Identifying & Mapping AI Use Cases

There is a lot of pressure at all organizational levels to deliver Gen AI Use Cases, but where to start? Will those use cases serve the back office, the core capabilities, or the products and services? How will those use cases generate the expected value, and is this aligned with the company's KPI and objective?

I propose a framework you can follow with your team to get an organized list of use Gen AI Use Cases.

This involves a series of steps you must do with your team.

1 - Introduction to Gen AI

Explain Gen AI; most of your team members must already know or have played with Chat GPT. But recall all the benefits of the technology:?content generation, data synthesis, and predictive modeling. Maybe explain the difference with other types of AI.

Acknowledge that adoption of this technology has already started, that the train has left the station and is moving fast:

• In the 2023 Global Survey, McKinsey reported that nearly 50% of organizations have incorporated AI into at least one business function.
• According to a 2023 survey by the Content Marketing Institute, 60% of marketers are using Gen AI tools to create content.
• Deloitte's 2023 banking industry outlook revealed that 40% of financial institutions use Gen AI for risk assessment, fraud detection, and algorithmic trading.
• According to CB Insights, venture capital investment in Gen AI startups surpassed?$2 billion?in the first half of 2023 alone.
• Gartner believes that in 2025, Generative AI will be a workforce partner for 90% of companies globally.

2 - Aligning Gen AI with Business Objectives

Technology needs to serve the corporate strategy and objectives; the corporate objectives should be defined with metrics so that you can identify how you can improve them.

In your quest to identify Gen AI use cases, you must start with your corporate objectives. If the corporate goals are not clear and measurable, they should be defined first before addressing Gen AI use Cases.

• Define which KPI and target you want to focus on for your Gen AI project
• Imagine expected gains at an aggregated level

3 - Identifying and listing pain points

Conduct interviews and meetings to identify pain points in your processes. Map and visualize your business processes to identify bottlenecks. This will feed the ideation process that follows.

Pain points can be identified and located where you find:

• Manual and Repetitive Tasks
• Bottlenecks
• Lack of Information Sharing
• IT Integration Issues
• Complex Regulations
• Risk Management
• ..

You can use Employee Surveys and Feedback, process mapping, or customer feedback to identify those pain points.

4 - Spotting Opportunities for Gen AI

We are now getting to the heart of it, and hopefully, the work you have done so far will pay off. You have an idea of the type of use cases you can solve, know the company objectives, and finally, know what pain points you want to address.

You need to focus on the following aspects:

• Repetitive and Time-consuming Tasks

This is not new; Henry Ford did the same. You want to automate or improve the process. However, now, Generative AI can help with Data Entry, Report production, and responding to prompts.

• Data-rich Areas

Rich data sets can be leveraged with the Foundational Models to learn, identify patterns and trends, and provide forecasts. Those use cases are not limited to Gen AI but also Statistical analysis. The data-rich area can also be easily indexed and searched using natural language, which will help operators.

• Creative Content Generation

If you have played with Chat GPT and other models, you have witnessed its ability to draw, paint, review, and write. Whether you work in HR and need to write some job Specifications or you would like to work on marketing content,

Another approach is to review the pain points and see if they fit in any of the above categories to determine whether a Gen AI solution is possible.

Another way is to look at it from an organizational standpoint:

• Back office: Admin, HR, Legal, Finance, IT.
• Core Capabilities: R&D, Supply Chain, Operations
• Front Office: Sales: Marketing, Customer Service
• Product & Service

How can Gen AI benefit each of those categories? What are the pain points in each category..?

5 - Evaluating Feasibility

Gartner proposes to evaluate the Feasibility as Low, Medium, and High based on three parameters:

• Technical Feasibility: Is the company equipped to implement the use case? Do they have the technology and skills needed?
• Internal Readiness: Is the company culturally ready to enable this use case? Are they open enough?
• External Readiness: Are customers and partners ready to adopt the new Use Cases?

The Gen AI feasibility can also be analyzed based on other parameters such as:

• Skill Sets: Does the company have the talent for this use case? Are the upskilling plan in place?
• Regulatory Consideration: Is there issues with GDPR or CCPA?

6 - Prioritizing Use Cases

Here again, Gartner proposes some exciting tools to map your use cases so that you can efficiently see what part of your business they address and how easily they can be implemented. The example below shows the AI Opportunity Radar for Manufacturing.

But you can also use this typical Impact vs. Effort Matrix template to judge, based on the quadrant location, whether the Gen AI use case you have developed is worth doing now.

Conclusions

I hope these step-by-step action plans can help you decide which Gen AI use case to begin with. Gen AI is emerging as a tool for innovation and efficiency.

My call to action would be to 1) start the discussion with your teams and see how this could make a meaningful impact, 2) Start small, discover your data, do Proof-of-concepts, and 3) as this is a new field and you are discovering, stay agile, adjust the course if needed and based on the feedback and observed value.

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