakeholders, review data, run a workshop or two, and deliver a written opportunity assessment with a prioritised roadmap and indicative costs. Typical fee: £15,000–£40,000 depending on scope.

Proof-of-concept and MVP builds (6–12 weeks). A fixed-scope, fixed-fee build of a single high-value use case, deployed to a small group of real users and measured against pre-agreed success metrics. The deliverable is working software plus an honest readout of what worked, what didn't and what scaling would cost. Typical fee: £50,000–£200,000.

Embedded AI pods. A blended team of strategist, ML engineer, software engineer and designer working alongside your people for three to twelve months. Best when you have several use cases queued up and want consistent velocity. Priced on a monthly retainer with clear deliverables per sprint.

Fractional Head of AI. For scale-ups and mid-market firms that need senior AI leadership but cannot yet justify a full-time hire. One of our principals acts as your interim Head of AI, two to four days a month, owning the roadmap and chairing the AI steering group.

Managed AI operations. Once systems are live, we run them under SLA: monitoring, retraining, evaluation, incident response and incremental feature work. This sits well with clients who do not want to build a permanent MLOps team in-house.

We deliberately do not offer open-ended day-rate staff augmentation. Every engagement has a defined outcome and a defined end-state, even if it is followed by another phase. That keeps incentives aligned with shipping something useful.

The iCentric AI consultancy methodology

Our delivery method has five phases. It is deliberately unglamorous; the goal is repeatable outcomes, not a clever-looking framework.

Phase 1: Diagnose

The first one to four weeks of any engagement focus on understanding the business outcome, the underlying data, the existing process and the risk envelope. We interview people doing the work today, sit alongside them where we can, and document the current state in enough detail that we can later prove we changed it.

Key artefacts: stakeholder map, current-state process diagram, data inventory, risk and compliance register, success metric definitions.

Phase 2: Design

We choose the solution shape. That includes model selection (frontier vs open-weights vs classical ML), retrieval strategy if relevant, integration points, security model, evaluation approach and rollout plan. We will typically present two or three viable options with cost and risk trade-offs and let you choose.

Key artefacts: solution architecture, model and tooling choice with rationale, evaluation plan, rollout plan, indicative run costs.

Phase 3: Develop

Iterative, two-week sprints with a weekly stakeholder demo. We build in a real environment from week one, not in slides. Evaluation harnesses are written alongside the system, not bolted on at the end. Where data is missing or messy, we surface it immediately rather than discovering it in UAT.

Key artefacts: working software in a staging environment, evaluation dashboard, test data, technical documentation.

Phase 4: Deploy

Integration into production systems, security and privacy review, user training, change communications and a phased rollout. We are firm about phased rollouts. A pilot with 5% of users for two weeks finds problems that no amount of internal testing will.

Key artefacts: production deployment, monitoring dashboards, runbook, training materials, model card and DPIA.

Phase 5: Drive

The phase most consultancies skip. We measure adoption, business impact and model quality against the success metrics defined in Phase 1, retrain where appropriate, and feed lessons into the next use case on the roadmap.

Key artefacts: monthly impact report, retraining log, prioritised backlog for the next phase.

The methodology is industry-agnostic; the specifics inside each phase change significantly by sector.

Industries we serve

We deliver across most sectors, but the engagements we know best – and where we can move fastest – are the following.

Financial services and insurance. KYC and AML document processing, customer service copilots, broker assist, fraud and anomaly detection, regulatory reporting acceleration. Heavily shaped by FCA expectations and the SS1/23 model risk management requirements for PRA-regulated firms.

Professional services and legal. Internal knowledge agents that turn decades of precedent into a live tool, contract analysis, proposal generation, time-recording and billing copilots. The single biggest win is fee-earner time recovery.

Retail, eCommerce and consumer brands. Personalisation engines, generative product content at scale, conversational shopping assistants, demand forecasting, returns and CX deflection. We work closely with our eCommerce practice on these.

