The Future According to BCG: How Will 'AI x Innovation' Transform Companies?

As artificial intelligence (AI) undergoes rapid evolution, companies are facing a major opportunity not just for cost reduction and operational efficiency, but for creating entirely new business models. Beth Viner of BCGX, who appeared on the BCG podcast 'The So What from BCG,' views the utilization of AI in three stages: 'Deploy, Reshape, and Invent.' She emphasizes that 'Invent'—that is, 'creating new businesses'—is what will determine the future competitiveness of companies.

In this article, we will examine the impact of AI from the perspective of 'Invent,' explain common misconceptions and points of caution for large corporations, and discuss the future of organizations and leadership.

1. The 'Biggest Opportunity' in AI Utilization

'AI holds great potential for creating new revenue streams and business models for companies. However, many organizations remain focused solely on cost reduction, potentially missing out on this significant opportunity,' points out Beth Viner.

With the spread of Generative AI and Large Language Models (LLMs), tasks that previously required significant human effort, such as analysis and content creation, have been dramatically accelerated, and the scope for exercising creativity has expanded. In particular, the ability to quickly prototype what customers truly want, conduct market tests, and repeat improvements in a short cycle is considered an unprecedented advantage.

In fact, even around Viner, cases are beginning to emerge where 'non-engineer general users are using ChatGPT and Claude to build application prototypes in a short period of time.' This wave represents a massive opportunity not only for individuals and startups but also for large corporations.

2. BCG's Three-Stage Approach: 'Deploy, Reshape, Invent'

BCG proposes the following three steps for companies to utilize AI.

2-1. Deploy: Streamlining Individual Tasks

First is the stage where each employee uses AI tools to automate and streamline parts of their work. Examples include 'using ChatGPT within the company to speed up writing and email correspondence' or 'entrusting some marketing analysis to AI.' The focus here is on having employees acquire the skills to master these tools while managing risks.

2-2. Reshape: Transforming the Organization

Next, moving beyond individual tasks, this is the stage of transforming the functions and processes of the entire organization. This includes shifting specific functions such as 'accounting and finance,' 'supply chain,' and 'customer support' entirely to AI-enabled systems and fundamentally redesigning business workflows. At this stage, it is important to move toward a structure where humans do not need to intervene in every decision.

2-3. Invent: Creating the Future

And the stage that brings truly significant impact is 'Invent,' where AI is used to create entirely new products and services. The goal is to leverage AI's capabilities to find completely new revenue streams that are not constrained by existing businesses. This includes attempts to redefine the fundamental nature of business, such as 'generating new businesses from the data assets a company possesses' or 'dramatically speeding up conventional R&D through AI.'

3. Innovation in the AI Era and Concrete Examples

3-1. Pharmaceutical Industry: Accelerating Drug Discovery

In the pharmaceutical industry, new drug development using AI, especially generative AI, is already underway, and the speed of transitioning from the initial stage to clinical trials has increased significantly. Because it can instantly analyze vast amounts of compound data and list promising candidates, it is possible to drastically shorten the exploration period that previously took years. Getting products to market early leads to competitive advantage for the company, and patients also benefit from receiving innovative treatments sooner.

3-2. Financial Industry: Advancing Credit Models

In financial services, credit scoring and risk assessment have been AI-driven, but going forward, generative AI will make screening models even more accurate and flexible. For example, there are expectations for moves toward financial inclusion, such as opening up loan possibilities to segments that were previously underserved by existing data. However, at the same time, because the risks of misjudgment and regulatory concerns are significant, 'responsible AI operation' is strongly required.

3-3. Media and Content Industry: Lowering the Barrier to Creation

In industries centered on 'content production' such as film, music, and publishing, the evolution of generative AI is bringing about new creative methods and revenue models. While AI makes it easy to produce scripts, generate video, and assist in music composition, leading to the creation of a vast number of works, it also presents challenges regarding copyright and review guidelines. How major content companies incorporate AI to provide new entertainment value will be the key to the future.

4. Misconceptions and Points of Caution for Large Enterprises

4-1. Risks and 'Responsible AI Use'

One of the biggest reasons large enterprises hesitate to adopt AI is the issue of liability and brand risk. Especially in regulated industries like finance and healthcare, there is a strong sense of caution about 'leaving incorrect decisions to AI,' making it difficult to take the first step.
However, Vainer states, 'If you postpone adoption due to risk, startups and other companies will capture the market first. By leveraging the strengths of large enterprises—such as abundant capital and existing customer relationships—while setting up testing and small-scale experiments and establishing a responsible AI governance system, you should be able to move forward without sacrificing speed.'

4-2. Is Acquiring Startups Enough?

The idea that 'since we don't want to take risks ourselves, we can just acquire external startups once they produce results' is certainly one option. It is a strategy unique to large enterprises with financial power.
However, 'acqui-hiring' (acquiring technology and talent through acquisitions) in AI does not always work well. For the talent and culture of an acquired startup to be utilized within a large enterprise, a sufficient 'receptacle' and mindset are required. If organizational integration fails, you have merely bought 'short-term AI technology,' and you will ultimately fail to gain innovation power as an entire organization.

5. Future Organizations and Leadership

In an era where every aspect of business is being redefined by AI, leadership also requires transformation. The following three points are important keywords.

1. Agility
   While maintaining the control unique to large enterprises, it is essential to foster a culture that learns from small-scale experiments and failures.

2. Collaboration & Diversity
   A system where diverse experts such as engineers, designers, business analysts, and legal staff cooperate is essential for AI development and use. Leaders who actively utilize partnerships with startups and open innovation will be the ones who survive.

3. Responsibility & Ethics
   If AI algorithms have biases or errors, there is a risk that their impact will spread on a large scale. Leaders must maintain a strict yet flexible stance on maintaining governance and compliance while maximizing the benefits of the technology.

'Companies should first re-examine their own strengths (assets) and explore what value users are seeking. Then, it is important to follow the process of 'validating ideas → validating business models → validating technical feasibility,' and actually launch small-scale tests in the market,' suggests Vainer.

What she emphasizes is an approach of 'repeatedly learning while scientifically verifying, rather than blindly putting out ideas and waiting for one to hit.' In doing so, risk management, AI ethics, and an organization-wide cooperation system are naturally indispensable.

Now, large enterprises, startups, and individuals all have the same powerful tool: AI. That is precisely why factors such as business model design capability, leadership, and organizational structure will significantly influence success or failure.

Now is the time for companies to move beyond 'Deploy' and 'Reshape' and reach the stage where they have the resolve to 'Invent' with AI.

To that end, it is advisable to be conscious of the following action steps.

• Rapid Experimentation: Launch small pilot projects and verify results in short sprints.

• Responsible Implementation System: Design governance that includes risk assessment and ethical perspectives, and thoroughly conduct internal training.

• Utilization of External Resources: Build a network of startup partnerships and experts, and flexibly use both in-house development and external procurement.

• Continuous Learning and Expansion: Horizontally deploy success stories within the company and foster a culture where new ideas can be tested one after another.

By taking these steady steps, companies will be able to evolve AI from a mere 'means of operational efficiency' into a 'source of new business creation'.

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