Evaluation framework · AI tools

How to choose an enterprise AI assistant

The short answer. Eight dimensions decide which enterprise AI assistant fits a UK business: data handling (where data goes, whether it trains the vendor's models), workflow integration (where the AI lives — inside your existing tools or alongside them), governance fit (admin controls, audit logging, data residency), commercial model (per-seat, usage-based, included with existing licensing), capability match (which workflows the tool genuinely improves), regulatory posture (sector-specific certifications and DPAs), switching cost (data and prompt portability if you change), and vendor maturity (enterprise track record, support availability, contractual seriousness). Score each, weight for your sector, then choose.

Why we won't recommend a specific tool

The enterprise AI assistant market is changing faster than any blog post can keep up with. Tools that were obviously best in one quarter may be obviously second-best by the next. The principled answer to "which enterprise AI assistant should we use" is: apply a consistent evaluation framework to the current options, weight the dimensions for your sector, and reassess every 6–12 months. The framework below is what doesn't age. Tool-specific recommendations would.

The eight dimensions in detail

1. Data handling. Where is your data processed (UK, EEA, third country)? Is it used to train the vendor's models — and is opt-out the default? What's the retention window? 2. Workflow integration. Does the AI live inside the tools your team already uses (Microsoft 365, Google Workspace, Slack, your CRM), or alongside them as a separate destination? Integration usually wins on adoption; standalone usually wins on capability range. 3. Governance fit. What admin controls exist? Is audit logging available and retained for long enough? Can data residency be guaranteed contractually? 4. Commercial model. Per-seat, usage-based, or included with existing enterprise licensing? Hidden costs of training and adoption support? 5. Capability match. Which specific workflows in your business does this tool actually improve — and which are noise? Pilot before scaling. 6. Regulatory posture. Sector-specific certifications, DPAs that align with your regulatory context (FCA, SRA, NHS supplier, etc.). 7. Switching cost. If you change tools in 18 months, what's portable — your prompts, your data, your custom configurations? 8. Vendor maturity. Enterprise contracting track record, support availability in UK business hours, contractual seriousness about uptime and incident response.

Sector weighting

Not every dimension weighs the same for every business. Legal: data handling and switching cost weigh heaviest. Financial services: regulatory posture and governance fit weigh heaviest. Healthcare suppliers: data handling and regulatory posture. Recruitment: governance fit (Article 22 implications). Logistics: workflow integration (TMS/WMS/ERP). Small business: commercial model and capability match. The Arx Certa scorecard's use-case dimension surfaces which of these will matter most for your specific business.

How to actually run the evaluation

Four-step process: 1. Shortlist 2–3 tools. Don't evaluate 8 — you'll never finish. 2. Score each on the 8 dimensions, 1–5, with evidence (not vibes). 3. Weight the dimensions for your sector (see above). 4. Pilot the top-scoring tool with a defined cohort, defined data scope, and defined success metric for 4–6 weeks before scaling. The single biggest failure pattern is choosing a tool based on capability alone, skipping the governance + integration + regulatory dimensions, and discovering them after deployment.

What this framework deliberately doesn't include

Brand reputation, marketing claims, headline benchmark performance, sales-engineer demos. All of those are noise relative to the eight dimensions above. The framework is intentionally evergreen — when new tools arrive (and they will, every quarter), they get scored on the same eight dimensions. The exercise is the same in 2027 as in 2026.

Test your AI readiness in 4 minutes

Free AI Readiness Scorecard

Twelve plain-English questions across governance, data, infrastructure, security and use case. Get your 0–100 score, your readiness band, and a personalised 30-day action plan.

Take the scorecard →

Frequently asked

Why don't you just compare Microsoft Copilot vs ChatGPT Enterprise vs Claude for Work directly?

Because tool-specific comparisons go out of date in months. Pricing changes, features change, data handling positions evolve, new tools arrive. A framework that scores any tool against the same eight dimensions stays useful — a direct comparison from May 2026 is partly wrong by November 2026.

Is Microsoft Copilot the obvious default for businesses already on Microsoft 365?

Often, but not always. The default-favourability is on workflow integration and commercial-model dimensions (already in your licensing). But Copilot scores differently on governance fit (tenant work required), regulatory posture (sector-specific), and capability match (some workflows other tools handle better). The framework lets you check whether the default-favourability holds in your specific case.

How long should the pilot phase be?

4–6 weeks is the working range for an enterprise AI assistant pilot. Less than 4 weeks: you can't tell adoption signal from novelty signal. More than 6 weeks: decision drift kicks in and the pilot becomes a low-grade deployment. The pilot's success metric should be defined before it starts, not negotiated at the end.

What if multiple tools score similarly?

Then pick the one with the lowest switching cost. In a tie on the other dimensions, switching cost is the tiebreaker — because at least one of the contending tools will look obviously worse 18 months from now, and you want to be able to migrate.

How does this connect to AI readiness?

The framework above is the tool-selection question. AI readiness is the prior question of whether your foundations are ready for the tool you choose. A Copilot pilot with the foundations gap unaddressed will be a worse pilot than ChatGPT Enterprise with the foundations in place. The Arx Certa scorecard surfaces the foundations picture before tool choice becomes the operative question.

Related Arx Certa services

If the readiness gaps the scorecard surfaces for your business need outside help to close, these are the engagement types we run for UK firms:

AI services — implementation reviews, AI policy work, vendor due diligence, and pilot scoping for UK businesses adopting AI safely.
Cybersecurity — the security overlay AI use requires, including UK GDPR, NCSC alignment, vendor risk assessment, and audit-readiness.
Database — the data foundations work AI projects depend on. Most AI pilots fail because of the data underneath, not the model.
Infrastructure — cloud, identity, network and integration foundations that need to be in place before production AI deployment.