Healthcare and life sciences. Clinical document summarisation, literature search, regulatory submission acceleration, patient-facing triage. Always built with the MHRA and ICO regimes front of mind.

Manufacturing, logistics and supply chain. Predictive maintenance, computer vision for quality inspection, demand and capacity forecasting, route and load optimisation.

Public sector and not-for-profit. Case-handling copilots, FOI and complaints triage, grant and funding analysis. We have a strong view on the proportionality and transparency standards required here, drawn from the UK government's Algorithmic Transparency Recording Standard.

High-value AI use cases by business function

If you are early in your thinking and looking for ideas, the following grid is a useful starting point. None of these are speculative; they are use cases we or our peers have shipped to production in the last two years.

Customer service. Tier-1 deflection on common queries (typically 20–40% containment with careful design), triage and routing for the queries that do reach an agent, real-time agent assist that surfaces the right knowledge article and drafts replies, and post-call summarisation that ends after-call work for agents almost entirely.

Sales and marketing. AI-assisted lead scoring informed by behavioural and firmographic data, generative content at scale with brand-voice fine-tuning and human review, account research summarisation for SDRs, proposal drafting and personalisation, conversational onboarding for self-serve products.

Finance. Cash flow and revenue forecasting (often outperforming spreadsheets by significant margins), anomaly detection in spend, invoice and receipt extraction with end-to-end posting, narrative generation for management reporting, board pack drafting.

Operations. Demand planning, inventory optimisation, predictive maintenance on connected assets, scheduling and rota optimisation, computer vision quality assurance on production lines.

HR and people. CV screening with bias controls, internal mobility matching, learning content generation, employee self-service copilots that answer policy and benefits questions accurately and with citations.

Legal and compliance. Contract analysis and clause extraction, KYC document review, policy Q&A, regulatory change tracking, internal audit support.

In most organisations, three to five of these will dominate the first wave of investment. Our discovery work is largely about choosing the right ones, not generating a longer list.

The technology stack we work with

We are deliberately model-agnostic and cloud-agnostic. The right stack depends on data sensitivity, latency requirements, cost envelope and existing investments. The stack below covers the great majority of what we ship.

Foundation models. OpenAI (GPT-4o, GPT-4.1, o-series), Anthropic Claude (3.5 and 3.7 Sonnet, Opus), Google Gemini, Meta Llama family, Mistral and other open-weights options where private deployment matters. We benchmark per use case rather than defaulting.

Orchestration and agents. LangChain and LangGraph for complex flows, LlamaIndex for retrieval-heavy applications, Microsoft Semantic Kernel for .NET shops, and custom agent frameworks where the off-the-shelf options add more complexity than they save.

Vector and search. Pinecone and Weaviate where managed services make sense, pgvector inside existing Postgres estates, Elastic and Azure AI Search for clients already invested in those platforms.

Data and MLOps. Databricks and Snowflake for the analytical core, dbt for transformations, MLflow and Weights & Biases for experiment tracking, Airflow and Dagster for orchestration, Evidently and custom harnesses for evaluation.

Cloud and security. AWS (Bedrock, SageMaker), Azure (AI Foundry, OpenAI Service), GCP (Vertex AI). Private deployment on customer VPCs where data residency or sensitivity requires it. SOC 2 and ISO 27001 aligned working practices.

Front-end and integration. Next.js, React and TypeScript for bespoke UIs; Microsoft Teams, Slack and SharePoint as deployment surfaces; integration via REST, GraphQL and event-driven patterns into Salesforce, Dynamics, ServiceNow, SAP and the long tail of bespoke line-of-business systems.

We are not religious about any of these. The right choice is the one that ships, is supportable by your team, and keeps the unit economics positive at scale.

AI governance, risk and the EU AI Act

Governance is the single most underweighted area in AI buying conversations, and the area that will cause the most pain if you get it wrong. The EU AI Act is now in force on a phased timetable, the UK has signalled a pro-innovation but sector-led regime, and the ICO has published increasingly specific guidance on AI and data protection. Any AI consultancy you engage should be fluent in all three.

