Should You Use AI in Your Business? 4 Simple Questions to Ask

I recently spoke to a customer doing early experimentation with Gen AI. He was building an LLM-based FAQ bot to answer policy related questions for internal users. Upon inquiry, it was discovered that they have 30-40 policy documents & the policies don't change frequently. Now as you can imagine, this is a use case more suited to a rules based chatbot than to an LLM based Gen AI model.

?Now that enterprises are starting early experimentation on Gen AI, I'm seeing lots of queries around how to pick the right use cases for Gen AI. Most of them are starting with simple low-risk AI bots for customer service, internal FAQ bots etc. They have queries about picking low-risk, high-impact areas appropriate for Gen AI. So, here are a few principles to keep in mind.

?Before you start building an AI model, thoroughly examine the use case. Why Gen AI, why not a rules based technology, what will it help you accomplish - efficiency, higher revenue, customer satisfaction etc, how will your customer (internal or external) benefit from it, what's the measurable targeted RoI? Also think about the quantifiable outcome KPIs you're targeting and how to collect the data to calculate those.

?AI excels in:

• Personalization: Recommending products like movies, retail loans, credit cards based on individual preferences and past data.
• Prediction: Forecasting future events like probability of loan default, network outage, stock prices etc. based on historical data and complex algorithms.
• Understanding natural language: Processing and analyzing human language by say multi-lingual virtual assistants or doing real time audio sentiment analysis or video translations in multiple languages
• Image recognition: Identifying and classifying objects within images or videos, used for facial recognition, medical diagnostics etc. For example, we've seen AI models with higher accuracy in diagnosing diabetic retinopathy from retinal images than human doctors.

?Consider an alternative to AI when:

• Predictability is critical: If consistent and predictable outcomes are essential, a rule-based approach might be more reliable than AI, which can sometimes exhibit unexpected behavior. For example in regulatory reporting or emergency response procedures a rules based approach could be better than AI.
• Transparency is crucial: If users or developers need to fully understand how decisions are made, opting for a rule-based system allows for clear and transparent logic. For example? in content moderation for social media platforms, a transparent rule-based system explaining prohibited content categories and removal criteria is needed to maintain user trust and avoid accusations of censorship.
• Automation not desired: If users prefer tasks to remain human-controlled, an AI implementation might not be necessary or even desired. For example, AI powered robo-HRs can auto generate job descriptions, recommend best suited candidates etc but are not best-suited for conflict resolution or initiating disciplinary actions.

?So, in summary, here are some questions to consider before building with AI:

• Does this problem truly require AI, or could a simpler approach suffice?
• How important is consistent and predictable decision-making?
• Is it essential to understand how AI arrives at its conclusions?
• Would people prefer to perform this task themselves, or does it require automation?

By answering these questions, you can ensure you're using AI effectively and maximizing its value for your business.

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