What 600+ Use Cases Tell Us: Where AI Is Delivering Real Business Value

By Steve Nicholls – your strategic advisor from Bizedge.ai

When OpenAI recently analysed over 600 of its most successful customer use cases, the results were striking, not for their complexity, but for their clarity. Every use case fell into just six categories.

Not hundreds. Just six.

That is a strong signal for any mid-sized company trying to make sense of AI. The message is this:

You do not need to reinvent the wheel—you need to know which wheels to choose.

In this article, I will walk you through those six categories and provide practical examples of how mid-sized businesses (like those you or I work with every day) can apply them today.

1. Content Creation

Description:
Using AI to generate, translate, edit, or visualise content. This includes everything from marketing assets to internal documents and legal templates.

Examples for Mid-Sized Companies:

1. Marketing Campaign Generator: AI produces social posts, email campaigns, and image creatives in bulk, reducing reliance on agencies.
2. Internal Policy Drafting: Drafts HR policies, compliance documents, or employee handbooks quickly and accurately.
3. Client Proposal Editing: Refines and personalises sales proposals or pitch decks based on the prospect's sector and priorities.
4. Localisation of Sales Material: Translates product brochures or training material into multiple languages for regional teams.
5. Video Scripts and Blog Posts: Creates engaging scripts for customer onboarding or thought leadership blog content.

Why it matters:
This is one of the fastest ways to save time, especially if your marketing team is small or overstretched.

2. Research

Description:
AI rapidly gathers, organises, and summarises information—ideal for competitor benchmarking, trend analysis, or opportunity scouting.

Examples for Mid-Sized Companies:

1. Competitor Intelligence: Aggregates insights about competitors’ product launches, pricing changes, and hiring trends.
2. Supplier Research: Evaluates global supply chain options based on cost, reliability, and sustainability credentials.
3. Market Entry Analysis: Summarises market dynamics, customer behaviour, and regulations when expanding to a new region.
4. Grant and Tender Research: Identifies and pre-screens suitable funding or procurement opportunities.
5. Tool Comparison Reports: Evaluates different software platforms for CRM, HR, or finance—summarised in simple pros/cons tables.

Why it matters:
Research used to take days. Now it takes minutes. That levels the playing field against larger firms with dedicated teams.

3. Coding

Description:
AI helps generate, explain, or debug code, even for non-programmers. Useful for automation, web presence, or custom tools.

Examples for Mid-Sized Companies:

1. Rapid Web App Prototypes: Create internal dashboards or mini-portals for order tracking or employee feedback.
2. Automated Form Builders: Creates online forms that capture and validate customer input and push data into CRM systems.
3. Email Parser Tool: Develops scripts to extract structured data from inbound emails (e.g. enquiries, orders).
4. API Integrator: Builds connectors between off-the-shelf tools (e.g. connecting Stripe to QuickBooks).
5. Bug Fix Suggestions: Helps diagnose and fix code issues in Excel macros, web pages, or legacy systems.

Why it matters:
This opens up innovation. Suddenly, non-tech teams can prototype ideas or automate tasks without waiting for IT.

4. Data Analysis

Description:
AI identifies patterns, forecasts trends, and visualises insights from data. Especially useful when data is fragmented or underused.

Examples for Mid-Sized Companies:

1. Sales Funnel Analysis: Identifies where leads drop off and suggests improvements to the customer journey.
2. Customer Segmentation: Analyses customer databases to build distinct audience groups for targeted marketing.
3. Pricing Strategy Review: Models price elasticity and recommends adjustments based on competition and margin.
4. Staff Productivity Metrics: Uses time tracking and task data to identify inefficiencies in team workflows.
5. Inventory Demand Forecasting: Enhances stock planning by utilising historical sales and seasonal variation data.

Why it matters:
Your data is often underused. AI helps unlock its value, without needing a data scientist on staff.

5. Ideation and Strategy

Description:
AI supports creative and strategic thinking—from brainstorming product names to testing campaign ideas.