EU AI Act risk tiers. Most enterprise use cases fall into the limited-risk or minimal-risk categories, with obligations focused on transparency. High-risk classifications apply where AI is used in employment decisions, credit scoring, critical infrastructure, education and several other defined areas. A small number of use cases (social scoring, certain biometric applications) are prohibited outright. Mapping each use case in your roadmap to the right tier early avoids expensive rework later.

UK regulator guidance. The ICO's guidance on AI and data protection covers lawful basis, DPIAs, automated decision-making rights under Article 22 UK GDPR, and accuracy and fairness expectations. The FCA expects firms to apply existing consumer duty, operational resilience and model risk management standards to AI systems. The CMA is taking an active interest in foundation model markets and competition implications.

Practical artefacts. For every system we ship, we produce a model card describing intended use, known limitations and evaluation results; a data lineage map; an audit log of inputs, outputs and any human overrides; and a DPIA where personal data is involved. None of these are optional in regulated sectors and all of them are easier to produce alongside the build than retro-fitted afterwards.

Human-in-the-loop design. We are firm that the level of human oversight should scale with consequence. A marketing copy generator can run autonomously with sampling-based QA. A credit decision support tool requires a human decision-maker with full visibility of the model's reasoning. Designing this in from day one is much cheaper than refitting later.

Bias, hallucination and red teaming. Every system we deploy ships with an evaluation suite covering accuracy, factuality, refusal behaviour and known failure modes for the use case. For higher-risk applications, we run structured red-team exercises before go-live and at agreed intervals afterwards.

Good governance is not the enemy of speed; sloppy governance is, because it produces the late surprises that derail programmes.

Selected client outcomes

NDAs mean we cannot always name clients on a public page, but the patterns below are drawn from real engagements over the last 24 months.

Mid-market specialist lender. Document-heavy underwriting process. We built an LLM-based extraction and triage pipeline across borrower documents, with a structured human review step for edge cases. Result: 38% reduction in document review time, with extraction accuracy on key fields above the agreed 97% threshold and a full audit trail acceptable to the firm's internal audit function.

B2B SaaS scale-up. Marketing team of six trying to support a sales motion that needed four times the content output. We delivered a fine-tuned brand-voice model, a structured content brief workflow, and a review and approval interface integrated into their CMS. Result: 4x net content output, no measurable drop in engagement quality, and the marketing director kept her headcount budget for senior strategy hires rather than producers.

Specialist retailer. Onboarding journey for a relatively complex product where customers were dropping out before first purchase. We built a conversational onboarding agent that asked the right diagnostic questions and routed customers to the right starter bundle. Result: 22% uplift in first-purchase conversion within the cohort, with a payback period under two months.

Logistics operator. Stockout problem driven by lagging forecasts. We implemented a hybrid statistical and ML forecasting model, with explainability so planners could see and override drivers. Result: 17% reduction in stockouts and a meaningful drop in expedited freight costs.

Professional services firm. Internal knowledge agent over fifteen years of matter files, integrated with their DMS and time-recording system, with strict access controls. Result: roughly 6 hours per fee earner per week reclaimed from search and drafting tasks, against a target of 4.

In every case the success metric was defined at kick-off, measured against a baseline taken before any work started, and reported against monthly after go-live. We are happy to walk new clients through the detail of any of these engagements under NDA on a discovery call.

How much an AI consultancy costs in the UK

A few honest benchmarks. UK senior AI consultant day rates in 2025 sit in a wide range: roughly £1,000–£1,800 for boutique senior delivery, £1,500–£2,500 for partner-level work at mid-tier consultancies, and £2,500–£4,000+ at the Big Four and large strategy houses. ML and AI engineering rates run £900–£1,600 for senior delivery in the UK market, lower for offshore.

