There has been a lot of excitement and many headlines generated by the recent launch of DeepSeek. And, while the technology behind this latest iteration of Generative AI is undoubtedly impressive, in many ways its arrival encapsulates the state of AI today. That is to say, it’s interesting, promising and maybe a little overhyped.
I wonder whether that may be partly a generational thing. The baby boomer generation was the first to be widely employed in IT and that cohort learned the lessons of business the hard way. Projects had to be cost-justified because technology was expensive and needed to be attached to a robust ROI case. Projects were rolled out slowly because they were complex and had to be aligned to a specific business need, endorsed by the right stakeholders. ‘Project creep’ was feared and the relationship between IT and ‘the business’ was often fraught and complex, characterised by mutual suspicion.
Today, the situation is somewhat different. The IT industry is enormous, the Fortune 50 is replete with major tech brands and other sectors marvel at the profit margins of the software sector. That may all be very well for Silicon Valley and the venture capitalists of Sand Hill Road desperate to find The Next Big Thing. But back in the real world of corporate IT, matters should be seen with more caution, an appropriate level of pragmatism and even a raised eyebrow or two.
Which brings us back to AI. AI is far from new and has its roots all the way back in the middle of the previous century. So far, despite all the excitement, it has played only a moderate role in the business world. The success of tools like Chat-GPT has catapulted it to mainstream attention but it is still beset by familiar issues. It is costly to deploy in earnest, it requires (at least until DeepSeek) enormous compute power to develop and it delivers responses that are often questionable. There are also serious questions to be asked about legal liability and copyright.
A balancing act
We need to strike a happy balance between the boosterism and experimentation inherent in AI today and a healthy sense of pragmatism. We should begin with the business case and ask how AI helps us. What is our mission? Where are our strategic opportunities and risks? OK, now how can AI help us? Today, there is too much “AI is great, let’s see what we can do with it”.
Today, I see AI as a massive opportunity but use cases need to be worked out. AI is great at massive computation tasks that human beings are bad at. It can study patterns and detect trends faster than our feeble human brains can. It doesn’t get out of the bed on the wrong side in the morning, tire easily or require two weeks holiday in the Mediterranean each year. It is surprisingly excellent at a limited number of creative tasks such as making images, music, poems and videos. But it is bad at seeing the big picture. It lacks the human sense of caution that keeps us from danger, and it has no experience of the real world of work that is composed of an enormous range of variables, not the least of which is human mood and perception.
AI today is great at the edge: in powering bots that answer predictable questions or agents that help us achieve rote tasks faster than would otherwise be the case. Robotic process automation has been a useful aid and has changed the dynamic of how the human being interacts with computers: we can now hand off dull jobs like processing credit card applications or expense claims and focus on being creative thinkers.
There are grey areas too. Conversational AI is a work in progress, but we can expect rapid improvements based on iterative continuous learning by our binary friends. Soon we may be impressed by AI’s ability to guess our next steps and to suggest smarter ways to accomplish our work. Similarly, there is scope for AI to learn more about our vertical businesses and to understand trends that humans may miss when we fail to see the forest for the trees.
But we are some way off robot CEOs, and we need to ensure that AI ‘decisions’ are tempered by human bosses that have common sense, the ability to check, test and revert. The future is one where AI and humanity work in concert but for now we are wise to deploy with care and with sensible budgets and the appropriate level of commitment.
We need to watch carefully for the next DeepSeek hit, query it and always begin with old-fashioned questions as to applicability, costs and risk. I note that DeepSeek’s website bears the tagline “Into the Unknown”. That’s about right: we need to maintain a spirit of adventure and optimism but avoid getting lost in a new technological wilderness.
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