AI adoption by companies is gathering pace, but initial use cases naturally tend to focus on optimization and efficiencies around internal use cases instead of novel AI-enabled products, services, and business models. In this Viewpoint, we use examples from a range of industries, exploring why companies should ensure they are positioned to seize long-term, revolutionary, and client-centric AI opportunities.
A year and a half has passed since OpenAI launched ChatGPT and kickstarted the widespread use of generative AI (GenAI). It’s worth remembering that AI itself is nothing new, dating back to the expert systems of the 1950s (see Figure 1).
In fact, AI is a great example of an exponential technology. Confident predictions were made in the 1960s that general AI with greater-than-human intelligence would be available by the end of the 1970s. But there were some “AI winters” ahead in the 1980s and 1990s, initially because of limiting algorithm patterns, and then because of scarce computing power. It was only the confluence of maturing digital and data transformation technologies that led to the GenAI breakthroughs of a couple of years ago and the sharp acceleration we see today.
Yet industry gurus, such as Sam Altman of OpenAI, are keen to remind us that we are still very much in the early stages of discovering the true potential of how AI can disrupt business. In November 2023, the US Census Bureau reported that only 3.8% of US businesses used AI to produce goods and services. The adoption by the IT and telecom sectors, as well as by professional services, is further advanced, as indicated below.
Nvidia reported in 2023 that 38% of telecom companies had been using AI for more than six months, with only 5% either not using or not planning to use it.
Telecoms’ experience with past disruptions like cloud computing (e.g., Amazon, Google) and over-the-top content (e.g., Netflix) is one reason they are ahead of other industries.
Nonetheless, even in the telecom industry, initial use cases focus heavily on business they already know, through optimization (60%), cost reduction (44%), customer engagement (35%), and meeting revenue targets (31%). Examples of early AI applications include customer care enhancement, sales/marketing analytics and personalization, and network optimization. It is also worth bearing in mind that some current claims of AI-driven innovation are really little more than a rebranding of ongoing digitalization efforts.
However, it is clear that AI has disruptive potential way beyond productivity and efficiency. Similar to how digital transformation remade business models over the last two decades by disintermediating the value chain and putting the customer at the center of business processes, AI will lead to new opportunities within and even beyond the current value chain. In this Viewpoint, we look at current, state-of-the-art applications, and what AI will soon bring to business value, not just for efficiency and productivity but also for growth and business model innovation — in other words, AI is evolutionary as well as revolutionary.
If we map current GenAI applications in terms of task complexity and technology sophistication, it is apparent that most of the focus remains on the first stage (content production), where advances in accuracy rates, levels of control, and task coverage are proceeding rapidly (see Figure 2).
In the next few years, improvements in natural language fluency will evolve into the second stage and allow the deployment of more direct, generalized human-machine-human interfaces, enabling the commercialization of new products and services with far greater personalization, interaction, and ever-improving accuracy (e.g., “user in the loop” services).
The third stage, which some optimistic commentators believe could be only three years away, will involve seamless integration between systems, allowing autonomous agents to orchestrate complex tasks and pilot parts of the business. This will have profound implications for business and society.
Today, most companies’ use cases are in the first stage, which focuses on productivity and efficiency (see Figure 3). In the short term, keeping the attention on productivity and efficiency is a good way to experiment while investing in solid technology, talent, and governance foundations. However, some companies are already moving toward using AI to generate new services within the existing business; significantly more disruptive business model transformation is anticipated in the longer term. Below, we look at some actual examples of AI evolution and revolution in practice.
There are already many great case examples of major AI-based productivity and efficiency gains. Of these, Klarna and GitHub are good illustrations of what AI can achieve:
However, these game-changing case examples of productivity and efficiency are not yet as widespread as anticipated. A survey of Fortune 500 companies by Andreessen Horowitz in early 2024 confirmed that the vast majority of use cases were around internal productivity, with most companies still preferring to have humans in the loop before interacting directly with customers. The same survey indicated a huge increase in planned spend on GenAI in 2024 (2.5x the spend in 2023), with budgets equally reallocated from IT, R&D, and business units. Despite these budgetary shifts, just over half of the companies had not yet precisely measured the ROI gain, although it was generally expected to be positive. Around 30% of use cases focused on cost savings, but only 9% on new revenue opportunities.
The current shortfall in demonstrable results is likely because new AI initiatives are often launched as a technology proof of concept, but they do not determine the true nature of the ROI gain (e.g., productivity, efficiency, quality) and do not adequately consider scaling up across the whole enterprise. There is also a lack of foundational capabilities, such as proper data to train the AI engine. Companies are still building applications in-house, given the limited availability of battle-tested enterprise AI apps on the market. Knowing where the weaknesses are, and where to focus AI investment for the greatest impact, is a major challenge for large companies. Developing an AI maturity heat map is one effective way to begin tackling these challenges (see Figure 4).
The heat map uses two dimensions to summarize a company’s AI maturity. On the value side, the heat map helps companies start prioritizing investment and skills development based on greatest impact, looking at the picture from both an external and internal perspective. On the enabling side, the heat map helps companies identify and structure foundational capabilities that are imperative to sustainable success over the longer term (e.g., data sourcing, AI governance, and talent acquisition). As such, it works great as both a blueprint for the AI landscape in a company and as an executive communications tool.
What seems clear is that taking action on AI development and integration — even if it’s only pilots and experiments to start with — is essential for companies to build knowledge and capability. However, in addition to pilots, quick wins, and low-hanging fruit, it is also important to adopt a more strategic perspective that looks further ahead. This helps ensure that the full potential of AI is realized, and companies are not left behind by further sudden accelerations during the coming years.
As stated earlier, AI has the potential to (1) revolutionize business models within companies’ existing value chain and (2) provide opportunities for companies to enter the AI value chain as more than mere technology users. The first point is especially the case when AI can be integrated into novel products and services, opening up new revenues through fully autonomous systems, hyper-personalization, or seamless product convergence and integration. The latter occurs when companies intend to monetize some of the AI capabilities they have developed (not only in-house, but also through M&A, corporate partnerships, and ecosystem investment). As such, they can invest in each step of the AI value chain, which consists of infrastructure (e.g., GPUs, super calculators, data centers), foundation models (e.g., large language models [LLMs], speech models), and applications (e.g., chatbot interfaces, enterprise software).
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