Choosing the Right AI Solution for Your Business
Not every problem needs generative AI. Learn to match the right type of AI — from predictive models to Copilot to custom Foundry solutions — to the business problem in front of you.
How do you choose the right AI solution?
Think of AI solutions like tools in a workshop. You wouldn’t use a hammer to cut wood.
Some business problems need prediction (“Who will churn?”). Some need automation (“Route tickets to the right team”). Some need creation (“Draft a proposal from our last 10 winning bids”).
Choosing the right AI solution starts with the problem, not the technology. Once you know what you’re solving, the right tool becomes obvious.
The AI solution spectrum
Not all generative AI solutions are created equal. They range from ready-to-use to fully custom:
| Feature | Customisation | Cost | Time to deploy | Best for |
|---|---|---|---|---|
| Microsoft 365 Copilot | Low — use as-is with your M365 data | $30/user/month add-on | Days (enable licences) | Productivity: drafting, summarising, analysing across M365 apps |
| Copilot Studio agents | Medium — custom agents with your data sources | Included with Copilot or PAYG | Weeks | Specific workflows: HR FAQ bot, policy lookup, onboarding helper |
| Azure OpenAI Service | High — custom prompts, fine-tuning, RAG | Pay-per-token | Weeks to months | Custom applications: customer-facing chatbots, document processors |
| Microsoft Foundry | Full — end-to-end AI app development | Pay-per-use + compute | Months | Complex solutions: multi-model orchestration, custom agents, enterprise AI apps |
Decision framework: Four questions to ask
Before recommending a solution, ask these four questions:
1. Who is the user?
| User Type | Best Starting Point | Why |
|---|---|---|
| Knowledge workers (internal) | Microsoft 365 Copilot | Already embedded in their daily tools |
| Customers (external) | Azure OpenAI + custom app | Need branded, controlled experience |
| Specific teams with specific data | Copilot Studio agents | Grounded in team-specific knowledge sources |
| Developers building AI products | Microsoft Foundry | Full control over models, data, and deployment |
2. What data does the AI need?
- M365 data only (emails, documents, meetings) → Microsoft 365 Copilot
- Specific knowledge bases (policies, product catalogues) → Copilot Studio agents with connectors
- External or proprietary data → Azure OpenAI with RAG pattern
- Multiple data sources, complex orchestration → Microsoft Foundry
3. How much control do you need?
- Minimal — just want AI in existing workflows → Copilot
- Some — custom prompts, specific knowledge sources → Copilot Studio
- Full — control every aspect of the AI experience → Foundry + Azure OpenAI
4. What’s the budget model?
- Per-user monthly → Copilot (~$30/user/month)
- Per-use consumption → Azure OpenAI (tokens), Copilot Studio (messages)
- Mixed → Enterprise agreements combining per-user and consumption
Scenario: Elena's firm chooses three AI solutions
Elena’s consulting firm (Meridian Consulting) doesn’t pick just one — they pick the right solution for each problem:
- Proposal drafting → Microsoft 365 Copilot (works inside Word with existing documents)
- New hire onboarding FAQ → Copilot Studio agent (grounded in the HR SharePoint site)
- Client-facing research portal → Azure OpenAI + custom app (branded experience, external users, proprietary data)
This is a common pattern: most organisations use multiple AI solutions for different use cases.
When generative AI is NOT the answer
Not every problem needs generative AI. Sometimes traditional AI or even simple automation is better:
| Scenario | Better Alternative | Why Not Gen AI? |
|---|---|---|
| Predicting equipment failure | Traditional ML (predictive maintenance) | You need precise numerical predictions, not generated content |
| Routing support tickets | Classification model | Categorisation is faster and cheaper with traditional ML |
| Processing invoices with fixed formats | Rule-based automation (RPA) | Structured extraction doesn’t need the flexibility of an LLM |
| Real-time fraud detection | Anomaly detection model | Speed and precision matter more than language generation |
Exam tip: 'Select a generative AI solution' questions
When the exam says “select a generative AI solution to meet a business need,” it’s testing whether you can:
- Recognise when gen AI is appropriate (vs traditional AI or automation)
- Choose the right Microsoft gen AI product (Copilot vs Studio vs Azure OpenAI vs Foundry)
- Match the solution to the constraints (budget, users, data, customisation needs)
The wrong answer often suggests gen AI for a problem that’s better solved by traditional ML or simple automation.
Key flashcards
Knowledge check
Ravi's company needs a customer-facing chatbot that answers questions about their product catalogue. The chatbot must be branded and integrated into their website. Which solution should he choose?
Tomás oversees 5,000 workers at PacificSteel Manufacturing. His maintenance team wants AI to predict when conveyor belt motors will fail before they cause downtime. What type of AI solution is most appropriate?
Elena wants her consultants to use AI to summarise client meeting notes in Microsoft Teams. Budget is a concern. What's the most cost-effective solution?
🎬 Video coming soon
Next up: AI Models: Pretrained vs Fine-Tuned — understanding the building blocks of generative AI.