Chapter 11: The Role of AI in Innovation

AI-Driven Innovation

The Opportunity: AI enables organizations to use data-driven insights to learn and adapt quickly, fundamentally changing how innovation happens. This creates new possibilities for value creation and competitive advantage.

The Challenge: Innovation is more than just technology. Digital leaders must place AI in the context of a well-defined innovation process that considers feasibility, desirability, and viability to create genuine social and economic value.

Innovation Scenarios and Possibilities

These are the kinds of scenario being envisaged by innovators and futurists as they think about AI and the future of retail, banking, education, entertainment and many other activities we carry out every day. Increasing digital transformation in these sectors has allowed them to consider many such possibilities. They push the boundaries about what is possible and what is not.

By working through these kinds of exercises, innovators can consider:

• What capabilities are provided by the latest technology advances
• How clients behave in different situations
• What kinds of experiences they value
• Where additional investment can increase profitability for companies looking to move forward

In this environment, innovation requires a complex, multidimensional perspective. How can innovation be reinvented for the AI age?

Before looking forwards toward new approaches, consider the challenges faced in innovation and the role it plays in organizational strategy.

Many authors have provided insights into the innovation process and explored the elements of innovation that are critical to success. Far from the misleading 'mad scientist' image of innovation, they emphasize that a vast majority of innovation requires a coordinated team executing a well-structured, disciplined approach. For instance, Peter Drucker, one of the most influential scholars in the field of management theory, highlighted that innovation is not an isolated activity but part of a broader collaborative value-creation process.

Several aspects of Drucker's definition are worth highlighting. First, the goal of innovation is purposeful change with an economic or societal impact. It is the outcome of innovation that guides and dictates the parameters of its success. Second, it is not only the resulting product or service that is important to innovation, but also the approach taken to get there. Innovation is as much about process as it is about ideas. Significantly, Drucker sees the process of innovation as a set of activities providing the basis for a systematic approach that organizations can take to be successful.

Third, the actor in innovation – an entrepreneur – aims to make a financial or societal difference through his or her actions. Hence, the characteristics, experiences and personality of the entrepreneur play a key role in innovation. Drucker's definition was given some years ago, and its perspective frames the role of innovation in relation to wealth creation through a coordinated, well-managed process. In the digital era, this remains an essential perspective on how an organization generates value.

While many of these fundamental ideas regarding innovation were developed some years ago, they are just as important to us today in the age of AI.

A core aspect of any digital transformation is to establish a climate that encourages, rewards and supports innovation. In transforming large established organisations (LEOs) with complex structures and constraints, this is particularly critical – and also difficult to achieve. Taking lessons from successful Silicon Valley start-ups can be helpful, but it is not sufficient to ensure they can overcome the challenges of scale, complexity and inertia frequently experienced in many LEOs.

Experience from large-scale digital programmes has raised many questions about the way innovation is approached today. Are traditional innovation practices effective in today's fast-paced, volatile, digitally driven environment? What are the issues for LEOs as they establish their innovation climate?

Certainly, much of the basis for innovation culture remains unchanged. However, several additional areas are worth emphasizing as critical to establishing a positive innovation culture in the age of AI:

Key Innovation Culture Elements

• Highlighting the challenges and opportunities that require new ways of working
• Introducing practical techniques for exploring new ideas, testing them in realistic scenarios, and defining meaningful approaches to their adoption
• Creating adoption models and pathways that overcome innovation barriers
• Adapting to the organization's history and environment
• Bringing insights from deep knowledge of the domain

With AI, the relationship between innovation and data is becoming even more entwined.

The power of machine learning and data analytics means that innovators can now gain insights from vast data sets, enabling them to make better decisions. It also means that innovation can be more predictive and personalized. AI can help innovators to identify new trends, anticipate customer needs, and even create new products and services. It can help organizations to build new business models and explore new markets.

The challenge is to move from small-scale pilots to enterprise-wide adoption, and that requires a new way of thinking about innovation. Organizations must develop a culture that encourages experimentation, risk-taking, and collaboration between humans and machines. The role of leaders is to create an environment where innovation can flourish and where the organization can adapt and thrive in the age of AI.