New research from the MIT Center for Information Systems Research (CISR) at MIT Sloan School of Management finds that Generative AI (GenAI) management requires data, analytics, and technology leaders to distinguish between two distinct types of implementations:
The MIT CISR research briefing, titled Managing the Two Faces of Generative AI, examines data, analytics, and technology executives’ early GenAI experiments and the challenges and management principles for implementations of GenAI. According to the paper, GenAI tools include conversational AI systems (e.g., OpenAI’s ChatGPT) and digital assistants embedded in existing productivity software (e.g., Adobe’s Acrobat AI Assistant) that primarily enhance users’ personal productivity, aiding workers in tasks such as summarizing documents, brainstorming ideas, and writing first drafts of emails. On the other hand, GenAI solutions are based on business case-driven development initiatives that address strategic business objectives and create monetary value for specific groups of organizational stakeholders — ideally at scale.
The authors’ recommendations are:
Reduce the allure and risks of BYOAI (Bring Your Own AI) with clear usage guardrails and guidelines. “Providing enterprise-sanctioned access to a select number of GenAI tools creates a safe space for employees to experiment while diminishing the appeal of BYOAI, which often increases the risks of data loss, intellectual property leakage, copyright violation, and security breaches.”
Educate employees by investing in ubiquitous training. “Prioritize establishing effective AI direction and evaluation practices, which involves teaching employees to effectively instruct and interrogate GenAI tools and the underlying models, along with using the tools ethically and responsibly.”
Control costs by standardizing on a select set of vendors. “Providing users with licenses to tools from multiple vendors can quickly become expensive once free trials and early adoption incentives expire. Instead, form a cross-functional team of potential GenAI tool users to help the IT organization determine which tools hold the most potential for your organization.”
Avoid the risk of “shadow GenAI” development by establishing a formal, transparent GenAI innovation process. “Develop clear governance structures, early and consistent stakeholder engagement, and a focus on scalable solutions. This helps dissuade groups of stakeholders from independently pursuing unsanctioned GenAI solutions when employees’ growing interest for new GenAI solutions is not addressed.”
Realize financial value by formulating guidelines for GenAI development decisions. “Make sure to differentiate among the three GenAI development approaches—buy, boost, build — to help teams make informed decisions on trade-offs in transparency, context-awareness, and cost.”
Prevent risks such as exploitation of GenAI model behavior, data leaks, and inaccurate outputs by creating a GenAI vendor partnership strategy. “View GenAI vendor partnerships as ongoing relationships that rely on mutual understanding and long-term collaboration, not one-time transactions. Vendors benefit from direct feedback on organizations’ willingness to pay and insights into how they will use their offerings to create value, while organizations gain from vendors’ transparency, advice, and custom support.”
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Source: MIT CISR