Build vs Buy vs Integrate
Building a custom model is expensive and rarely necessary. Buying an AI product is fast but shallow. The real value is in integrating AI into your existing workflows via API. Know which strategy fits each use case.
- Training a frontier model costs $10M–$100M+.
- Off-the-shelf AI tools pay back in weeks for admin tasks.
- API integration typically costs £5–50k and delivers in 2–4 months.
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The AI Maturity Model
Level 1: experimental (individual tools). Level 2: functional (department automation). Level 3: strategic (AI in decision-making). Level 4: transformational (AI-native business model). Where is your organisation?
- 70% of large enterprises are at Level 1–2.
- Level 3+ requires data infrastructure, governance, and cultural change.
- AI-native companies (Airbnb, Stripe, Uber) bake AI into their core product loops.
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Identifying High-Value AI Opportunities
The best targets share three traits: high volume, pattern-based decisions, and clear success criteria. Think: contract review, call triage, inventory forecasting, onboarding guides.
- Legal contract review: AI reduces review time 70–80% at leading law firms.
- Customer service AI: Intercom reports 31% of queries fully resolved without human.
- Code review AI: reduces review cycle time from days to hours at Microsoft.
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Prompt Engineering as Strategy
How you instruct an AI is as important as which AI you choose. A well-structured prompt with role, context, format, and constraints produces dramatically better output. This is a transferable organisational skill.
- Adding 'think step by step' to a prompt improves accuracy 10–40% on reasoning tasks.
- System prompts define AI persona, scope, and safety guardrails.
- Prompt libraries reduce inconsistency across teams — treat them as IP.
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The Data Moat
Your proprietary data is your AI advantage. A model trained or retrieval-augmented on your company's knowledge, history, and tone outperforms any generic product. Identify and curate your data assets now.
- RAG systems can inject up to ~200K tokens of company knowledge per query.
- Structured data (CRMs, ERPs) + unstructured (emails, docs) = differentiated AI.
- Data quality trumps data quantity — garbage in, garbage out.
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AI Pilot Design
A good pilot has a measurable baseline, a defined success metric, a 90-day timeline, and a decision gate. Without these, pilots drift into endless POCs that never reach production.
- Define the metric before the pilot — speed, accuracy, cost, NPS.
- Pilot with power users, not sceptics — build momentum, then convert.
- Set a 90-day hard decision gate: scale, pivot, or stop.
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Scaling from Pilot to Production
Scaling fails when the model, the prompt, or the data changes — and no one notices. Production AI needs monitoring, versioning, and rollback capability just like software.
- 'Model drift' occurs when the AI's behaviour shifts after a provider update.
- Implement evals: a test suite that flags regressions automatically.
- Production AI needs human-in-the-loop escalation paths for edge cases.
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Make vs Buy for AI Talent
The shortage of AI engineers is real. Strategies: upskill product managers to 'AI product owners', hire two senior ML engineers who can mentor others, partner with specialist agencies for delivery.
- AI engineer salaries: £80–150K UK, $150–300K US in 2024.
- Prompt engineers earn £60–90K — a new role that didn't exist in 2021.
- Internal AI champions (non-technical) accelerate adoption faster than external hires.
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