AI Leads the Way: Building a Next-Generation Business Creation Model | Insights

The advancement of AI technology has sparked a new trend in business development. Traditionally, business ideas were conceived from scratch based on market research and customer needs. Now, there is a shift to a new approach where companies start from their own assets and know-how. Instead of people thinking of ideas, AI produces them, and then employees choose from the lineup of AI-generated ideas. This new approach is becoming increasingly relevant in today’s world. Foundation models, such as generative AI, now enable quick and comprehensive exploration of how to combine and apply assets and know-how, including proprietary technologies, data, and industry knowledge.

This Insight shall redefine the process from idea generation, which starts with company assets, to selection as a new AI-driven co-creation model for new business. This model leverages AI’s information processing capabilities and its ability to create potential business ideas at scale and great speed. In the traditional business development process, the focus was on human thinking influenced by the experience and thoughts of experts. This new approach shifts to focusing on personnel choosing from AI-generated ideas, redesigning the process around human choice rather than human creation. This Insight explores this new approach that improves both the speed and precision of business development.

About the Author

Yuji Shimoda

Director
Akashi Miyata

Manager
Kazuma Taira

Senior Specialist

1. Changes to business development — Shifting to a new age in which “inspiration” is no longer enough

Until now, business creation has relied on the “idea generation capabilities” of talented leaders and experts. That is, even with market research and competitor analysis, ultimately, the key to discovering an idea to create a business was the experience and intuition, or rather, “inspiration,” of an individual. However, this “inspiration-reliant model” has now reached its structural limit. There are two reasons for this.

The first is that the volume and complexity of company assets and know-how have expanded to a scale never seen before. Advancements in digitalization mean that all kinds of information are produced every day. Not only information assets from business activities like customer and transaction data, but also technical assets like products, source code, and development know-how as a result of R&D activities. Therefore, solely relying on the decisions of leaders and the inspiration of teams makes it increasingly difficult to digest this vast amount of information across domains and shape it into business opportunities. The second reason is that the speed of change in technological trends and customer needs has begun to surpass the traditional linear process cycle of information gathering, hypothesis generation, and hypothesis testing. Use of the conventional approach, which sometimes takes years to create a business, now results in a greater risk of missing competitive opportunities, as it becomes harder to keep up with the market trends and regulatory changes.

This Insight focuses on the rapid advancement of a platform-based model driven by generative AI. The ability of AI to semantically analyze unstructured data, such as company documents, meeting minutes, and technical specifications accumulated based on natural language, and then algorithmically extract connections across departments and domains really complements the business generation search by human beings based on their experience and intuition. Previously, discovering relevant information or uniting information across different fields relied on one’s network or chance insights. Now, AI can generate anything between thousands to tens of thousands of ideas within seconds. This makes it much easier to identify potential business opportunities based on a company’s existing assets.

That being said, AI is not a one-size-fits-all solution. Ideas and hypotheses provided by AI are merely examples based on statistical and linguistic possibilities. Aligning them with real customer value and business strategy requires the assessment and decision-making of human beings. Consequently, modern business development requires a shift to a process in which people do not think of ideas but choose from the ideas generated by AI. Choosing strategically meaningful ideas from the vast possibilities and combinations generated by AI and verifying them in short cycles maximizes the potential of company assets while accelerating the speed of business development. As a result, this shift to an AI-driven co-creation business development process will help increase the success rate of your business development efforts.

2. Why is it not used? — Reasons why companies are unable to overcome the assets and know-how “utilization gap”

As outlined in section one above, the “planning phase” in business development already has limitations, relying solely on the inspiration and experience of a specific small group of experts and experienced individuals. Furthermore, despite the existence of vast company assets and know-how, these are not always put to use as the focal point of business generation. This is a problem that many companies face.

Assets, such as insights gained from technical documents created by an R&D department and through customer touchpoints, as well as expertise based on work processes, are isolated within many companies. Meanwhile, even when information is available at hand, it may remain as tacit knowledge held by individuals. Alternatively, it may be publicly accessible but only understandable by a select few. It is common for companies to be aware that they have valuable information but are unable to find it, or that they are unable to utilize it even if they find it.

