By Bryan Doerr, Executive Vice President of Product and Technology, Ensono A time-tested framework has helped organizations drive value and growth for decades. Can it still provide sound direction amid the groundbreaking disruption of generative AI? FIRST IN A SERIES
As new technologies emerge, society and businesses need to reflect on what they mean in terms of new opportunities and new potential threats. This has always been the case as revolutionary new technologies emerge with the ability to disrupt business—and life—as usual.
Cloud computing, and before that the internet, drove the last big technological upheavals. It appears that AI is now poised to make an even greater impact on business and society. The pace of evolution in the AI world is staggering. New tools and enhancements are constantly emerging, accompanied by experts who offer fresh applications and advantages—particularly for generative AI platforms like ChatGPT and others. But the precise impact of these applications is opaque.
That raises some uncomfortable questions for business leaders: Where do we focus? How do we create value for our business from this rapidly changing technology when the direction isn’t clear? How do we innovate, and where? In short, how do we manage the collision of ideas associated with AI that risks causing decision paralysis or poorly prioritized effort?
Innovation is the process by which businesses turn technologies into value. Successful businesses use innovation to supplement an overarching market strategy. A classic example is Netflix and Reed Hastings who, guided by a vision of delivering tailored video recommendations, developed predictive algorithms based on past viewing and optimized delivery methods. Netflix invested heavily in innovations related to working with the post office to deliver and return CDs and then, as soon as the technology was available, directed innovation efforts to online streaming. Netflix’s innovation efforts were informed by a market strategy, and they continually innovated to increase value.
Often, people and organizations will look at a new technology and ask themselves what they should be doing with it. Where does it fit? What’s the potential? But putting technology before strategy turns the innovation thought process on its head, making it very difficult to orient people’s minds around a specific vision and create the critical ingredients needed to drive value and growth. Sony’s integration of CD technology in its once market-leading Walkman is arguably an example of an innovation that lacked the overarching guide of a strategy rooted in user experience. This approach ultimately created an opening for a more strategic and visionary competitor—Apple, with its revolutionary iPod®—to step into and dominate.
Established innovation frameworks, such as Clayton Christensen’s The Innovator’s Dilemma and Geoffrey Moore’s Crossing the Chasm, have long been integral way markers for enterprises looking to foster a culture of innovation, promote strategic thinking and drive long-term growth and success. They help define and focus the conversation toward specific categories of innovation: new offers, new solutions, new internal processes, new revenue models or new ways to reach customers. When a new technology emerges, these frameworks can help to align and speed organizational decision-making.
AI is clearly one of those technological waves that needs to be looked at through the lens of innovation frameworks. It has been presented as a solution to almost anything and everything for businesses. Companies need to reflect on all the ways that AI might aid their business and support their strategy, but they need to be focused on the most impactful and beneficial applications of this technology. Innovation funds are limited and innovation efforts, to be impactful, must be focused. Using an innovation framework can help form a clearer picture of where AI can be used, where it might provide the most benefit, and where to concentrate efforts to maximize beneficial outcomes while fully understanding opportunity costs.
Adopting a multi-dimensional view of value creation
Mohanbir Sawhney’s The Innovation Radar1 is one framework that helps to provide this focus. Through the Innovation Radar, organizations can isolate and relate all of the dimensions through which a firm can look for opportunities to innovate using AI. These four key dimensions include:
What – The offerings a company creates
Who – The customers it services
How – The processes it employs
Where – The points of presence it uses to take its offerings to market
Each of these dimensions is then broken into twelve categories related to specific business systems or innovation avenues that a company can pursue. These categories are shown in the image on the previous page.
The point of this framework is to highlight the many ways a company can innovate. Company leaders don’t need to create a revolutionary new product to innovate and grow. Instead, they can find targeted opportunities for innovation across a spectrum of business functions and outputs.
1Mohanbir Sawhney, Robert C. Wolcott and Inigo Arroniz, “The 12 Different Ways for Companies to Innovate,” MIT Sloan Management Review, 2006.
For example, a company may focus on the customer dimension, with a specific interest in customer experiences and the journey users take from prospect to new client. Through this lens, they can identify and employ AI-based tools—and develop new processes—to help automate, enhance or even remove specific touchpoints within the customer journey to create an overall better experience. AI chatbots can be used to provide 24/7 customer support. Marketing segmentation tools, likewise, can use natural language processing to understand feedback from customers to create dedicated buyer personas and highly-personalized content journeys.
The key point is this: By focusing the conversation around where and how to innovate using AI through this lens of key dimensions and sub-categories, companies achieve a much more nuanced and impactful strategy for innovation and can guide subsequent investment in a more targeted way.
Broadening the definition of innovation with AI
Innovation frameworks are nothing new. Nor is the desire to focus innovative efforts to maximize impact across various categories of the company. We shouldn’t reinvent innovation practices to make progress with AI. But we should open the aperture wider and think differently about what types of innovation are possible, where it can be done, and what is needed to innovate across the organization.
In the case of AI, companies need data—lots of it. They need to cleanse data for accuracy, organize it properly, make it accessible, and be ready and willing to mine it for insights and opportunities across the business. Improving customer experiences using AI, for example, requires benchmark data about the current state of the client lifecycle—from initial contact through various channels, to sales engagement, contracting and purchasing, delivery, use and support. Drug trial data, manufacturing history, part performance results, environmental conditions and all manner of business information becomes a competitive enabler. In AI-based innovation, data is fuel. (See also, “A new way to conquer the great data divide”)
Considering AI through the lens of disruptive and sustaining innovation is also useful. Using Christensen’s innovation concepts, the innovations likely to arise from AI technology are, paradoxically, just as likely to be sustaining innovations as a disruptive ones. Smaller companies are usually the disrupters that exploit a new technology, but AI’s dependence on proprietary data to be harvested in new, faster ways gives established companies who possess this data the edge if they know how to harness it. Because of that, their innovative efforts will likely be focused in sustaining ways, such as using AI to create new optimization cycle opportunities or fundamentally shifting cost structures for established offers.
But there is also a strong element of disruptive innovation at play here—one based on the democratization of who can innovate, and where. That disruptive element is accessibility. While the technology is incredibly powerful and complex, generative AI platforms are actually incredibly easy to use. In addition, many public sources of data will be an enabler. Because of these factors, the innovations that emerge will not necessarily be just the domain of product teams and companies with private data. Anyone with access to these tools can find ways to streamline and enhance their own jobs, creating a cascading effect of innovation.
Turning the innovation crank faster with AI
Missing the boat is a real risk and concern, both for established and potential disruptor companies. While AI plays nicely within established frameworks like Sawhney’s, the pace and scope of AI innovation is significant. It has the potential to create an entirely new standard of technological progress that will require business leaders to shift their thinking around what technology means to their businesses and how it can be used.
To keep up, companies need to be broad in their interpretation of AI-based innovation opportunities, focused on how they apply those innovations, and expedited in how they roll them out. They must draw insights from their data faster, experiment faster, fail faster and refine their strategies faster. In other words, they need to turn the crank of innovation faster than ever before. This will be a challenge for many businesses. However, as we’ll explore in a future article, it’s a challenge that AI itself is uniquely suited to help solve.
