In the second AI imperative of our 4 E’s framework, we look at explore, where we consider a methodical approach to prioritising AI use cases for development.

With the AI floodgates officially opened, organisations are exploring its depths to discover the use cases that will uncover valuable pearls of ROI-led innovation. They’re eager to capitalise on AI’s significant potential to optimise processes, improve customer experiences and drive innovation. At the same time, many remain wary of the challenges of navigating an evolving regulatory environment, data readiness and measuring impact on business outcomes.

Our practical, human-centered approach aims to cut through the hype and enable organisations to embrace AI through four imperatives for action: envisionexplore, engineer and expand.

Enabled by CGI's Responsible AI Delivery Cycle

CGI Envision - Set your AI vision

CGI Explore - Evaluate ROI-led use cases

CGI Engineer - Build future-fit and adaptive foundations

CGI Expand - Accelerate value and operate responsibly

In the first of our follow-up series, we focused on envision, where a clear strategy aligned with business priorities factors in security risks, reputational and financial considerations, and regulatory requirements. Next up is explore; exploring and testing tangible, ROI-led use cases.


Diving for pearls

Suppose you’ve done the hard work to create a bold and practical AI strategy, so you’re ready for the next step, which is seeking and experimenting with use cases that bring the strategy to life.

While it may be tempting to start with the most obvious visible opportunities, we advocate a more methodical approach. After all, the best use cases for AI – those that are scalable and can deliver the most significant return on investment – may not be readily apparent at first glance.

Our approach when leading clients through the experiment phase begins with a series of discovery sessions, where we conduct in-depth interviews across the organisation. We focus on understanding each business unit’s operations, pain points, and areas for improvement or innovation.


Prioritising for value

Prioritising AI use cases for experimentation can be tricky, particularly when multiple groups compete for resources to apply AI-powered solutions to their problems. Once again, a methodical approach can ensure proper focus.

We work with clients to conduct Proof of Concepts (POCs) to explore the feasibility and potential benefit/impact of AI solutions. This involves close collaboration with business and technology stakeholders across the organisation to ensure solutions meet operational needs and business objectives. Insights gained from experimentation are applied for refinement and determining the potential for broader deployment.

We advocate a data-driven approach to prioritise and assess each use case through a set of common criteria, including:

  • Technical feasibility (data and models)
  • Potential for utilisation of existing AI (people, process and technology)
  • Alignment to a current maturity model
  • ROI/business value
  • Alignment to strategic objectives
  • Potential risks
  • AI readiness and organisation change management.

We help clients prioritise projects by expected ROI, back up the rationale with proof points, and set short, medium, and long-term timelines for projects in development.

Case in point: We conducted discovery sessions for a US-based mortgage association, across their 11 business units, to identify the most promising 50 potential use cases for transformative impact and strategic value. This was followed by a detailed evaluation of processes to identify and prioritise actionable generative AI use cases aligned to each business unit’s objectives and technological capabilities.

The outcome: A forward-looking analysis of the potential impacts and benefits the proposed initiatives will deliver, tailored to current and future goals. The projection included tangible improvements in operational efficiency, revenue generation, risk management, and overall organisational agility and resilience in the face of evolving market, regulatory, and technological landscapes.


Expect some waves

Exploration never follows a straight line. Just ask James Dyson. He created 5,000 prototypes before coming up with his revolutionary, bagless vacuum cleaner.

Rather than allowing perfect to be the enemy of the good, learnings can be discovered by shifting to an agile mindset as you begin the development phase. During this six to eight-week period, some failures and unexpected detours are to be expected, but you should be willing to move forward even when you’re not achieving 100%. Build minimum viable products (MVPs) quickly, learn from them, and change course accordingly. Try different approaches to solving business problems, including generative AI, automation, or maybe a completely non-AI solution.

Your exploration process will almost certainly reveal the need to address underlying issues, such as data gaps, a change in infrastructure, or a process that needs an overhaul. See these as opportunities, not roadblocks, to driving more efficiency.


Continue to cultivate

The explore phase shouldn’t happen in silos. Active engagement from the business is essential to building successful AI solutions. Whether using in-house developers or working with a partner like CGI to build MVPs and prototypes, ensure end-users are closely and continually involved throughout the process. Only then will you ensure the solution matches the business problem.

Continued stakeholder involvement also helps mitigate the risk of development teams going too far, too fast. The team can gain valuable insights from business and data experts who understand the big picture. These periodic evaluations allow both parties to assess progress and projected outcomes. This gives clients the option to proceed with or stop the project based on evaluation results, enabling the team to address challenges, adjust the direction and ensure ongoing alignment with business objectives.


Ready to collect your pearls?

Putting your AI plans into action can seem daunting, but it doesn’t have to be. By prioritising use cases to explore based on data-driven insights, we help clients minimise guesswork and proceed with confidence. Through active engagement, agile adaptation, and continuous evaluation, we ensure that AI solutions help our clients meet their immediate business needs and pave the way for sustainable growth and transformation.

Ready to find out how CGI can help? Reach out to our AI experts for more information, or continue our 4 E’s series by reading our envision, engineer and expand articles.