Dive Brief:
-
Companies that combine in-house innovation with investments in artificial intelligence (AI) and collaboration with outside partners saw their enterprise value grow an average of 4.2% since 2013, compared to 2.3% for those that did not invest in such things, according to new research from Accenture. Accenture defines enterprise value as a measure based on market capitalization, debt and cash positions.
-
Overall, the research found that just 17% of the 200 companies Accenture evaluated — the Fortune 100 and the Intelligent 100 — are high-performing "collaborative inventor[s]" while more than half of the companies were seen as "observers." Observers with relatively low levels of either in-house innovation or external collaboration on AI. Accenture Research estimates companies that move from "observer" status to "collaborative inventor" could see their firm’s enterprise value increase by an average of 90%.
-
Accenture found companies must converge and integrate technology, data and people to achieve high success. "Success in AI cannot happen in isolation," said Francis Hintermann, global managing director, Accenture Research, in a press release. "Our analysis shows that creating AI innovations requires incumbents to open up their technology, data and talent to work with specialist startups and entrepreneurs. That requires them to transform their innovation strategies and organizational cultures."
Dive Insight:
AI is clearly going to be huge in the enterprise. IDC recently estimated revenue from AI-related hardware, software and services will reach $47 billion by 2020. More than 80 of the world’s 100 largest enterprise software companies (by revenue) will soon also have one or more integrated cognitive technologies in their product line and customer experiences.
Accenture’s study is a good reminder that tech alone is not the solution. To make investments count, companies will need to develop not only the technology, but also their data and people capabilities.
But where do you start? Experts say the most practical applications of AI today are going to be in areas where companies have a lot of historical data and where the nature of the interactions are fairly predictable.