Examples for Mid-Sized Companies:

1. New Product Brainstorming: Generates and filters product ideas based on trends, customer feedback, and gaps.
2. Rebrand Concepts: Suggests new naming ideas, brand messages, or visual themes with sample taglines.
3. Internal Strategy Jam: Creates simulation prompts or thought experiments to challenge strategic assumptions.
4. Workshop Planning: Helps facilitators design better brainstorming, planning, or innovation sessions.
5. Board Pack Support: Drafts strategic options for the board—e.g., growth vs. cost-cutting scenarios with pros and cons.

Why it matters:
You still lead the thinking, but AI gives you a head start and a fresh perspective.

6. Automation

Description:
Using AI to streamline or take over repetitive tasks, from scheduling to document processing, freeing up human capacity.

Examples for Mid-Sized Companies:

1. Lead Qualification Bot: Triages website enquiries and sorts them into priority tiers using AI scoring.
2. Customer Service Inbox Assistant: Drafts responses to common customer support questions, ready for human review and approval.
3. Invoice Data Extractor: Automates the extraction of invoice amounts, due dates, and line items into finance systems.
4. Meeting Summariser: Captures and distributes meeting notes and action items from Zoom or Teams calls.
5. Staff Onboarding Workflow: Automatically sends welcome emails, booklets, training invites, and checks document submission.

Why it matters:
Automation is no longer just for the enterprise. Mid-sized companies can now do more with fewer resources—and scale more confidently.

Why This Classification Is Useful

1. Simplifies the AI conversation – Separates real business opportunities from noise.
2. Provides a clear roadmap – Helps identify exactly where to start based on proven success from other companies.
3. Supports low-risk trials – Enables businesses to test solutions without disruption rapidly.
4. Democratises AI – Makes powerful capabilities accessible to businesses without huge budgets.
5. Addresses immediate business needs – Directly relates AI solutions to common business pain points.

Finding Quick Wins and Building a Roadmap

Using this classification helps you quickly pinpoint AI opportunities within your organisation based on proven use cases. Start by identifying quick wins—projects that are easy to implement and offer immediate, impactful results. Quick wins not only deliver rapid business value but also build team confidence and familiarity with AI tools. The experience gained from these early successes creates a foundation for tackling more complex, longer-term AI implementations.

Quick Wins Checklist

1. Identify key pain points, such as content overload, research bottlenecks, manual tasks, unused data, ideation blocks, or a lack of simple digital tools.
2. Map to AI Categories: Align pain points with categories such as Content Creation, Research, Automation, Data Analysis, Ideation, and Coding.
3. Choose One Pilot Project: Select a manageable task such as drafting a blog post, automating meeting summaries, or segmenting customer data.
4. Nominate a Team Member: Pick someone curious and capable, allocate dedicated time for exploration, and encourage sharing of insights.
5. Measure and Share Results: Track improvements, such as time saved or errors reduced, gather feedback, and document lessons for future projects.

Common Mistakes to Avoid

1. Do not attempt to work on multiple categories simultaneously. Focus on one category, master it, and then expand. Companies that try to implement AI across all six categories simultaneously typically fail in all of them.
2. Do not skip the measurement phase - If you cannot measure your current state (time spent, error rates, costs), you cannot prove AI is working. Always establish baseline metrics first.
3. Do not choose complex first projects - Your first AI implementation should be simple enough to complete in 2-4 weeks. Avoid projects requiring multiple integrations or significant process changes.
4. Do not implement without team buy-in - The most sophisticated AI solution will fail if your team resists using it. Start with willing early adopters, not sceptics.
5. Do not expect perfection immediately; AI tools typically achieve 80% accuracy initially and continue to improve with training and fine-tuning. Plan for an iteration period.

Final Thought

When exploring AI, it’s essential not to start with technology but with what genuinely matters. These six categories provide clarity, highlighting precisely where AI delivers real value.

Ignoring all six categories is no longer viable; embracing even one can transform your business’s competitive edge.

Would you like help mapping these categories to your specific business context? Bizedge.ai specialises in guiding mid-sized companies to practical, impactful AI solutions.

Want to See More Simple, Practical Uses of AI for Small Businesses?

Steve Nicholls, MBA MSc, is the author of "The AI Business Opportunity" and founder of Bizedge.ai, where he helps small and mid-size businesses implement practical AI solutions for growth.

Explore the book at: bpi5.short.gy/AIBook

Or find it on Amazon.co.uk or Amazon.com.