While AI in general has been around since the late 1950s, we have seen a rapid evolution in recent years. We are well beyond Netflix’s recommendation system and social media feeds.
Generative AI, a different kind of AI, is at the forefront of the AI revolution we are experiencing. A generative AI creates new data that resembles its training data. That gives generative AI the ability to produce new content: acting on a prompt as an example of what we expect as an output, a model generates an answer as a continuation of it. In other words, like a person, a generative AI model can craft a response that is coherent, contextually appropriate, and often indistinguishable from human output. In the generative AI models we experience today, like ChatGPT 4 and Claude, users interact with AI through prompts, refining outputs iteratively.
A leap in generative AI is the development of agents that can complete tasks independently, incrementally completing sequences of them, and delivering final products. These generative AI agents will reduce the need for human intervention, allowing the human collaborator to focus on decision-making and higher-level strategy tasks.
Like earlier general-purpose technologies, generative AI’s rapid evolution generates both excitement for new opportunities and concerns about its impact. AI can automate tasks that require intellectual skills, which can be unsettling for workers. Without a doubt, AI will shift the nature of job tasks and create new ones. In other situations, as we have started to see in customer service roles, generative AI agents will fully replace workers. However, companies that master AI innovation (both innovation processes and outcomes) are poised to create great value for the market and for themselves.
If companies want to make the most of AI, they must invest in developing the AI applications that are relevant to their strategy, invest in developing their workforce, and redesign organizational processes.
Schneider Electric, a global energy management and automation company, has prioritized people over technology, establishing clear guidelines and support to ensure the successful integration and adoption of generative AI technologies. This comprehensive approach includes upskilling the workforce and transforming operational processes to maximize the benefits of generative AI.
If companies want to make the most of AI, they must invest in developing the AI applications that are relevant to their strategy, invest in developing their workforce, and redesign organizational processes.
Generative AI Will Transform Corporate Innovation Roles
Generative AI can complete multiple tasks across the innovation process, reshaping the innovation practice and the roles of those responsible for organizational innovation. For example, it helps with the generation of ideas, accelerates the design and prototyping of minimum viable products, and helps in the commercialization of innovations. In addition, generative AI can enhance the collaboration between innovation workers, allowing the innovation practice to accelerate. Innovation executives should pay particular attention to three ways in which generative AI will change the innovation roles. For each, I propose a key strategic action.
- A Shift to Strategy and Citizen Innovation – As generative AI completes more tasks, first routine and increasingly complex ones, innovation workers will shift their focus from executing innovation tasks to strategizing, planning, and directing the AI agents, and using their outputs to make business decisions. Further, more non-innovation workers will be able to act as “citizen innovators” with the support from generative AI, increasing the need for strategic planning and orchestration of innovation closer to the functional areas of the organization.
- Strategic Action: As the innovation roles shift to strategy, establish cross-functional innovation hubs to sieve the collective innovation efforts. These hubs incorporate workers with diverse perspectives and functional expertise. Acting toward clear goals and with clear guides, the team should have the freedom and resources to evaluate and pilot generative AI innovations.
- New Skill Requirements – Innovation workers will face an increasing demand for technical skills to manage their collaboration with AI. They will also need analytical skills to interpret AI outputs, and adaptability, communication, and collaboration skills.
- Strategic Action: Implement A Digital Academy. These learning and development programs will help all workers learn about generative AI, gain confidence using the technologies, explore their application to different aspects of the organizational innovation processes, adapt the innovation practice, and create a culture of experimentation and innovation.
- Ethical and Regulatory Oversight – Innovation workers will have to oversee the ethical and regulatory compliance of the AI and help in the development of AI solutions that meet regulations and ethical norms, accounting for the impact that innovations have on diverse stakeholders.
- Strategic Action: Develop an Ethical AI Innovation Framework. The framework should clarify what uses of AI align with organizational values, regulatory requirements, and social norms. It should also offer guidelines, guardrails, and goals for the use of AI in organizational innovation.
DuPont, a multinational chemical company, launched a digital academy to upskill its employees on analytics methods and technologies. The experiential program helped employees gain confidence using technologies, explore solutions to their job-related challenges, and built their support networks. The Digital Academy helped DuPont promote a culture of data and experimentation.
As generative AI technologies advance and become part of organizational processes, companies need to adjust their innovation strategies and workforce development. Implementing cross-functional innovation hubs, digital academies, and ethical AI frameworks will assist in managing generative AI’s transformative effect on innovation roles and practices. These strategies will help companies optimize their use of generative AI, promote responsible innovation, and stay competitive.
Rubén Mancha is Associate Professor of Information Systems, Operations and Information Management Division, at Babson College.
