AI Transformation That Accelerates Value Creation From Trade-Offs to Trade-Ons: A Roadmap for Genuine AI Utilization and Transformation

Despite this potential, at many Japanese companies, AI utilization remains at the localized and experimental stage. Far from reaping these trade-ons, these efforts are stuck at limited attempts at operational streamlining, with their impact on corporate value still vague. Behind this lie four structural bottlenecks that feed into one another.

Companies lack internal AI personnel to begin with, and there are deep gaps between IT departments (technology focused) and business departments (little interest in technology). This leads to a dearth of “owners” who can instrumentalize AI to lead business transformation. Even if PoCs are carried at “points,” this does not end up in their extension to structural transformation across “surfaces.”

Companies are biased towards ideas that ask, “what can we replace with AI,” taking existing, complex operational processes for granted. Initiatives fail to get as far as redesigning “high-level operations handled by people,” which actually need to be put under the microscope, or fundamentally transforming operating models on the premise of AI, resulting in efforts that begin and end in domains where their impact is limited.

Even if companies implement AI, the vast majority of employees will not be capable of using AI as “a companion who extends their own capabilities.” There are also major differences among individuals in terms of how employees use the technology. While companies mandate trainings and the acquisition of AI-related qualifications, these remain separate from real work, and a state of “learned but not used” has become entrenched in many organizations.

This is where the biggest problem is to be found. Even if companies succeed at implementing AI and reducing work hours, the structural limitation in Japan, where “terminations, reskilling and redeployment are all difficult to implement,” causes companies to turn away from genuine revenue structure transformation because they “cannot see it making enough of a contribution to P/L”. Furthermore, despite, AI investment being by no means inexpensive, companies have not adequately considered strategies for maximizing the ROI on those investments in the form thinking about “where to reinvest the people, time and money saved”.

Thus organizational, operational, HR and financial factors are acting on each other to get in the way of companies extracting the full potential out of AI transformation. However, there are limits to approaches to that attempt to address these bottlenecks through isolated optimizations. What all of these bottlenecks have in common is that they cannot be solved through “isolated measures,” and that they stem from the lack of a “central driver” that can store and continue to develop knowledge companywide. So what sorts of mechanisms would companies need to overcome these structural challenges? In the next section, we will propose an approach that treats AI Centers of Excellence (CoEs) as an answer.

Because the domain of AI application extends far and wide, it is essential for companies to set priorities. ABeam Consulting champions portfolio management along the twin axes of “comprehensive ROI (including trade-on value)” and “ease of implementation,” in order to ensure not only the pursuit of idealized value (trade-ons) but also dependable execution.
When evaluating the “return” (the numerator) in ROI, it is important to judge not only based on immediate “economic value,” but to also take into account “strategic value.” Economic value refers to clear quantitative outcomes on the P/L arising from operational streamlining or reduction in headcount. Strategic value signifies things like building competitive edge or averting business risks (minimizing downside). While not all of the “trade-on effects” of AI can be quantified, incorporating strategic value allows companies to take such value into account as part of their criteria for making investment judgments.
It also needs to be noted that, while attention tends to fall on the personnel and contracting costs required for AI implementation measures, the biggest part of such investments (the denominator) is actually the inference costs (operating costs, including generative AI token billing). Companies must design sustainable operating models by incorporating such costs ahead of time.
Companies should also evaluate “ease of implementation” holistically, including maturity of technology (whether it is deprecated or cutting-edge), how well prepared the data is, the barriers to changing operating processes.

Based on such evaluation axes, Japanese companies should follow these three steps to advance the implementation of AI.

The first phase is one in which companies seek to earn “capital” and “trust.” “Trust” refers to the fostering of confidence in the use of AI among people on the ground and among leadership by building up experiences of success and accumulating smaller results. The technical barriers involved in domains such as sales support and corporate operations, for example, in the automation of screening in purchasing and procurement, have come down dramatically thanks to the use of generative AI. Companies should seek to produce early results in these areas (reducing costs or improving the quality of operations), thus not only increasing momentum internally for AI transformation, but also securing investment capital for subsequent endeavors.

The next step is to attack the “core” of management. Taking the manufacturing sector as an example, real competitive edge lies in enhancing production planning (SCM) and factory operations in line with demand forecasting. While these tasks are high difficulty in terms of data linking and on-the-ground coordination, they are also core to the “trade-ons” that combine growth with order. Companies must invest the capacity they free up through quick wins here to achieve structural transformation in these areas, even if it takes time.

Companies should then expand on and deepen the domains tackled in Step 2 by continuing to repeat organizational, operational and personnel “learning cycles.” They should seek to continuously improve on these trade-on effects, while regularly revising their overall portfolios, premised on change in technology and the business environment. What is important here is not treating the use of AI as a “project to complete,” but, rather, incorporating it into the mechanisms that regularly re-evaluate and redistribute resources as management agenda items.

Following on from this, R&D and other domains of high uncertainty should be managed under a separate rubric as “bets” aimed at future game-changing potential. At the same time, it is important for companies to have the courage to “say no to” areas with low ROI and where implementation is difficult.

What matters is the fact that neither simply “doing what you can (easily)” and “pursuing the idea (high ROI)” are enough. Drawing up a path to scale the heights of realizing ultimate trade-ons (the Strategic Core) while securing your footing in easy-to-achieve domains is the responsibility of leaders and staff involved in AI projects. This is also is key to consolidating AI as a management OS (see Figure 4).

AI is no longer just an application. It has now become a “management OS” and a “machine for accelerating value creation.” However, this does not mean that just implementing AI will automatically deliver results. As shown in this Insight, designing “learning cycles” that combine the four key areas of organization, operations, personnel and finance, as well as strategic deployment of resources with clear prioritization of execution from an ROI perspective, including trade-on effects, are critical for accelerating value creation through the use of AI. The time has come for Japanese companies to chart the course for their next phase of growth while learning from the “trade-on style” use of AI demonstrated by leading companies.

ABeam Consulting will continue to seek to help clients accelerate value creation and achieve speedy and reliable business transformation through the provision of consulting services aligned to each individual company’s goals and stages of development.