Ask business leaders about today's hottest technologies, and one answer will dominate the conversation: generative artificial intelligence (Gen AI).
Driven by rapid increases in AI capabilities and availability, generative AI for business has become an object of fascination for companies across industries. Becoming more familiar with this technology and its ever-evolving potential is a way to engage with the present and future of enterprise technology use.
Learning about generative AI technology isn't as easy as it may first seem. Complexities around the technology include its quick evolution, as well as the hype that has accompanied its rise. It can be hard to grasp the facts amid constant changes and intense promotional cycles.
Executive education represents one way for business leaders to learn how to truly integrate generative AI into their most critical workflows and processes, with courses such as Leading the AI-Driven Organization, Frontiers of Generative AI in Business and the Generative AI Business Sprint showing experts' latest thoughts on the technology. Insights from the MIT Sloan faculty members behind these courses can illustrate the practical role of Gen AI in the corporate world.
What is Gen AI and why does it matter?
Generative AI is a term that signifies a specific part of the artificial intelligence field. Algorithms that fall under this category use vast data sets as input, synthesizing responses based on an automated assessment of that content.
The output of a generative AI program could be text, as produced by chatbots, or it could be multimedia, including images, videos or audio. OpenAI's popular ChatGPT system is the go-to example of generative AI in action. This tool has evolved consistently as Gen AI has risen in popularity, increasing its capacity to comprehend and create new types of files over a series of upgrades.
Generative AI algorithms, when used in a strategic and intelligent way, can become powerful business tools. Knowledge workers who harness generative AI tools effectively can add efficiency to their daily routines, automating common tasks and business processes and opening new opportunities to be productive.
Gen AI as a productivity tool
There's some temptation to treat generative AI as an autonomous system, one that can accomplish work unaccompanied, whether due to the science-fiction connotations of the name "artificial intelligence" or bold promises from software developers. With that said, today's most compelling generative AI use cases tend to be smaller and more targeted in scale.
When used to augment knowledge workers' own abilities, Gen AI algorithms can deliver productivity boosts around existing tasks. A recent experiment by MIT PhD students demonstrated how this process can work, observing how quickly employees can perform their tasks with assistance from ChatGPT.
The generative AI tool helped workers act more quickly and decisively — employees who struggled with aspects of their work were able to boost their performance by drawing on automated help from the Gen AI tool. Those who were already adept at completing their tasks saw decreases in completion time.
Knowledge is power with Gen AI use
One point to keep in mind is that users should understand generative AI solutions to get the most out of them. While the technology is relatively simple — the text-based interface of ChatGPT and similar programs is intentionally user-friendly — truly grasping how and why to apply the technology is critical.
When a business leader takes the time to understand Gen AI's potential and role, there are a few strategic benefits:
- Executives can apply Gen AI to problems that suit its specific abilities, rather than applying it in a scattered fashion.
- Businesses can avoid wasting money on implementations that don't address real needs.
Despite the popular perception of Gen AI as a free or low-overhead technology, carried along by the availability of free-to-use consumer tools, there are costs associated with using more advanced systems.
Training a custom model on a company's own data sets is an intensive process, tied up with fraught questions around keeping that data secure and confidential. Before engaging in such a potentially costly effort, companies should have solid strategic cases that make use of Gen AI capabilities — and those strategies come from a place of knowledge.
Generative AI use cases across business sectors
Some of Gen AI's power comes from its versatility. Generative AI algorithms are essentially blank slates because their output is determined by their input. Organizations across industries can adapt Gen AI for their own purposes by creating data sets that reflect their own objectives and use cases.
Despite the relative newness of the generative AI phenomenon in business, companies have already begun exploring tailored use cases for powerful algorithms. MIT Sloan faculty members have witnessed these efforts firsthand.
The work of MIT Sloan Senior Lecturer and Principal Research Analyst George Westerman and Georgia Tech Professor of IT Management Maryam Alvi highlights some vertical-specific use cases for Gen AI:
- Law: Attorneys spend large parts of their days on manual tasks around assembling information for legal briefs. By automating some of these programmatic jobs, knowledge workers at law firms can free up time for value-adding analytical work.
- Finance: Similar to Gen AI usage in the legal field, analysts at large financial institutions can employ algorithms to automate rote tasks and business processes, putting more human effort into strategic work. They may also use Gen AI as a knowledge dissemination tool.
