How to manage two types of generative artificial intelligence

As organizations continue to experiment and take advantage of generative artificial intelligence, leaders are implementing the technology in two distinct ways.

According to a new research briefing by researchers Nick van der Meulen And Barbara H. Wixom At MIT’s Center for Information Systems Research, organizations distinguish between two types of generative AI implementations. Early, widely applicable generative AI tools are being used to increase personal productivity. The latter, tailored generative AI solutions, are designed for use by specific groups of organizational stakeholders.

The research, based on round tables with members of the MIT CISR Data Research Advisory Board and interviews with executives, describes both approaches and highlights the unique management challenges and principles for both.

Widely applicable generative AI tools

Generative AI tools like conversational AI systems and digital assistants built into productivity software are widely applicable by design. They are versatile and their use is generally defined and refined by their users, the researchers write.

“It’s AI for everyone,” said J.D. Williamsvice president and chief data and analytics officer at global animal health company Zoetis, member of the MIT CISR Data Committee. “This is where you bring in external products and privatize them within the company so your data is protected.”

Generative AI tools pose four key challenges for organizations, according to researchers:

1. Since generative AI tools are based on large linguistic models trained to predict the most likely sequence of words in a given context, they often produce common results. Therefore, the quality and relevance of the result depends on the specificity of the prompts entered by the user.
2. Generative AI tools can lack context, contain bias, present false or misleading information as fact, and fail in their calculations. Therefore, users must continually critically evaluate a tool’s results to avoid accepting biases or inaccurate claims.
3. Unapproved, publicly available generative AI tools can pose significant risks, especially when employees use them for business purposes. These risks include data loss, loss of intellectual property, copyright violations and security breaches.
4. Generative AI tools are expensive. Providing users with licenses for tools from multiple vendors can quickly become expensive once free trials and early adoption incentives expire.

To address these concerns, companies should provide their employees with permissioned access to a number of generative AI tools to create a safe space for experimentation. To enable safe and successful use of generative AI, researchers suggest leaders do the following:

• Develop clear usage guidelines. These guidelines should be developed by cross-functional leadership teams comprised of representatives from technology, legal, privacy, and governance interests. The guidelines should specify which tools are authorized and under what conditions, and articulate the associated risks and potential consequences. Williams said mitigating risk, protecting data and ensuring regulatory compliance are essential to any AI governance framework. “You want to be innovative and fast, but you also want to be aware of the risks and secure and respect the data,” he said.
• Invest in training. Organizations should establish AI guidance and assessment practices, including teaching employees to effectively instruct and query generative AI tools, understand the underlying models, and use the tools responsible.
• Standardize with a select set of suppliers. Form a cross-functional team of likely users of generative AI tools to help you determine which tools have the most potential for your organization.

Generative AI as a tailor-made solution

Generative AI solutions are business case-driven development initiatives that address strategic business objectives and create value for specific groups of organizational stakeholders, ideally at scale, the researchers write. Organizations fund these solutions after they meet innovation criteria related to end-user desirability, technical feasibility and commercial viability.

“(Companies) are deploying these solutions in specific functions that perform specific tasks,” Williams said. “In manufacturing, for example, this might involve monitoring processes and products to ensure they are moving in the right direction as they are manufactured. There are many interesting applications here.

Although generative AI solutions share some similarities with other AI initiatives, they present three unique challenges, according to the researchers:

1. As more employees begin to realize the potential of generative AI, organizations risk the development of “Shadow-generating AI”, in which stakeholder groups independently research unauthorized solutions with the help of enthusiastic vendors.
2. A few vendors own and control most of the core models that support generative AI solutions. This makes it difficult for organizations to understand models and their own ability to assess bias and predict model behavior, which can introduce various risks, including data leaks and inaccurate results. Uncertainty about future usage, model performance, and pricing also makes it difficult for companies to estimate the long-term operating costs of generative AI solutions.
3. The value organizations derive from generative AI solutions depends on whether they purchase a solution, upgrade a vendor model, or build their own solution. Depending on the approach taken, there are trade-offs in terms of transparency, context awareness and cost.

Organizations can benefit from generative AI solutions through cross-functional efforts. To succeed with targeted generative AI solutions, organizations can also do the following, the researchers write:

• Establish a formal and transparent generative AI innovation process. Organizations need clear governance structures, early and consistent stakeholder engagement, and a focus on scalable solutions.
• Formulate guidelines for generative AI development decisions. Leaders need to differentiate between different generative AI development approaches to help teams make informed decisions, given that there are different pros and cons when buying, building, or improvement of generative AI models.
• Create a generative partnership strategy with AI vendors. Effective partnerships with generative AI providers are built on mutual understanding and long-term collaboration. This promotes adaptability and continuous improvement, which benefits both parties.

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