The 10 best ways to use AI in your business

Does your product even need AI?

Know your users and your solution before thinking about using AI. AI costs more money to use. AI can add complexity where a simple solution could be far easier and cheaper.

Start with “why”

the AI revolution is here. There is time to join it! Don’t waste time building the wrong AI feature in your product. Use this guide to figure out what you should spend time boosting with AI, and what you should skip.

Find existing pain points

Where do your customers say something is difficult or painful in their journey?
Where is there a difficult interaction in your feature or product?
Where is there an unfulfilled feature request that your team wants to take on?

Don’t use AI just because

If you didn’t use any AI, what would the solution look like?
- Try to take account of the total friction, effort, or number of interactions (interaction cost) the non-AI solution might take, and the delta between a non-AI and AI-boosted technical approach
Sometimes AI-boosted could be a cheaper path, but typically it will be more expensive.

Boosting with AI

How would this AI-boosted solution:
- Save time for users on tedious, boring, or challenging tasks
- Empower creativity for users by offering examples they might otherwise not know about
  - Help users move past the blank page and start remixing examples instead of having to come up with new ideas out of nothing
  - Help you become more confident using a complicated product by empowering “learning by doing”
- Add accessibility options that were previously missing like captions or vision assists
- Improve the overall quality of content, ideas, architecture, frameworks, or any written content
  - Grammar, structure, and prose
  - Sermon notes and questions
- Help you search for or find something faster than another solution using natural language processing
- Automate something boring or mind-numbing through computer vision or trained models
  - Data entry
  - Video generation
- Generate insights that would otherwise be cost-prohibitive or extremely tedious to discover
  - Analytics crawler for your own unique data. Like having your own plug-and-play data analyst that can generate insights on your private data.
  - Open-source and fully private.
  - Automated reports based on your preferences every week
- Offer unique personalization in your product, like recommendations or suggestions for creating new things
  - User behavior can drive powerful content recommendation
- Help you manage a task or workflow that would otherwise require a person’s careful review and attention
  - AIs are more accurate, don’t get tired, don’t get bored, and can review things 1,000,000,000 times faster than a human, and are infinitely patient
- Multiply the impact of a single user 2-10x by speeding up decision making through a programmatic AI agent
  - A single person can orchestrate a team of AI agents to run an entire business
How would this AI-boosted solution reduce:
- The total friction, effort, or number of interactions (interaction cost)
  - Show the UX Flow and take note of the time and effort savings in # of steps or complexity of the chart

Human in the loop

What’s the cost to your users if an AI is wrong? Your company?
Does a human need to verify and tweak responses? Or is close enough good enough?
You are ultimately responsible for the responses from an AI, so consider how to mitigate risks there

Determine real value

Is this actually helping your users, or just making it more complicated? Is it generating enough more business to justify the costs?

Get realistic about your timeline

Be realistic about how long it will take to:

gather data (if needed)
build the feature or product
if that feature or product will still be relevant (evergreen problem) by the time you’re done building

Final checklist

Estimating costs, risks, and vendor lock-in

AI costs money. How will your solution:
- Be hosted and run? What new infrastructure is needed?
- What model will it use, and at what speed?
  - What models can we afford, and what models do we actually need for the capabilities of the feature?
  - How fast will we need a response to inference?
  - How will we display loading / failed / error states when the model hallucinates?
- What is uptime and downtime
  - What will ensure uptime for this model?
  - How will the model be throttled or not depending on usage?
- How will we avoid losing money through system abuse or “jailbreaking” of a model?
- API or local? On the edge?

10 best use cases for AI

The best categories to use AI in so far are:

Generative AI for Content Creation: Use AI to generate text, images, or videos, enhancing product content. Design involves creating prompts for relevant outputs, integrating models like GPT or DALL-E, and ensuring quality, as per the HeightsPlatform article How to Create Digital Products With AI . Privacy and security are critical, as noted in the McKinsey article What every CEO should know about generative AI .
Personalized User Experience: Tailor interfaces using machine learning, analyzing user data to customize content. Design algorithms for behavior analysis, integrating them into delivery systems, as seen in the Forbes article Council Post: How To Effectively Integrate AI Into Your Business Operations . Privacy considerations are key, ensuring user trust.
Chatbots and Virtual Assistants: Provide support with NLP, designing conversational interfaces for real-time interaction. Integrate models to handle queries, with human oversight for complex cases, as per the Reteno article How to Implement AI in Your Product — Steps and Strategies . This enhances customer service, as noted in the CIO article.
Recommendation Systems: Suggest products or content based on behavior, using machine learning. Design involves analyzing user data for patterns, integrating algorithms into search features, and updating regularly, as detailed in the LeewayHertz article AI-based recommendation system: Types, use cases, development and implementation . This drives engagement, per the Algolia blog What role does AI play in recommendation systems and engines? .
Search and Discovery: Enhance search with AI for better query understanding, using NLP and machine learning. Design systems for improved result ranking, integrating them into search bars, as per the Uptech guide How to Integrate AI into Your App: Comprehensive Guide . Efficiency is crucial for large datasets.
Security and Fraud Detection: Protect users with AI, using anomaly detection and pattern recognition. Design models to analyze activities, integrating them into authentication systems, and updating to counter threats, as seen in the MasterofCode article 10 AI Use Cases for Business Today . This is vital for products handling sensitive data.
Data Analysis and Insights: Offer insights using AI, processing and visualizing data. Design features for actionable insights, integrating tools into the product, as per the IBM article AI Examples, Applications & Use Cases . Accuracy and relevance are key for user value.
Automation of Tasks: Streamline processes with AI, like workflow automation. Design tools for repetitive tasks, integrating them to improve efficiency, and monitoring for errors, as noted in the LeewayHertz article AI Use Cases & Applications Across Major industries . This enhances productivity.
Predictive Features: Forecast trends using predictive modeling, offering proactive solutions. Design models using historical data, integrating predictions into the product, and validating with real data, as per the SuperAnnotate blog The most popular AI use cases and applications in 2022 . This anticipates user needs effectively.
Accessibility Features: Use AI for speech recognition or image description, supporting users with disabilities. Design assistive technologies, integrating them into interfaces, ensuring usability, as seen in the ProductSchool article Top 15 AI Business Use Cases in 2024 + Examples . This promotes inclusivity.