Companies undertaking Reshape initiatives should keep the following in mind:
Predict the impact on your employees. As generative AI becomes integral to business units, individual tasks and responsibilities will change. For example, content copywriters on her marketing team will begin to focus more on editing GenAI's output. However, the demands associated with the transition to GenAI are far-reaching. New roles need to be created and budgets reallocated. Performance evaluations should also reflect her usage of GenAI.
Combine generative and predictive AI with your own data. Job-centric AI assistants are a useful asset when combining GenAI with other traditional AI tools, also known as machine learning systems and predictive AI. For example, an integrated AI assistant for field maintenance workers uses predictive models based on rich proprietary data to predict failures and direct workers to the appropriate repair site. The same assistant also uses generative AI models to provide on-site knowledge and repair instructions.
Clients have achieved a 30% reduction in repair time using this type of composite model. Our clients' field workers are more productive and their equipment is more capable.
At BCG, we see the value in integrating predictive and generative AI not only in internal tools for employees, but also in solutions for our clients. BCG's Fabriq marketing platform utilizes 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. Use the.
Our experience with Fabriq underscores the broader point that the strategic value of integrated AI is real and measurable. We have also seen significant results for our clients in regulated industries such as healthcare, banking, and fintech, including a 40% increase in engagement, an 80% increase in account creation, and a 30% increase in customer recommendation scores. Ta.
Regardless of the industry, you should strive for simplicity and avoid duplicate point solutions when integrating GenAI with other AI tools. It must also prevent hallucinations and provide relevant and accurate output to the user. You will also need to balance cost and stability to optimize the time it takes your solution to respond to queries. A fast-paced development process in which business and technology teams regularly share feedback on model performance helps organizations build a solution that fits their needs.
Business and functional leaders must lead the strategy. These leaders define the company's desired vision for AI use. They establish guardrails and lead a series of pilots across multiple parts of the organization to identify what works. Then develop a systematic plan to scale up your most effective pilots.
But leaders must be wary of the dark side of GenAI magic, including the risks associated with unintended use, hallucinations, and false accuracy. There are also risks to productivity. Using generative AI for the wrong tasks is a huge way to destroy efficiency. His extensive BCG study found that workers who used his GPT-4 for tasks outside the limits of the technology's capabilities performed worse than those who did not use the tool.
Experimentation can determine where technology is most effective. It also helps find where humans can complement machines, either through human-involved feedback models or by taking over the last mile of processes that can't be fully automated. Staff is a big part of his Reshape efforts, and full impact can only be achieved if organizations focus most of their efforts (about 70% based on the 10-20-70 rule) on people.
invent new business models
Generative AI is not just about increasing productivity. It helps you reinvent the customer experience, create new services and products, and even build new business models.
Companies are pursuing ambitious revenue growth with GenAI. A financial information company is using this technology to transform its core product of selling financial data and analytics into a conversational insight generation platform for customers. We aim to generate up to $100 million in net new revenue from this service alone. His entire GenAI product suite at the company will transform revenue profiles and have a significant impact on enterprises.
In another example, a consumer goods company is building a GenAI-powered conversational assistant that provides customers with personalized diagnostics, trend discovery, product recommendations, and virtual try-on services. The company is now beginning to explore a number of new direct-to-consumer interactions, interactions that could play a larger role across the value chain. (See Exhibit 2.)