Translated into typical engagement totals:

• Discovery sprint: £15,000–£40,000 for two to four weeks of focused work covering opportunity assessment and roadmap.
• Proof of concept: £30,000–£80,000 for a small, narrowly scoped technical proof against a single hypothesis.
• MVP build: £50,000–£200,000 for a deployed, user-tested system covering a single high-value use case.
• Enterprise programme: £500,000–£2m+ over 6–18 months for multi-use-case rollouts including platform, governance and change.
• Managed AI operations: typically £8,000–£40,000 per month depending on the number of systems, SLAs and the depth of evaluation required.

Where hidden costs appear. Data engineering is consistently underestimated; budget 30–50% of the total programme for data work in less mature organisations. Integration work into legacy systems frequently dwarfs the model work. Change management – training, comms, hypercare – is too often left out of business cases entirely and is usually 10–20% of the total cost to value.

Run costs. Foundation model API costs, vector database hosting, observability tooling and cloud compute should be modelled at the start. We provide a per-transaction cost model in every Phase 2 design so that scaling decisions can be made on real numbers, not assumptions.

How to choose the right AI consultancy

A short checklist we recommend clients use, even (especially) if they are evaluating us against alternatives.

1. Evidence of shipped production systems. Ask for references on live systems, not pilots. Ask how many users they support today and what last month's evaluation scores looked like.
2. Depth across data, ML and applied software engineering. Many firms have one of these capabilities deeply and the others as a thin veneer. Ask to meet the engineers, not just the partners.
3. Sector experience and regulatory understanding. If you are in financial services, healthcare or the public sector, this is non-negotiable.
4. A clear point of view on build vs buy. A consultancy that recommends bespoke builds for every problem is selling its bench, not your interests.
5. A clear point of view on model choice. Beware anyone with a single preferred provider for every problem. Frontier APIs, open-weights models and classical ML all have places where they win.
6. Commercial flexibility. Look for fixed-fee discovery and MVP options. Open-ended T&M alone is a red flag.
7. A route from pilot to scale. Ask how they handle the second, third and tenth use case once the first one works. The answer should not be 'another big programme'.
8. Cultural fit. You will be in the trenches with this team. Make sure they fit how your organisation actually works.

We are confident enough in our approach to encourage clients to run this checklist on us. If the answers do not land, do not engage.

Why most AI projects fail – and how we de-risk yours

The widely cited figure that 70–85% of AI projects fail to meet expectations is not quite right, but it is not far off. The deeper question is why, and the answers are surprisingly consistent.

Problem framing. Teams start with a technology in search of a use case. We start with a workflow and a P&L line, and only choose the technology once we understand the work.

Data quality. The single biggest predictor of success. We surface data issues in Phase 1, build the case for fixing them, and refuse to paper over them in the build.

Over-engineering. Many AI programmes drown in platform work before they have proven a single use case. We ship a usable system on a thin slice of platform first, then invest in platform as multiple use cases justify it.

Change management. Adoption is harder than the model. A model that is 95% accurate but used by 10% of the target users delivers less value than an 80% model used by 90%. We over-invest in the user experience and the change programme.

Measurement. Without pre-defined metrics and a measured baseline, success becomes a matter of opinion. Every iCentric engagement defines metrics in Phase 1 and reports against them monthly.

Sponsor drift. Programmes that span more than two quarters often outlast their original sponsor. We design engagements so that the first measurable win lands inside the first 90 days.

We cannot make AI risk-free. We can dramatically narrow the distribution of outcomes by being deliberate about the things above.

iCentric vs Big Four and offshore agencies

Clients frequently ask how we compare to alternatives. A fair summary:

Versus Big Four consultancies. We are smaller, more senior, faster and significantly cheaper on a like-for-like basis. We do not use pyramid staffing models, so the team that sells the work delivers the work. Big Four firms have the edge on global rollouts and on regulated programmes that require enormous documentation overhead; we will tell you when that is the right answer.

Versus offshore development shops. We are more expensive on a day rate basis and worth it for any engagement where ambiguity is high. Where the scope is locked down and the work is execution-heavy, offshore partners can be excellent, and we are happy to work alongside them.

