What you will learn:
Organizations are using generative artificial intelligence for “small t” transformations that help their businesses scale. This means building capacity and managing risk at each stage of a three-level risk slope:
- Tier 1 focuses on individual, low-risk tasks, such as email management and meeting summarization.
- Tier 2 applies AI to specific roles, like coding and customer support.
- Level 3 integrates autonomous AI into products and operations.
Under pressure to leverage generative AI, smart organizations are discovering that what works in pilot phases doesn’t always translate to full-scale implementations.
Instead of embarking on a radical overhaul of their core business functions, these organizations are climbing the risk slope, pursuing a series of actions. “small t” transformations that aim for additional value, according to a study led by senior professors at MIT Sloan And which was published by MIT Sloan Management Review.
These smaller transformations are also better suited to managing generative AI risks, including data security, AI ethics, and compliance challenges.
“Smart leaders are taking a much more measured and systematic approach to achieving the big things they want to achieve with generative AI,” Westerman said at the event. a recent webinar that detailed the research. “At each stage, they learn to manage risks, become familiar with the tools, and develop their capabilities to move forward toward these greater opportunities.
Climbing the slope of risks linked to generative AI
Webster and Westerman defined three categories of AI transformation that represent different levels of risk. Here’s how they chart the journey.
Level 1: Individual productivity
This is where most companies fall on the maturity curve. At this level, organizations make generative AI available to employees for basic, low-risk tasks related to their specific roles while keeping a human in the loop during interactions. A common use case is inbox management, such as using generative AI to summarize emails, draft responses, and flag priorities. Employees also use generative AI to produce real-time meeting transcripts and summaries, optimize their daily calendars and automatically schedule meetings, and prepare briefings by getting quick summaries of markets, items, and headcount. Many office tools now include extensive language model capabilities to improve individual productivity.
In more advanced scenarios at this level, companies use generative AI to recast communications in a different voice or to adapt them based on cultural norms. Some large companies, including McKinsey, create company-specific LLMs that give employees access to vast internal intellectual property resources, helping them perform their tasks more efficiently with improved quality.
Level 1 work sets the stage for doing more with generative AI. “These tasks put people at ease and reduce some of the fear,” Webster said. “Then they can move on to Level 2 tasks that begin to transform the way the organization operates. »
Level 2: specialized roles and tasks
At this stage, companies are applying generative AI to specific job or business process tasks, such as coding and data science, human customer support, and low-risk content generation and personalization. Software development is a particularly hot area, with generative AI giving programmers an edge when writing and reviewing code, creating documentation, and analyzing data.
Generative AI is also reshaping some customer service and sales workflows. At CarMax, for example, LLMs are used to summarize reviews in a few hours rather than having multiple workers work for weeks, the researchers said. Other companies generate custom scripts for sales calls or use sophisticated chatbots to handle common customer queries, while more complex ones are routed to human agents.
“A general theme found in Tier 2 applications is collaboration between humans and AI, to find places where AI can support humans and for humans to oversee the work of the AI,” Webster said.
Level 3: Products and processes
This is when companies start adding more autonomous generative AI capabilities to products, customer experiences, and internal operations. Webster and Westerman see companies at this stage as using generative AI as part of a multi-faceted toolkit that also includes different technologies and different people.
With built-in capabilities, organizations such as Adobe, SAP, and Workday are beginning to use generative AI to facilitate rapid content creation, automate marketing campaigns, or provide more sophisticated chatbots that make decisions and perform their work independently.
Organizations that already use AI tools can often start enabling these Level 3 capabilities, Westerman said. “These measures may require considerable capacity development, as well as considerable risk management,” he said. “And that’s why companies are taking a cautious approach to getting to this point.”
Leading the AI-powered organization
In person at MIT Sloan
Register now
Make small transformations with generative AI
New technologies will improve what is possible at each of the three levels. AI agents are an emerging area of interest, with autonomous task execution streamlining workflows and solving new business problems. This technology spans a continuum, from simple AI assistants to autonomous agents that act autonomously based on directives and human input. Agentic AI, the multi-agent version of this technology, will require an “AI manager” to supervise other specialized agents performing individual tasks, the researchers noted.
Although there is a lot of excitement about generative AI and AI agents, some skepticism remains – another reason why a “small t” approach makes sense. Webster and Westerman shared the following recommendations for leaders implementing generative AI tools:
- Don’t treat all of your organization’s problems as nails that can be hammered in with a generative AI “hammer.” Focus on the problems where this technology could be most useful in solving.
- Consider where your company is on the risk slope and get management buy-in to the plan moving forward.
- Don’t impose technology on everyone. Find people who are excited and leverage their enthusiasm and successes to promote change.
The bottom line: take your time. “Building the right strategy does not go hand in hand with the gold rush mentality that is currently prevalent with generative AI,” Webster said. “Take a hard look at the job and the needs of the company, and develop your skills before embarking on bigger changes. »
Watch the webinar: Developing Generative AI – Getting More Value from Small Efforts
This article is based on a webinar and research conducted by Melissa Webster and George Westerman and published by MIT Sloan Management Review.
Melissa Webster is a lecturer in managerial communications at the MIT Sloan School of Management. She teaches oral, written and interpersonal communication; convince with data; teamwork; and leadership. She also studies the adoption and implications of ChatGPT and other generative AI tools in professional and educational domains. His research explores the use of generative AI by knowledge workers and its integration into business education.
George Westerman is a lecturer at MIT Sloan. It helps leaders understand the transformative potential of AI and other rapidly evolving digital technologies. His research-based insights show leaders the questions they should ask themselves and the steps they can take to help their organizations thrive. His studies on digital culture and workforce transformation provide important insights for moving from transformation projects to transformation capability.