Companies launching the “Reshape” game should keep the following in mind:
Anticipate the impact on your workforce. Individual tasks and responsibilities will evolve as generative AI becomes an integral part of business units; for example, writers on a content marketing team will begin to focus more on editing GenAI output. But the requirements for moving to GenAI are vast. You will need to create new roles and reallocate budgets. And performance reviews should also reflect the use of GenAI.
Combine generative AI with predictive AI and first-party data. Work-centric AI assistants are practical assets when they combine GenAI with machine learning systems and other traditional AI tools, also known as predictive AI. For example, an integrated AI assistant for field maintenance workers would use a predictive model, based on a wealth of proprietary data, to anticipate failures and direct crews to the right repair sites; the same assistant would also use a generative AI model to provide knowledge and on-site repair procedures.
One customer uses this type of combination model to achieve a 30% reduction in repair times. Customer field workers are more productive and their equipment performs better.
At BCG, we see the value of bringing together predictive and generative AI, not only for internal tools for employees but also in our solutions for clients. BCG Fabricq marketing platform leverages both types of AI to power personalization programs, using predictive AI tools for product selection and experimentation as well as GenAI solutions to support campaign automation and high-volume content creation.
Our experience with Fabriq underscores a broader point: the strategic value of embedded AI is real and measurable. We’ve seen striking results for customers even in regulated industries like healthcare, banking, and fintech: 40% more engagement, 80% increase in account creation, and 30% more improving customer recommendation scores.
Regardless of your industry, you should strive for simplicity when integrating GenAI with other AI tools by avoiding duplicate point solutions. You also need to provide accurate and highly relevant results to users while avoiding hallucinations. You’ll also want to balance cost and stability to optimize the time it takes for a solution to respond to a query. A rapid development process, where business and technology teams regularly share feedback on model performance, will help your organization create a solution well suited to your needs.
Business and functional leaders must run the game. These leaders will define a target vision for the company’s use of AI. They will establish guardrails and guide a series of pilots across multiple parts of the organization to identify what works. And they will develop a systematic plan to scale up the most effective pilot projects.
But executives should be wary of the dark side of GenAI’s magic: the risks associated with unintended use, hallucinations and false precisions, for example. And there are productivity risks too: using generative AI for the wrong task is a great way to destroy efficiency. A large-scale BCG study found that workers who used GPT-4 for a task outside the technological competency boundary performed worse than those who did not use the tool.
Experimentation can determine where technology is most effective. It can also help you find where people can complement machines, either through human-in-the-loop feedback models or by supporting the last mile of a process that can’t be fully automated. Your people play an important role in the Reshape game, which only reaches its full effectiveness when one organization focuses the majority of efforts (around 70%, according to our analysis). 10-20-70 rule… on the people.
Invent new economic models
Generative AI is about more than just increasing productivity. This can help you reinvent customer experiences, create new services and offerings, and even create new business models.
Companies are seeking ambitious revenue gains from GenAI. A financial information company is using this technology to transform its core product, selling financial data and analytics, into a conversational insight generation platform for its customers. This offering alone targets up to $100 million in additional net new revenue. The company’s overall GenAI offerings will transform its revenue profile and add significant impact to the business.
In another example, a consumer goods company is building a GenAI-powered conversational assistant to provide customers with personalized diagnostics, trend discovery, product recommendations, and virtual try-on services. The company is just beginning to explore many new direct-to-consumer interactions, interactions that could play a larger role in the overall value chain. (See Exhibit 2.)
