What are the right use cases to start with that can accelerate value and help solve the company's biggest problems?

Start by Solving Business Problems

A successful AI strategy begins by identifying a business problem and a sponsor committed to using AI to solve it. The key is to start by defining a business and customer centric north star for your AI adoption. This means working backwards from existing business and customer problems and considering the effect AI can have on them. When organisations focus on irrelevant business technicalities, they can end up with proofs of concept that never make it beyond the technical stage. To get the most from AI investments, organisations need to connect AI to critical strategic areas.

Instead of asking "How can we use generative AI?", the right question is, "What use cases have we got that we need the most help with, and what role could different areas of technology play?" By identifying and prioritising transformation opportunities in line with business objectives, you associate initiatives with key stakeholders and measurable business outcomes. Focusing on use cases that are too challenging or have long term or small benefits can reduce a company’s enthusiasm to invest more, stifling further innovation. On the other hand, starting with use cases that are easier to achieve or have a proportionally faster or higher return on investment can create momentum for further investment.

Prioritise High-Impact Opportunities

To achieve meaningful impact with analytics, successful companies prioritise and map the decisions that will drive the most value. It is important to identify use cases with the greatest business value. Leaders should prioritise AI use cases in terms of business value and feasibility. These use cases often involve tasks that are repeated frequently, have clear inputs and outputs focused on prediction and optimisation, and involve data that can be digitised.

This prioritisation helps ensure AI projects are selected based on their ability to drive tangible outcomes. By starting with high impact use cases, organisations can demonstrate AI’s return on investment early on. Small wins can drive faith in your organisation, as they help people connect to where they could use AI in other portions of your business. This, in turn, helps build the business case for more ambitious visions. For instance, customer service has leaped to the top of the implementation list for many companies because it offers a clear opportunity to demonstrate success.

Consider Your Industry Context

The processes, practices, and regulatory contexts specific to each industry have a large influence on the way companies pursue AI investments. Top use cases can vary significantly by sector. For example, in the consumer industry, larger firms may see better results from investing in AI-enabled value chain optimisation, while transportation companies often focus on cost-saving problems like fleet management. In life sciences and health care, leading use cases focus on areas like AI-assisted diagnoses and smarter drug manufacturing. The financial services industry is seeing value in natural language processing and voice agents. For technology, media, and telecommunications companies, customer acquisition and retention efforts have driven the development of AI capabilities. Meanwhile, government and public service agencies are often focusing AI efforts on back-office activities to create efficiencies through automation.

It is important to develop an AI strategy that can achieve outcomes in a shorter time frame, while also identifying use cases that may require a steeper learning curve. Starting with your organisation's business strategy and key priorities is the first step to realising value from AI and solving the problems that matter most.