The focus and determination to make use of Artificial Intelligence (AI) is red hot across all industries. While some race ahead, others like the research sector are watching these early adopters and learning valuable lessons that could help inform their own implementation plans.
Technology and, particularly, the retail sector have been quick to make use of benefits offered by different forms of AI.
Apple’s Siri is a ubiquitous form of natural language processing (NLP) that interprets voice commands and responds accordingly, while online retail often involves automated chatbots supplying customer support.
A study from Boston Consulting Group examined the appetite for AI across businesses. It found that 75% of executives believe AI will enable their companies to move into new businesses.
The research also suggested that almost 85% believe AI will allow them to obtain or sustain a competitive advantage, while more than 60% said a strategy for AI is urgent for their organizations.
Desire is high, but the gap between appetite and execution remains significant. The Boston Consulting study found that just one in five companies has incorporated AI into some offering or process, while just one in twenty has extensively incorporated AI.
AI in research
Within the research community, the need to incorporate AI is understood but use or experimentation are not yet widespread.
A lack of widespread experimentation, however, doesn’t mean there’s no experimentation. In fact, there was a conference last year on this very subject (recordings of the sessions can be found here) and there are several organisations making waves in research with AI and a string of other interesting applications.
One application highlighted in a research paper by InSites Consulting looks at the use of predictive analytics to ensure that market research communities and consulting boards are not disproportionality influenced by strong characters who might skew research outcomes.
Looking further afield, Prague-based response:now is an organisation doing impressive things. It supplies automated market research that creates reports based on machine learning. It already works with Google, Mastercard, and McCann.
Fred Barber, Managing Director of response:now in North America, explained his business’s proposition to Martech Today – 75-80% of the current effort in market research, he said, is in writing the reports.
“It [writing] is costly and time-consuming,” he said. In comparison to traditional research, response:now can deliver in “five days, not five weeks and for 2K instead of 20 (on average).”
So, that’s a heavily increased speed at a much-reduced cost. In those terms, market research can become much more every day. It is something actionable and relied upon by managers and workers, rather than accessed mainly by executives who may or may not build it into strategy.
Even in the two simple examples we’ve highlighted, it’s possible to see how AI can be a disruptive force in the research industry.
Our examples show how research production could be altered, but what we haven’t really touched on (and it’s a subject to which we’ll return to) is the effect AI is likely to have on the way research businesses interact with customers.
Before signing off, we just want to highlight a key finding from a recent 451 Research Study on Current and Future State of Artificial Intelligence and Machine Learning. It says machine learning is set to be the most transformative technology existing over the next decade. It also says that we are approaching an inflection point at which companies that have not integrated machine learning into their offerings will fall behind those that have.
To remain competitive in the research sector, it looks like executives might have to get to grips with a lot of new ways of interacting with the customer.