“Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.
Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.”
The economic potential of generative AI: The next productivity frontier, McKinsey
Your AI Journey – Finding the Right Place to Start
It won’t come as a surprise to learn that the financial sector has been an early and ardent adopter of artificial intelligence. In recent years, financial services institutions and fintech firms have continually pushed the boundaries of innovation.
The digitization of the banking sector might have been slow to get going at first, but in the past decade the industry has been adapting to the latest technologies at breakneck speed. With the renewed interest in AI, spurred by the advancement of generative AI, the finance sector once again finds itself at the forefront of AI integration, exhibiting one of the highest adoption rates when compared to other sectors.
Over the last few months, we’ve spoken to a number of different companies that are either in the financial sector or work with financial software.
When speaking to these people, we constantly hear the same thing: Companies understand the direction the industry is heading and the importance of AI. They understand the need to adapt to change and keep pace with the rest of the industry. They understand how much of an impact AI will have on productivity, efficiency and overall competitiveness. But what’s holding them back is that they simply don’t know where to start.
This is where AI use case identification comes in.
What is AI Use Case Identification?
AI use case identification is used to help companies decide if a given AI technology is worth pursuing and if its impact will be greater than investing in other technologies. AI use case identification goes beyond simply looking at your pain point and finding an AI-powered solution. It is instead about mapping out AI opportunities and deciding which is the highest value use case. This is an important distinction as, when it comes to integrating artificial intelligence into your business processes, the possibilities are limitless and it can often lead to decision paralysis.
Take generative AI for example. Generative AI is chiefly concerned with language models which means that it can be used to tackle any language-related task. But this also means that within your company, any task that uses language is a potential use case.
AI use case identification can also be used to test out a specific AI technology. This might be a technology that a technology partner, stakeholder or consultant has advised upon. However, there might be a significant upfront investment. Often a demo or short-term trial doesn’t provide the insight needed to arrive at an airtight decision. Use case identification is a more scientific approach, based on a 4-6 week sprint with continuous user testing, data gathering and recommendations.
At the end of any use case identification project, a full and detailed report is provided to the company along with recommendations and potential next steps. The report breaks down the initial challenges that the company needed to overcome, potential fallbacks/issues that might need to be resolved, a breakdown of the methodology and auxiliary technologies used, a full detailing of the cost factors and lessons learned, and an overall assessment of how the technology will positively impact the company.
Example: AI Use Case Identification in Action
A FinTech firm had a legacy codebase (an active software system built with outdated technology) that needed updating/refactoring. Keeper Solutions was tasked with exploring all the ways in which GitHub Copilot (an AI-powered code completion tool) could be used to enhance the refactoring process vs using traditional methodologies alone.
Keeper Solutions used an exploratory 4-week use case identification sprint to assess if GitHub Copilot could 1/ improve refactoring efficiency, 2/ enhance code quality and 3/ enhance developer experience. After the 4-week sprint, Keeper Solutions was able to share a number of specifically-tested insights and findings.
Upon completion of the use case identification project, Keeper Solutions was able to suggest a solution that involved the use of GitHub Copilot. This solution provided the client with an opportunity to reduce engineering capacity from 35 to just under 20 developers while improving code quality and increasing development velocity.
Using AI use case identification, the firm was able to dip its toes in the water and test the possibility of a specific AI technology, reducing the sense of unknown and uncertainty. This allowed the company to pursue innovation in a more controlled, low-risk manner.
To review this project in more detail, click here.
