Now that AI is here to stay and proliferate, the question has turned to its adoption, almost inevitably getting posed in terms of size of companies, with, as you would expect, ‘surveys’ by leading consulting companies, who stand to gain in this evolving environment. Those who have followed the birth and growth of the internet and web-based applications and software will recall that this question was asked then, in the early 2000s. however, there is an inconclusive collage of views.
Is it appropriate to pose the question in terms of size? Or are there more fruitful ways of understanding the incentive and the capability to adopt AI platforms and agents? The adoption of anything is a change and thus there is the aspect of understanding all that goes with the process of change management. As anyone who has worked on software projects ought to know, any change in process demands a thorough understanding of how and where in the organization (which means its functions and activities) the change will leave its impact and what the consequences will be and how well you have thought about responding to the consequences. Flexibility to adopt must be supported by a keen sense of anticipation. If you have not thought it through, you cannot blame the change!
A McKinsey survey finds that large organisations are likely to adopt AI more than smaller companies, at least specific aspects of it, with the cut-off at $500 million in revenues. An article in Fast Company, a well-known business online magazine, says that small and large companies are embracing AI. A completely different approach, ‘produced’ by chatGPT is outlined by a post on LinkedIn.
In this context, it is useful to recall an insight from Clayton Christensen’s book ‘The Innovator’s Dilemma’ – if you listened to your large customer, you were unlikely to innovate, because, often, innovation requires process adjustment or complete change, which, typically, large companies are not keen to do. Turning it around, it is arguable that large companies, precisely because they are large (with well-entrenched processes and procedures) may be slow to adopt AI, unless, of course, it is driven by the CEO and senior management in an intelligent manner.
The tech focus
The tech field often tends to lean towards technology per se as the decisive factor. An article in IEEE, Computer Society, January 2025, argues that AI adoption is easy for gigs, start-ups and small companies, but not mid-sized and large enterprises. As they see, “AI, machine learning and generative AI platforms and tools provide an opportunity for smaller companies to solve business problems the same way large companies do—expect faster and cheaper”, in many cases stronger positions than larger companies.
There is a similar view that technology is genuinely agnostic to anything, which means that factors such as size per se don’t make the decisive difference. According to an article in Forbes, “A recent study by Piper Frangos, a researcher at the Hult International Business School, finds AI is rapidly becoming more accessible and applicable to all organizations, regardless of their size and resources”.
Minimum Efficient Scale (MES)
Often, as nature keeps demonstrating, change will leave an impact only if it is of a sufficient scale, which will differ widely across businesses and companies, but each must find the scale at which the adoption will make a difference, like a proof of concept. A March 2023 article in MIT Sloan Management Review offers a different perspective – AI pays off when businesses go all in, itself based on an earlier (December 2022) paper, by Yong Suk Lee and others. According to them, “firms need to be ready to make a significant investment in AI to see any gains, because limited AI adoption doesn’t contribute to revenue growth. Only when firms increase their intensity of AI adoption to at least 25% — meaning that they are using a quarter of the AI tools currently available to them — do growth rates pick up and investments in AI start to pay off”.
Economics teaches us that there is a minimum efficient scale (MES) below which it does not make (financial) sense to operate. Although normally taught in relation to manufacturing, the critical concept can be extended to other areas. It can be used along with identifying where is a good place to start, which operations are most amenable, which will offer lessons to be incorporated in subsequent adoptions.
This is an important point because any process change faces obstacles. Only the naïve and the irresponsible think otherwise. There is BCG report, titled Where’s the Value in AI? based on a survey of 1,000 CxOs and senior executives from over 20 sectors, spanning 59 countries in Asia, Europe, and North America, and covering ten major industries and the participants were asked to assess their companies’ AI maturity in 30 key enterprise capabilities. The survey found many challenges, ‘with around 70% stemming from people- and process-related issues, 20% attributed to technology problems’. Only 10% involved AI algorithms but consuming a disproportionate amount of organizational time and resources. ‘Too many lagging companies make the mistake of prioritizing the technical issues over the human ones’. The survey identified Fintech, Software, and Banking as the Sectors with the Highest Concentration of AI Leaders.
The supply chain angle
The traumatic experience of Covid-19 brough global attention to supply chain disruptions and the complexity in global or regional or even national supply chains, seems to be a ground to argue that AI could widen the gap between big and small companies. Managing complexity often drains resources, which tilts the balance towards large companies, and with the new tariff walls, efficiency in supply chain will become all the more urgent. According to their survey, “conducted among 850 procurement managers in Europe and the US – 62% of respondents believe AI tools like ChatGPT will help procurement perform better and improve decision-making, but 35% are concerned that their role will be replaced by generative AI”. Further, 81% of larger companies are working on AI with external partners or vendors, and 32% are hiring full-time leaders to run AI implementation. The corresponding figures for smaller companies are 42% and 5%.
The critical differentiating factors will industry, company size and specific business goals.
There may well be surprises in store.
