Generative artificial intelligence can be intimidating and risky. But many companies implement it practically and efficiently. New insights from the MIT Sloan Management Review show how companies are focusing on small and medium-sized wins while ensuring that powerful AI tools, such as agents that can present choices and make decisions, are used appropriately.
Achieve small-scale transformation with generative AI
The early stages of using generative AI are best described as “extensive experimentation,” according to MIT Professor Sloan. and lecturer The companies are pursue small-scale transformation with generative AI, taking a targeted approach that creates value while minimizing risk and laying the foundation for large-scale efforts.
Webster and Westerman describe three categories of small-scale processing:
- Tasks common to employees in many roles. Large language models are popular for tasks such as summarizing information and documenting meetings. While most companies prefer to license private instances of publicly available LLMs and integrate them with productivity tools already in place, some accept the use of external tools when confidential information is not involved.
- Specialized uses for specific roles and tasks. Companies with a somewhat higher risk tolerance are willing to use generative AI for their business processes. Common use cases include coding, customer service support, guiding the creative process, and creating content at scale. For example, CarMax uses generative AI to summarize customer reviews, with the summaries published on search pages for customers to use. Meanwhile, highly regulated industries such as financial services have deployed AI to generate reports and review contracts.
- Consumer products and applications. E-commerce companies are launching chatbots and offering more personalized shopping experiences. Companies such as Adobe and Canva, both of which make graphic design software, are integrating generative AI tools into their products.
These use cases are the riskiest small-scale transformations because they largely exclude humans from the loop.
Executives looking to get the most out of generative AI should consider their risk tolerance, the ability to scale from the pilot phase, and the need for “fundamental investments,” such as data cleaning and model training, to get AI projects off the ground, Webster and Westerman write.
Read: Generating Value from Generative AI with “Small t” Transformations
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Encouraging innovation at Colgate-Palmolive
Consumer product companies have a history of market research and analysis that long predates the advent of generative AI. Thomas H. Davenporta comrade of MIT Digital Economy Initiativeand co-author Randy Bean explore how Colgate-Palmolive applies technology to proven business practices and generates measurable business value.
An example is quick access to research data. Colgate-Palmolive applies generation augmented by recovery to LLMs that process a treasure trove of proprietary consumer research, third-party data and Google search trends. Instead of poring over a stack of market research reports, employees can use generative AI to query all of the data.
Another source of value is the development and testing of new product concepts. Generative AI systems can help employees produce copy and images for a new concept in minutes. From there, concepts can be tested on digital consumer twins who serve a similar role to in-person focus groups, but don’t feel the fatigue of, say, testing two dozen flavors of toothpaste in a single session.
Colgate-Palmolive hosts its AI tools in an internal hub. To access the company’s AI Hub, employees must complete training covering both responsible and practical use of AI. This focus on skills development has paid off: according to the company, thousands of employees have reported an increase in the quality and creativity of their work thanks to the use of AI.
Read: Generative AI now focuses on innovation at Colgate-Palmolive
Reframing how AI supports decision-making
AI is now capable of generating sets of choices – as opposed to a single “best” decision – and has the ability to explain trade-offs, identify new opportunities and learn from past results. Companies using AI in this way are execution intelligent choice architecturesdefined by researchers Michael Schrage And David Kiron as “dynamic systems that combine generative and predictive AI capabilities to create, refine, prioritize and present choices for and with decision-makers”.
Liberty Mutual uses an AI-driven intelligent choice architecture to help claims adjusters triage incoming calls and resolve inquiries. And at pharmaceutical company Sanofi, AI systems guide managers in optimizing investments and combating sunk costs, a common barrier to abandoning unsuccessful projects.
These are powerful tools. To the extent that they inform and make decisions, they present a governance challenge. Leaders must balance responsible use and oversight while expanding the capabilities of smart choice architectures, writes Schrage, a researcher at MIT’s Digital Economy Initiative.and Kiron, editorial director, research, at MIT Sloan Management Review.
Ultimately, this means that the hierarchy of decision rights within the business will change, with the power to shape the decision-making environment having a greater impact than the power to make decisions outright.
Read: How smart choice architectures are rewriting decision rights