I’ve spent years testing AI tools, writing about their quirks, and helping teams pick the right tech that actually fits their day-to-day work. Choosing an AI assistant for your team isn’t just about picking the model with the highest benchmark — it’s about matching capabilities to workflow, assessing privacy and security trade-offs, and making sure the tool will still be useful six months from now.
Start by defining the problems you want the assistant to solve
Before you compare vendors, write down the concrete tasks you expect the assistant to perform. Be specific — “help engineers write tests,” “summarise meeting notes,” or “triage customer support tickets” are better than vague goals like “make us faster.”
For each task, list the expected inputs and outputs, who will use it, and how much human oversight is required. This will guide requirements for integration, latency, cost, and — critically — data handling.
Match capability to workflow, not benchmarks
Benchmarks and GPT scores are useful signals, but real teams care about reliability, context retention, and predictable behaviour. Ask:
- Does the assistant keep context across a multi-step workflow (multi-turn memory)?
- Can it access and query internal docs, ticketing systems, or knowledge bases (connectors and RAG)?
- How easy is it to customise prompts or add guardrails for your domain?
- Does it support the interfaces your team uses: Slack, Microsoft Teams, IDE plugins, or a web app?
Try a lightweight pilot with typical users. I prefer pilots that run against real data (masked or synthetic if needed) rather than synthetic demos. You’ll learn far more about friction points, hallucinations, and integration complexity.
Privacy and data governance: questions you must answer
Privacy is often the blocker that determines whether a model can be used at all. When evaluating assistants, treat data governance as a checklist:
- Data residency: Where does the provider store and process your data? Do you need on-premises or EU/UK-resident hosting?
- Data retention & deletion: Can you control retention policies and delete logs on demand?
- Training and model usage: Does the provider use your data to further train their models, or is it explicitly excluded?
- Access controls & audit logs: Are there granular RBAC controls and complete audit trails for queries and outputs?
- Encryption: Is data encrypted in transit and at rest? Are FIPS or HSM options available for keys?
Vendors differ wildly here. Some, like default cloud-hosted OpenAI endpoints, historically used customer data for service improvement unless you paid for an enterprise agreement or opted out. Microsoft’s Copilot for Microsoft 365 follows certain enterprise controls within MS ecosystems. Anthropic and some enterprise-focused vendors offer explicit contractual guarantees about training data usage. If you need strict controls, consider private-hosted models (Llama 2, Mistral, or specialist enterprise LLMs) that you can run in your VPC or on-prem.
Security and compliance considerations
Align the vendor’s security posture with your regulatory needs. Ask for:
- Certifications: ISO 27001, SOC 2 (Type II), GDPR compliance statements.
- Penetration test reports or a third-party security assessment.
- Details on incident response and breach notification timelines.
- Support for enterprise SSO, MFA, and SCIM provisioning.
For highly regulated environments, I’ve recommended isolating the assistant behind internal services that filter or redact PII before sending it to the model.
Integration and observability
An assistant is only useful when it fits into existing tools. Check for:
- Native integrations (Slack, JIRA, Zendesk, GitHub, Microsoft Teams).
- APIs and SDKs to integrate into custom apps or pipelines.
- Admin dashboards for usage, cost, and behaviour monitoring.
- Observability: can you log prompts and responses, set alerts for anomalous usage, and export data for analysis?
When I’ve seen deployments succeed, it’s because teams could quickly embed the assistant where work happens — not force people to switch tools.
Customization, fine-tuning and prompt engineering
Consider how the model can be tailored to your domain:
- Fine-tuning: Does the vendor allow supervised fine-tuning on proprietary data, and what are the privacy implications?
- Adapters or embeddings: Is retrieval-augmented generation (RAG) supported so the assistant uses your knowledge base rather than hallucinating?
- Prompt templates and guardrails: Can you centrally manage and version prompts, apply safety rules, and override outputs?
Sometimes a smaller, well-curated vector store with a strong retrieval policy outperforms a larger general-purpose model that hasn’t been aligned to your domain.
Cost, latency and scale
Costs can balloon if you only budget by user seat. Factor in:
- Per-request compute costs (tokens or inference time).
- Embedding and retrieval costs for RAG scenarios.
- Storage for conversation logs, vectors, and attachments.
- Operational costs if you self-host (infrastructure, tuning, maintenance).
Latency matters for real-time workflows (IDE assistance, chatbots). If your team needs <200ms responses, cloud solutions with edge inference or on-prem inference nodes may be necessary.
Decision matrix — quick evaluation table
| Criteria | High Priority? | What to check |
|---|---|---|
| Data privacy & training use | Yes | Contractual guarantees, on-prem options, data residency |
| Integrations | Yes | Native apps, APIs, webhooks, SDKs |
| Customization | Medium | Fine-tuning, RAG, prompt management |
| Security & compliance | Yes | Certifications, SSO, audit logs |
| Cost & operational overhead | Yes | Pricing model, self-hosting costs |
| Latency & reliability | Medium | SLA, edge inference, uptime history |
Which vendors fit which needs?
Here are simplified patterns I’ve seen work in the field:
- Cloud-first, broad capabilities: OpenAI (ChatGPT/Assistant API) or Anthropic for fast rollouts and strong models. Good for non-sensitive data or with enterprise contracts that limit training on customer data.
- Microsoft ecosystem: Copilot for Microsoft 365 or Azure OpenAI when you rely heavily on Microsoft tools and want tighter enterprise controls.
- On-prem / private-hosting: Llama 2, Mistral, or private enterprise LLMs when data residency and non-training guarantees are essential.
- Specialised vertical vendors: Industry-specific assistants (healthcare, legal) that ship with domain knowledge and compliance baked in.
Choose the pattern that matches your data sensitivity, integration needs, and ability to maintain infrastructure.
Rollout strategy I recommend
Don’t try to automate everything at once. My favoured path:
- Run a 4–6 week pilot with a small, cross-functional group using realistic data.
- Measure trust: accuracy, number of edits humans make, time saved per task, and error modes.
- Set governance: data policies, escalation paths for hallucinations, and fallback behaviours.
- Iterate on prompts, connectors, and policies. Gradually expand to other teams once trust is established.
Picking an AI assistant is as much organizational design as it is a technical choice. If you keep the use cases grounded, prioritise data governance, and run a tight pilot with measurable goals, you’ll avoid the common pitfalls — the bloated cost, the privacy surprise, and the assistant that nobody trusts to do real work.
