Generative AI-Driven Business Revolution: Four Keys to Success for Producing Outstanding Results Part 3: Our Approach to Getting the Most Out of Company-Wide Usage of Generative AI Beginning with Sharing Your Vision

Effectively sharing your company’s vision for the implementation of generative AI and advancing those efforts across the entire organizations and across different areas tends to make it easier for people to clearly perceive the benefits of implementing generative AI. In the survey on the use of generative AI by business men and women conducted by ABeam Consulting, we asked respondents what they believed were the most important points during the launch and the stage of formulating a vision in generative AI implementation projects. The most common responses we received were “building the organizational structures to drive the implementation of generative AI” (73.2%) and “making it clear what our vision was and what we wanted to achieve in using generative AI” (39.9%) (see Figure 1). This shows that it is essential to have a clear vision of what you want to do and to have the organizational structures in place to support that.

We also found that more people felt they benefited from generative AI in cases where it was employed generically across multiple departments, rather than being confined to use cases at specific departments. Figures 2 and 3 show the results of our survey. Defining high-impact respondents as those who have finished implementing generative AI and who saw benefits from using it above what they had expected, and defining as low-impact respondents those for whom the benefits were below what they expected, the charts demonstrate the relationship between the number of departments using generative AI and the number of use cases for the technology across high-impact and low-impact respondents.
Figure 2 shows the relationship between the benefits of implementing generative AI and the number of departments using the technology. Among high-impact respondents, as many as 70.0% use generative AI in four or more departments. This shows that the use of generative AI does more than just streamlining operations. Rather, it has the potential to contribute to organization-wide transformation by promoting knowledge-sharing and cooperation across departments.
Figure 3, meanwhile, shows the relationship between the benefits of implementing generative AI and the number of use cases to which it is applied. As with the number of departments using the technology, 75.4% of high-impact respondents have four or more use cases. By deploying generative AI to a broad range of business domains, companies can derive greater universality for the technology and thus capture greater return on investment.

Based on such tendencies, the keys to success in launching, formulating a vision, and refining use cases in generative AI projects are to improve cross-departmental coordination based on a clear, unified vision, and to optimally distribute resources. The universality of generative AI is most expressed through such company-wide efforts, contributing to the scalability that promotes organization-wide transformation.

If there differing interests and motivations across departments, then, without a unified vision, it will prove difficult to achieve consistent outcomes. This can best be understood through the metaphor of an orchestra. Each department or team is responsible for its own instruments, but without a unified score (the vision), it will be very hard to achieve a harmonious performance (the outcomes). The conductor (the appropriate organizational structure) brings together the whole, and begins to deliver tremendous success by bringing out the power of each individual part. A unified vision and improvements to cross-departmental coordination are thus essential to extending the universality of generative AI company wide and to achieving scalability.

In generative AI projects, making clear the aims and target areas of the project is a critical task for achieving success. In particular, by having a perspective of “incorporating AI from social issues to operational challenges,” companies can set themselves a broader field of view for the direction of their projects, while also building the foundations for a shared understanding among their stakeholders.
Because the key points to consider when implementing generative AI span such a wide range, companies run a greater risk of needing to change plans or of wasting resources and losing efficiency during the project period, if the aims and target areas remain ambiguous. Additionally, companies can seek to originate these key points from social issues such as labor shortages, inflation or climate change, rather than their day-to-day operational challenges. This allows them to bring a broader perspective to their business activities and to organize challenges in a way that ties in to their corporate philosophy.
The following two steps represent an effective, specific approach to clarifying aims and target areas.

By adopting an approach based in social issues, companies can organize industry-specific challenges from a bird’s-eye view. For example, in the financial industry, companies could clarify their challenges by organizing industry-specific issues, such as fluctuations in interest rates and the associated liquidity risks, in combination with social issues such as labor shortages (see Figure 4). By thus refining down issues from the large-scale to the small-scale, companies can build the foundations needed to clarify the issues generative AI should solve.

Next, companies should associate the specific effects that generative AI produces with industry-specific challenges. For example, leveraging the characteristics offered by generative AI in the form of streamlining and automation, companies could organize impacts oriented towards revision of operational processes or the generation of new business models (see Figure 5). Using a matrix, companies can view the impacts of generative AI multi-dimensionally and comprehensively consider the specific ways in which the technology can be applied to industry.

In generative AI projects, clarifying the aims and target areas of a project is without fail the key to success. By adopting an approach that originates from broader social issues and organizing challenges through our two-step process, companies can advance projects that combine a top-down, broad view with business feasibility.