It might seem that Artificial Intelligence (AI) has appeared from nowhere over the last 18 months but its deep roots and broad impact have long been felt across global industries.
Arguably what has changed with the rise of Generative AI (Gen AI) is that to fully leverage this technology, companies must embrace both large-scale projects and incremental improvements. This balanced approach - the Big Head and Long Tail - is critical for achieving thorough business transformation with AI.
Do one and not the other and you'll miss out on the full benefits that AI delivers. In this blog we focus on the lesser considered Long Tail impacts.
The 'Big Head': Driving Change with Major AI Projects
Major AI initiatives, such as automating significant production processes, integrating AI into strategic decision-making, or revolutionising customer service experiences, stand out as the most visible and impactful applications.
Projects like these can drastically enhance productivity and give firms a strong competitive edge. However, they involve substantial investment, come with extended timelines, and often require complex changes to company culture and operations.
They are an essential part of an AI strategy though, as the success of large projects can reshape a company’s position within its industry, demonstrating the potential of AI to lead rather than just compete.
But while these large-scale projects are transformative, the cumulative effect of smaller initiatives, or the Long Tail, also holds substantial value.
The 'Long Tail': Leveraging Incremental AI Improvements
On the other end of the spectrum, the 'Long Tail' of AI focuses on smaller, frequent improvements across various business areas.
The concept of marginal gains, which is already well used in business, comes from Dave Brailsford’s approach with the British Cycling team. Here he showed how minor enhancements in numerous areas collectively led to significant success, as demonstrated by a haul of 26 medals, including 16 Golds, across the 2008 and 2012 Olympics.
Whilst the marginal gains approach may have reduced in popularity in recent years, we predict that it's likely to have a dramatic resurgence through the introduction of the latest artificial intelligence technologies. As Brailsford explained, "The whole principle came from the idea that if you broke down everything you could think of that goes into riding a bike, and then improved it by 1%, you will get a significant increase when you put them all together."
Which is highly applicable to the latest AI technologies, particularly Generative AI.
Gen AI, through its ability to process a wide range of information including text, images and speech and generate human-like output, is ideal for use in streamlining tasks like email sorting, expense tracking, or customer interactions. Each may improve outcomes marginally, but together these enhancements can lead to considerable gains when applied at scale across an organisation.
Moreover, enhancing employee productivity through tools like Microsoft Copilot or the daily use of LLMs such as ChatGPT, Claude or Gemini, where employees can transcribe meetings, summarise emails and documents, or brainstorm ideas, deliver lots of small time savings for each employee that at organisation-scale can result in greater overall output whilst maintaining the same staffing levels.
And these smaller AI applications are usually more affordable and quicker to implement than large-scale projects while posing lower risks, making them ideal for companies exploring AI integration. Effective implementation involves the rapid identification of numerous use cases combined with effective training on, and adoption of user AI tools. We'll cover how to do this in a future blog.
Integrating Both Approaches
A strategic integration of both large and small AI projects is necessary for true business transformation. Big projects showcase the extensive capabilities of AI and can deliver massive value in one hit, while smaller projects - following the example set by Brailsford's cycling team - provide incremental benefits and allow for continuous learning and adaptation whilst delivering significant compound benefits.
Through our work with clients to uncover and deploy 'Long Tail' AI use cases effectively, we've helped them successfully identify key areas for incremental improvements and rapidly deploy practical AI solutions that deliver immediate benefits. To ensure you don’t miss out on the massive benefits of AI, book a discovery call with us today.
