The Importance of Pragmatic Thinking when Adopting AI & ML

In recent months, generative AI (GenAI) has garnered significant attention in the media, generating seemingly human-like content within seconds. GenAI models such as ChatGPT (GPT3 and GPT4) or DALL-E have the potential of bringing unprecedented automation and efficiency to a wide array of sectors. It seems as though businesses must race to unlock the power of generative AI and its nuances in order to stay ahead in this fast-paced era. In this blog post, we offer a pragmatic view on these developments, focusing on three key guiding principles: the importance of understanding business needs when organisations start using AI; the importance of starting small and building iteratively, and the role that “traditional” ML models will continue to play.

Our AI & ML Adoption Guiding Principles:

• The first guiding principle emphasises the importance of understanding business needs when organisations start using AI. As generative AI models like ChatGPT and DALL-E continue to monopolise headlines, it is vital for businesses to identify their specific challenges and objectives to ensure successful AI adoption. The number of potential use cases that can be addressed through AI is seemingly endless. As such, conducting a through needs assessments that reflects the needs of stakeholders and defining “what success looks like” will be key to success. We recommend coming together in functional groups within your business to identify the potential use cases, then focusing on a small subset, with a clear view on the rationale and benefits of embarking on each.
• Secondly, we believe starting small and building iteratively when adopting AI solutions is again a key success factor. Embarking on multiple massive AI projects without a clear roadmap or sufficient experience can be an easy route to wasted resources. Instead, organisations should focus on small-scale proof of concept projects. These allow their AI applications to be tested, validated, and refined. Businesses can therefore learn from their successes and setbacks, scale gradually, and gain business stakeholder buy-in along the way. This iterative and incremental process should be accompanied by a clear understanding of the change management needed for success. One of our recent projects involved building a decision-support system for a technology-enabled logistics provider. While on the project, we spent most of our time working with our client to define business needs and what success looked like, and critically analysing the output of each iteration. Only a small proportion of time actually building the system. We are confident that this, rather than our technical skills, was the key to the success of the project. After all, an AI project shares many elements with traditional technology implementation. In these projects, people and process, rather than the technology itself, are the ultimate driver of success.
• Finally, we would like to stress the relevance of “traditional” machine learning models. While generative AI has been making headlines, it is crucial to recognise that traditional machine learning approaches will continue to play a vital role in various applications. Predictive analytics, pattern recognition, and classification tasks can still be effectively handled by traditional ML models and applied to a variety of business contexts. Oftentimes, these approaches also have a higher degree of “traceability”. This allows the steps involved in the decision-making process to be documented and critically analysed, which we believe is a key benefit over the traceability of today’s GenAI models. There is still plenty of headroom for traditional machine learning approaches and they should not be forgotten in the AI gold rush.