The reason for this utilization gap is down to several structural inhibitors (see Figure 1). The first is the complexity of information and its sheer level of expertise. Technical information and operational knowledge are recorded based on language and structure unique to the relevant field. This makes it hard to understand for outsiders. Therefore, even if other departments or new business leads access the existing assets, they are unable to quickly understand them, instead often wrongly assuming that there is no usable information. The second reason is that there is no proper framework in place to utilize the increasing volume of information. Advancements in digitalization mean that companies now create tremendous amounts of data and documentation every day. There is, however, no framework in place to use them. If it remains this way, the cost of searching these assets will simply increase, and this information will be deemed practically useless. Specifically, the two issues at play here are poor searchability and ambiguity over the relevance of the information. The third reason for the utilization gap is the speed at which information changes and becomes obsolete. Customer needs and technological trends are changing daily. What’s more, there is a need to regularly review once useful expertise every few years, starting from its basic assumptions. Nonetheless, opportunities for systematically reviewing or reaccessing information are rare. Many assets are therefore left untouched because it’s impossible to tell whether they need updating, or they are left out of date, with no action taken. In this state in which it is not possible to tell which assets are currently usable, the decision-making materials are unclear. This makes it difficult to properly fill the gap between the information to be utilized and its purpose. This ultimately delays the decision-making process and invites wrong judgments.

Inhibitors	Challenges	Main problems and risks
① Level of expertise (difficulties in comprehension)	Technical documents and operational knowledge are written in specialist terms or an exclusive format, making them difficult for other departments to understand	This increases the possibility of information being judged “usable” and not being utilized
② Sheer volume of information (search cost)	A large volume of unorganized data and documents is accumulated, and so a lot of time and effort is required to search for the necessary information	This time and effort spent searching for information increases, slowing down decision-making and operations
③ Speed of change (risk of becoming obsolete)	The reviewing of information cannot keep up with changes in technology and market needs, leading to the tendency of useful but old information being left alone	Decision-making is delayed due to obsolete information, increasing the risk of letting competitive opportunities slip away

Figure 1. Three inhibitors blocking the use of assets and know-how

These structural problems are intertwined in a complex web, with companies unable to use their assets, or the assets age before they have been used. Consequently, not only do these companies lose monetization opportunities that they should have acquired from utilizing their assets, but they suffer “invisible losses” whereby time and costs quietly leak away by examining what to do.

There is, however, a link between these inhibitors. They are all simply too much for human resources to handle on their own. Interpreting meaning, structuring information, searching and organizing large volumes of data, and adapting to change—these are all areas in which AI is proficient.

Utilizing AI means there is no longer a need for people to read, sort, and categorize each document. Analyzing information across sources based on meaning or reorganizing by purpose of theme makes it possible to carry out systematic searching and rediscovery, without relying on someone to happen to notice by chance. In other words, utilizing AI allows companies to turn their overwhelming volume of assets and know-how into something that they can use.

3. Choosing changes everything — Advancements in the business development process

As I mentioned in section two, company assets and know-how are vast and complex, making it difficult to use them for business development as they are. Leveraging the power of AI here allows companies to turn simple data accumulation into a source of competitive advantage.

As one model for next-generation business creation using AI, I will introduce an AI-driven co-creation business development process focused on the idea of choosing. The four steps to this model are setting a theme, generating ideas, choosing ideas, and verifying/improving them (see Figure 2).

Step 1. Setting a theme: Define the search purpose and design the basis of assessment

In addition to clarifying what the business development process aims to achieve, the “setting a theme” stage also involves expressing the constraints and assessment criteria used when choosing from AI-generated ideas. Examples of goals are wanting to create a new service for the construction industry by utilizing untapped company technologies and know-how, or wanting to explore the possibility of a BtoB business in another industry by applying internal technologies. It is also necessary to set elements that will be used in designing the exploration process. Such elements might include: what assets will be subject to AI generation, what constraints are there to AI generation, and how a valuable outcome will ultimately be determined (assessment criteria). Rather than randomly exploring possibilities with AI (idea generation), intentionally defining an exploration scope and restrictions will influence the quality of the generated output.

Step 2. Generating ideas: Use AI to extract multifaceted possibilities

AI is then used to generate ideas based on the set theme. This does not refer to simply using generative AI chat tools to ask questions. In order to perform idea generation suited to the set theme, a framework is built that generates a large volume of unconventional hypotheses and ideas. This framework should utilize prompt engineering while incorporating internal asset information (past business development data, technical knowledge, sales information, etc.) and external references (market data, case studies, industry trends, etc.). The aim of this step is not to produce the “right” answer, but to maximize the coverage and diversity of choices. That is, it is important to create a full lineup of options to choose from.

Step 3. Choosing ideas: Narrowing down ideas based on assessment criteria

Next, the theme is narrowed down from the generated ideas. Here, the ideas undergo a multifaceted assessment based on the predefined assessment criteria, such as business strategy alignment, market needs and growth, and technical and organizational feasibility. This is not simply a process of clarifying and incorporating the business creation goals, as well as determining which business areas will be intentionally excluded. It also includes distinguishing whether these criteria are constantly applied or whether they are only applied to specific business opportunities. This makes the idea selection process more effective.

