Practical AI for Real Businesses (Specific Recommendations That Actually Work)

If you're thinking about using AI in your business, ignore the flashy tech demos for a moment. Most successful AI systems don't start with new experiences or ambitious automation. They start by quietly improving work you already do every day. If you want AI to be useful instead of distracting, here's a practical way to approach it.

Start With Work That Already Exists

You should start with workflows that are already real, already necessary, and slightly painful.

Good candidates usually include:

• Reviewing documents or records
• Summarizing long or inconsistent inputs
• Classifying or tagging information
• Drafting first passes that a human reviews and finalizes

If the work already happens and takes time or attention, AI can help.
If the work doesn't exist yet, AI probably shouldn't be the one inventing it.

Use Tools That Fit Where Work Already Happens

Document Review and Summarization

You should use:

• Microsoft Copilot (Word, Outlook, Teams)
• Microsoft Foundry for custom internal tools and agents

Use these for:

• Contract or proposal summaries
• Meeting notes and follow-ups
• Digesting policies or requirements
• Creating AI-driven automation for your workplace apps

Why this works:
These tools live where your documents already live. You don't need to retrain people or invent new workflows. Humans should always remain responsible for final decisions.

Internal Knowledge Search

You should use:

• Azure AI Search combined with Microsoft Foundry
• SharePoint + Copilot if your content already lives in Microsoft 365

Use this for:

• Internal FAQs
• Onboarding materials
• Answering "Have we done this before?"

Rule to follow:
If accuracy matters, responses should show their sources. Retrieval beats guessing every time.

Lightweight Automation

You should use:

• Power Automate
• Azure Functions
• GitHub Actions for engineering workflows

Use these for:

• Routing requests
• Cleaning or normalizing data
• Triggering predictable downstream steps

Important boundary:
You should automate steps, not judgment. If you can't explain the logic clearly on a small whiteboard, it's not ready to be automated.

Decision Support (Not Decision Replacement)

You should build:

• Dashboards with AI-generated summaries
• Simple internal web tools that highlight trends or anomalies
• Reports that prepare recommendations for review

Tools that fit well here:

• Microsoft Excel + Copilot
• Small internal apps built with familiar web frameworks and APIs

You should avoid:
Fully automated decisions in customer-facing, financial, or legal workflows. Humans should remain accountable for outcomes.

Brainstorming and Ideation

You should use:

• ChatGPT
• Microsoft Copilot for internal use

Use this for:

• Generating initial ideas or outlines
• Exploring alternatives quickly
• Drafting content that humans will refine

Caution: AI can inspire, but humans must curate. Always review and adapt AI-generated ideas before sharing or implementing them.

Where You Should Say “No” to AI

Some use cases sound attractive but consistently create risk:

• Customer-facing chatbots without clear escalation paths
• AI-generated content published without human review
• Removing approval steps from high-impact workflows

These systems tend to fail quietly and expensively. When they break, trust breaks with them.

If the cost of being wrong is high, AI should assist — not decide.

Measure Value in Plain Terms

You should avoid abstract ROI models early on. Instead, ask:

• Does this save measurable time?
• Does it reduce errors or rework?
• Can someone explain how it works in plain language?

If the benefit isn't obvious within weeks, the system is probably overbuilt or mis-scoped.

A Boring Truth That Matters

The AI systems that work best are usually invisible.

They don't impress in demos.
They don't require new roles or processes.
They quietly reduce friction in work people already understand.

If you approach AI with restraint — focusing on clarity, scope, and accountability — it becomes a practical tool instead of a recurring experiment.

If you're curious about AI but cautious about risk, that's not hesitation. That's good judgment.