Likening the impact of Generative AI (GenAI) to the advent of the Internet and even the Industrial Revolution, a major new study by Roland Berger identifies speed of change as one of the defining features of the “GenAI revolution” that is unfolding before our eyes. A potted history of developments to date nevertheless pinpoints one even more important reason for GenAI’s burgeoning global appeal: the promise of unprecedented gains in productivity.
An in-depth discussion highlights the capabilities of AI as a key factor that will continue to drive the diffusion of GenAI . The amount of data used to feed large language models (LLMs) likewise stands out as a significant way to improve and sharpen the performance of this technology – and therefore as an obvious target for weighty investment by the world’s hyperscalers. But so too does the provenance of this data, as the increasing use of data derived from multimedia sources tangibly enriches both results and the user experience in many areas. On top of these advances, gains in processing power are also enabling the latest iterations of products such as ChatGPT and Claude to more closely approximate human reasoning and even outdo humans in some areas.
AI agents are singled out as a “capability game changer” within an already game-changing domain. Here, the study outlines how these agents work and homes in on the need for more human supervision as AI tries its hand at solving ever more complex problems.
Costs are always an issue, of course, and the study spells out the anticipated trade-off between rising hardware costs (specifically for powerful GPUs) and superior cost-efficiency in future generations of chips. A similar relationship exists between heavier spending on cloud services and improved functionality. Yet while license costs (or model fees) are coming down substantially, GenAI’s voracious appetite for energy to crunch ever more data could fuel what some refer to as an AI energy crisis. Potential solutions to this downside effect are discussed.
On the regulatory front, the direction future developments will take is much less clear. While Europe is (predictably) spearheading moves to regulate the emerging AI space, little or no legislation currently exists outside the EU and a handful of other countries.
How industry experts perceive the projected benefits of GenAI
A second chapter of the study explores what more than a hundred leaders from all kinds of industries expect to see in terms of benefits. As a function of the potential for both automation and quality improvements, a clear picture emerges of where business leaders anticipate the greatest impact from GenAI deployment .
Interestingly, while the clear focus is on customer-facing business functions such as sales, service and support, Roland Berger’s interviewees still see very significant benefits even in those areas where they perceive the least potential. This finding underscores a further critical issue: the need for whole organizations to adapt and boost their (largely still lacking) AI maturity if they are to genuinely reap the handsome rewards of this technology.
Adopt and adapt!
The main focus of the study thus turns to the issue of transforming organizations and getting them “GenAI-ready”. Referencing its own still-unfinished GenAI journey, Roland Berger proposes a catalogue of practical steps derived from lessons learned. This six-point guideline includes the imperative of a clear strategy for the long term, proper governance structures, full-spectrum organizational alignment with the target operating model, and sufficient flexibility to let companies continue adapting to an unfolding story whose last chapter has not yet been written.
The matter of how to implement these proposals – how to adapt the whole organization, in other words – is then rooted in four concrete principles: acceptance of the fact that GenAI “really will get everywhere” and will affect everything in every company; the need for a central, strategic AI unit to avoid uncontrolled and wasteful proliferation; the need to empower people with the right skills; and the need to master the art of managing huge volumes of data in a changing world. Enlightening case studies add texture to the real-world outworking of these crucial principles.
No time to waste!
In closing, the study distills the essence of this profound discussion of GenAI and offers five hands-on ways for companies to start the ball rolling. Above all, one point is made loud and clear: For companies that do not want to be left behind in a race that looks set to outstrip even much of the hype of recent years, the time to act can only be now.
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