For example, a hybrid approach is adopted where an initial AI screening of the conditions that will be constantly applied is implemented, and then this is followed by a final judgment made by a human being. This step sees AI generate a comprehensive and multifaceted set of ideas based on available assets. Then, a separate AI automatically conducts an initial screening in line with the aims of the project. From here, personnel choose which ideas are feasible and meaningful. This makes it possible to get or develop useful business ideas.

Step 4. Verifying/improving the ideas: Ideas are improved and a business plan is formulated before moving on to subsequent efforts

For the chosen theme, efforts are made to improve the value hypothesis through research, user interviews, and prototyping. If the hypothesis is successfully refined, a business plan will be created and a product/service will then be developed.

If you examine the generated ideas and determine from the various research, user interviews, and prototyping that you did not get a suitable outcome or would like to examine better ideas, review the assumptions, conditions, and assessment criteria of steps one to three, and then generate and choose ideas again. Rather than simply going from idea generation to selection and stopping there, building on outcomes as “assets that will improve the next idea generation process” will enable a more precise business development process in the future.

4. Benefits and success factors of the choosing process transformation

By deploying an AI-driven co-creation business development process that shifts the focus from thinking to choosing, you can expect to enjoy direct and repercussive benefits from the viewpoints of quality, cost, and delivery (see Figure 3).

AI undertaking idea generation and the initial screening of the ideas gives direct benefits that are immediately apparent. These are a significant reduction in ideation lead times (delivery perspective), as well as a reduction in workshop and research costs that tend to occur as a result of people-based ideation (cost perspective). The generation of ideas by AI frees personnel from thinking based on experience and intuition, making it easier for a broader range of multifaceted ideas to emerge (quality perspective).

Furthermore, as a repercussive benefit, AI taking responsibility for idea generation and the initial screening means that human resources can be shifted to other tasks that generate a lot of value, such as creating hypotheses on business value or exploring potential customers. This provides benefits such as more verification cycles within the same lead time and compressing the overall work time of the project. By clarifying information such as the assumptions and rationale for idea generation and assessment perspectives and criteria for idea selection, it helps to standardize and articulate the business planning process, which tends to rely on particular individuals. The result is that not only can you improve the precision of decision-making with stakeholders during business planning, but you can also narrow down remaining issues to be verified systematically and expect to reduce PoC costs.

To produce such benefits, it is essential to organize clearly set goals and restrictions in advance within the setting a theme stage to maintain a consistent process. Once you begin using AI without clear goals and restrictions, you will reduce the quality of generated ideas. In addition, the ambiguity of selection and verification could make the entire process inefficient and prolong the verification period. Thus, defeating the purpose of using AI in the first place.

By clarifying the goals and assessment criteria within the theme setting stage and applying these consistently throughout the process, it makes it possible to maximize AI’s ability to generate ideas. Maintaining consistency from a clearly set theme and thoroughly executing the steps of idea generation, selection, and verification and improvement are the keys to successfully maximizing the benefits of this AI-driven co-creation process.

横にスクロールしてご確認ください

	Quality	Cost	Delivery
Direct benefits	Generates multifaceted ideas beyond the experience and intuition of people	Reduces various expenses such as workshop and research costs spent on creating ideas	Reduces the lead time of business planning from months to weeks
Repercussive benefits (secondary)	The use of AI advances the standardization and articulation of the business planning process and improves decision-making precision	The use of AI enables the clarification of assessment perspectives and criteria of each process, the narrowing down of remaining issues, and the reduction of PoC costs	Dividing the duties between AI and personnel allows each to focus on set tasks and further reduce the amount of time spent on business planning

Figure 3. Various benefits of an AI-driven co-creation business development process

5. Summary

This Insight introduced a new business development process that shifts the human focus of ideation from thinking to choosing and leverages company assets and know-how. Incorporating AI into the business development process makes it possible to visualize the overwhelming amount of information and its potential. It also enables the building of a foundation for producing more accurate ideas more quickly. However, to turn this model into results, consistency is essential in the design and operation of the entire process. Establishing a consistent perspective from setting a theme right through to AI idea generation, the choosing of ideas by personnel, and verifying and improving the ideas, makes the business development process repeatable and no longer reliant on the inspiration of set individuals.

It is anticipated that future advancements in support infrastructure, such as AI agents, will further automate and advance work such as research, selection, and assessment.

This is exactly why human beings need design capabilities, such as knowing what questions to ask, how to choose, and how to proceed, now more than ever.

ABeam Consulting has a framework that enables us to provide end-to-end support from the planning stage of the business to its monetization. If you need our assistance on any matter, whether it be in establishing the new business development process introduced in this Insight, I thoroughly encourage you to reach out to us.

Insights TOP