Growth is a universal imperative. Sustaining value-creating growth despite turbulence and disruption is hard. The companies that do it best tend to be the ones that discern emerging opportunities first.
When it comes to shaping a business strategy to drive growth, analytics and AI can provide an insight advantage. They can help you find new ways to deploy existing advantages—and the best paths to develop new ones—whether in the core, adjacent markets, or new frontiers.
And it’s not just about discovering opportunities. The discipline of growth and innovation analytics can also help with ideation and accelerating implementation. We focus on four powerful ways to leverage the technology for growth strategy. (See the slideshow.)
Stay ahead with BCG insights on innovation strategy and delivery
Uncover Unexpected Adjacencies
Looking beyond the core business to find new opportunities where you have a right to win is an important growth accelerator. This requires thinking beyond traditional moves such as bringing your existing offerings to new geographies or customer segments. Analytics can reveal more unexpected and potentially attractive adjacencies. For example, if you have intellectual property, mining the universe of global patents can frequently uncover new applications for your technologies.
To receive a patent, inventors must prove to issuing authorities that their inventions are novel. Part of that process is citing the existing patents on which the new invention builds. As a result, mapping the citation networks for your patents and distilling insights with natural language processing (NLP) can be eye-opening. For example, a citation analysis enabled one large national oil company to discover that its patents for polyol esters—which it primarily marketed as lubricants for the automotive industry—were being cited by personal care companies in applications related to the manufacture of skin wipes. This insight helped it identify and explore new applications for these compounds as an emollient in consumer products such as skin care—and resulted in the launch of a line of eco-friendly ingredients for cosmetics and other personal care products.
Identify Emerging Customer Priorities
Businesses succeed by delighting their customers and so staying on top of their needs and concerns is essential to sustaining competitive advantage. And today, with so much of life lived online, there is both a broad range and a vast volume of customer data to explore with AI and analytics. Online forums and social media can be mined for comments about your company or your priority innovation domains. Troves of open-ended responses in customer surveys, online reviews, or call center transcripts can be studied to reveal emerging themes. Even search terms and customer web journeys or app usage patterns can be investigated for clues to evolving customer needs.
It’s possible to mine these datasets for insight into shifting sentiment to uncover the topics most on customers’ minds. What are they positive, negative, or neutral about—and how is this changing over time? What are the most important new themes arising? The answers to these questions can provide valuable input for both strategy and innovation teams seeking to position the company for what’s next.
For example, a leading producer of monosodium glutamate (MSG) sought to sustain business performance while contributing to its commitment to help reduce global salt intake. Through social listening the company confirmed that consumers in many markets were increasingly negative about MSG—and discovered rising interest in low-salt umami flavorings. The insight enabled the company to reduce bulk sales of MSG while sustaining the growth—and improving the profitability—of its core seasonings business via new branded umami offerings, leading to an overall decrease in sodium consumption.
Reveal Early Bets by Competitors
When considering new innovation domains, before companies dive in, they typically dip their toes in the water, leaving detectable signals if you know where to look. AI and analytics can help detect the early ripples.
It’s not always upstarts that change the game in an industry. Incumbents can too—and they often have a richer data footprint to mine. You need to ask:
- What are competitors saying at their Investor Days about innovation and new technologies?
- What do conference proceedings, scientific literature, and company announcements reveal about new fields in which rivals are developing academic relationships and building capabilities via funding, joint research, and new hires?
- Do the social media postings of competitors’ scientists offer clues to new areas of interest?
- Are there signs of new areas of focus in rivals’ patent applications?
Training the most advanced reasoning models on this universe of data can reveal patterns suggesting shifts in strategies and future business models well before they hit the market.
Map Emerging Vectors of Disruption
Disruption happens when you’re not looking, but how and where to look? It could be a new entrepreneurial upstart or an emerging technology with the potential to radically improve your customers’ economics. Previously, this was a laborious hit-or-miss process. Senior staff would network at conferences, question new hires, and try to stay abreast of the literature.
AI and analytics make finding these kinds of “needles in a haystack” early much easier. Our advice:
- Focus AI agents on databases of “smart money” flows or new company formations in your core and adjacent innovation domains and have them cluster the companies into distinct logical groupings.
- Study the clusters and look for anomalies.
- Set AI agents on a quest through recent patent filings and scientific publications to map the landscape of bleeding edge innovation in your key sectors.
Taking a similar approach, we recently helped a new CEO in the med tech sector identify the companies and technologies with the greatest potential to disrupt each of his business lines—and to understand the likely timeframe of the disruption. The insights helped him and his team reshape the organization’s priorities for R&D, M&A, partnerships, and corporate venturing.
Not only are these applications of growth and innovation analytics a valuable way to build strategic advantage, they’re getting a huge boost with the rise of agentic AI. In the past, collecting and cleaning the datasets on which analytics are conducted was labor intensive and thus adding new data could only happen at intervals. But AI agents can now automate data acquisition and cleaning, enabling “always-on” analytics. These agents can also be programmed to alert strategy and innovation teams to emerging trends and anomalies that bear further study. With the tempo increasing, companies not already using analytics to shape growth strategy will be at great risk of falling behind as analytically savvier rivals rush to build leadership positions in tomorrow’s game.
The authors would like to thank Michael Wahlen and Rahul Desai for their help in shaping the article.