- Marketing: Knowledge workers in the marketing field can turn parts of their workflows over to a generative AI model. This streamlining, when applied strategically, allows each individual marketer to accomplish more in less time and deliver extra results for their firms.
- Learning: Generating theoretically endless amounts of interactive exercises and other gamified learning content is easier with the help of well-tuned Gen AI algorithms. Applications like Duolingo make use of this model.
The through line across these early use cases is that rather than introducing entirely new paradigms, Gen AI is enabling knowledge workers to do what they already do, simply better and more efficiently. With the most rote, manual parts of their jobs augmented by technology, employees can become more strategic in their work or get more done.
Westerman and Alvi break this down into the key concepts of:
- Reducing cognitive load by offloading rote tasks to algorithms and keeping strategic work in human hands.
- Enhancing cognitive capabilities by applying Gen AI to asking questions and sharing information.
- Facilitating learning and development, using algorithms to adapt to individuals' input and deliver useful knowledge.
Making Gen AI work in a corporate setting can be as simple as identifying high-priority business objectives and applying the technology to reach those goals. Rather than changing employees' whole approach, there is often room to intelligently adapt it with the aid of AI.
Key considerations for companies approaching generative AI
While the general theories behind using generative AI technology in the business world are relatively simple, there are complexities and complications to overcome. Rather than an automatic source of value, Gen AI is another item in a company's technology tool kit, one that must be employed with thought and care.
In addition to their thoughts on powerful early use cases, Westerman and Alvi have highlighted the importance of establishing solid policies and responsible AI frameworks specifically tailored for generative AI solutions.
Ethics and legality of AI use are constant points of debate and concern in the early days of the technology. Companies should have strong frameworks in place to make sure they aren't accepting misleading or biased results from generative AI models, and are using data in a secure, acceptable way.
Beyond the blanket need for secure usage policies, there are logistical challenges around AI. Many of these revolve around the data that acts as the fuel for generative AI algorithms. Companies' chief data officers are viewing the rise of Gen AI with a mix of interest and caution.
A study of CDOs revealed their feelings on data's role in the continued rise and effective usage of generative AI for business. Four-fifths of these professionals feel Gen AI will transform their businesses in the future, with the caveat that they don't want to fully abandon their current data initiatives yet.
As for the challenges around effectively using Gen AI, 46% of the CDOs named picking the right use cases and maintaining data quality. This was followed by the issue of building policies for responsible AI use.
The value of generative AI knowledge and skills for business leaders
Using generative AI models effectively means doing the same things companies have always done — pursuing their key business objectives and competing within their fields — with the help of new tools. Leaders in this situation must thread a needle, updating their techniques to stay competitive without becoming carried away by ideas that don't actually add real value.
This need for strategic knowledge and purposeful engagement with AI has led to extensive conversations about the best way forward. Some companies will transform themselves for the better with the aid of generative AI. Others will either fall behind or end up without results to show for their investments.
Companies take their cues from their leaders. A business leader who is aware of the best practices of Gen AI use — and who is willing to stay curious and update their knowledge — may be ideally positioned to take charge at this transitional moment. Gen AI use may end up being just as much about business transformation and change management as it is about tech implementation.
It's understandable why a CEO today may demand a company develop a strategy to integrate generative AI into daily processes. With so many companies investigating the technology, those that don't do so will inevitably worry about being left behind.
Dealing with these types of instructions in a way that will create real, lasting value for the business can come down to the knowledge level of leaders throughout the organization.
What can you learn about generative AI through executive education?
Despite the fast pace of generative AI evolution, MIT Sloan Executive Education's portfolio has kept pace, with faculty adding many new AI-focused courses that focus on business usage of Gen AI. Since these educational offerings are aimed at organizational leaders rather than undergraduate learners, the focus is squarely on practical applications of the technology, helping participants make an impact on organizational strategy.
The multiple AI-focused course offerings available through MIT Sloan Executive Education allow students to learn directly from experts who are actually using Gen AI in practice. These thinkers understand the latest evolutions in the systems, allowing participants to prepare for the next evolutions of various types of AI.
With so many companies committing to Gen AI, the next divide in business may not be between companies that use it and those that don't. Rather, distance may spring up between companies whose leaders understand the technology, and those lacking this knowledge.
Has your company joined the Gen AI revolution? Leading the AI-Driven Organization, Frontiers of Generative AI in Business, the Generative AI Business Sprint and other course offerings available through MIT Sloan Executive Education can inform your thinking on this vital technology.