Versus in-house build. Many of our clients are building in-house capability and use us to accelerate it. We design engagements so that knowledge transfer is built in and your team finishes more capable, not more dependent.

Versus 'agency' shops without AI depth. Plenty of digital agencies now have an 'AI offer'. Ask them to walk you through a production system, the evaluation harness, the cost model and the incident response plan. The good ones will. The rest are reselling APIs.

Our sweet spot is mid-market and upper-mid-market organisations, scale-ups past Series B, and divisions of larger enterprises that want to move faster than their group programme allows. If that is you, we should talk.

Frequently asked questions

What does an AI consultancy do?

An AI consultancy helps organisations identify, design, build and deploy artificial intelligence systems that deliver measurable business value. A full-service consultancy covers strategy, data readiness, build, deployment, governance and ongoing operations. Some firms cover only strategy or only build; iCentric covers the full lifecycle and will tell you which parts you actually need.

How long does an AI consultancy engagement take?

It depends on the shape of the engagement. A discovery sprint runs two to four weeks. An MVP build for a single use case typically takes six to twelve weeks. An enterprise programme covering multiple use cases, platform and governance usually runs six to eighteen months. We deliberately design first phases to land a measurable result inside 90 days.

Do we need clean data before engaging an AI consultancy?

No, but you should expect data work to be part of the programme. Most organisations have data that is good enough to start with for one or two priority use cases and patchy elsewhere. A good consultancy will audit your data, tell you honestly where the gaps are, and recommend a sequence of use cases that does not depend on solving every data problem at once.

What's the difference between AI consultancy and AI development?

AI consultancy covers the full lifecycle including strategy, governance and change management; AI development is the build phase only. If you already know exactly what you want to build, you may only need an AI development partner. If you are still working out what the right priorities are, or how to govern AI at scale, you want a consultancy. Many engagements blend both.

How do you protect our data and IP?

We work under NDA from first conversation, use customer-owned cloud environments where data sensitivity requires it, and never use client data to train shared models. Foundation model providers are configured with zero-retention settings and data-processing addenda in place. We are happy to support SOC 2, ISO 27001 and equivalent due diligence processes and to align with your existing information security policies.

Can you work with our existing technology partners?

Yes, and we frequently do. We work alongside existing systems integrators, internal data and engineering teams, hyperscaler professional services and other specialist agencies. Our preference is for a clearly defined scope and a single accountable owner per workstream, regardless of which firm sits where on the org chart.

Do you work with start-ups or only enterprises?

We primarily work with mid-market organisations, scale-ups and enterprise divisions. We occasionally work with earlier-stage companies where the use case is significant and the team is ready to move quickly. The fractional Head of AI model is particularly well-suited to Series B and Series C companies.

What happens after the engagement ends?

Most engagements transition into either a managed AI operations retainer or a handover to your internal team. We are firm believers in not creating dependency; the system, code, documentation and evaluation harness all belong to you. We are usually retained for ongoing optimisation and the next wave of use cases.

Book an AI consultancy discovery call

If you have read this far, the next step is a 30-minute discovery call. On the call we will ask you about the business outcome you are trying to influence, the data and processes that touch it, who the executive sponsor is and what 'good' looks like in 90 days. You will leave with an honest view of whether AI is the right tool for the problem, what a sensible first engagement might look like, and how we would approach it.

What to bring. A rough sense of the problem, the names of one or two stakeholders we should meet next, and any prior thinking your team has done. We do not need polished decks.

Next steps. Where appropriate, we follow the call with a written proposal for a discovery sprint or MVP within five working days. There is no pressure to proceed; we would rather walk away from a poor fit than start an engagement that disappoints both sides.

Book a discovery call or email hello@icentricagency.com. We typically respond within one working day.

For related reading, see our guides to generative AI for business, the EU AI Act for UK organisations and calculating ROI on AI projects.