Editor’s note: Wale Omiyale is the senior vice president of market research at Confirmit, a customer experience and marketing research firm.

Are you struggling to keep up with the Artificial Intelligence (AI) chatter? While there’s no shortage of conversation on the impact of automation and AI in MR, you’ll have to cut through the white noise to fully understand how these innovations are really changing the MR landscape. In 2019, 37% of enterprises implemented AI in some form, but what’s the state of AI in MR? Opinions range from grave warnings about how AI will kill MR (think: scary robot takeover) to automation’s potential to transform the MR industry into a brand-new world. 

While automation and the rise of AI-based MR-specific applications improves processes, saves time and reduces costs across many MR functions, it hasn’t entirely transformed the industry beyond recognition. The adoption of new AI tools and techniques has taken a measured path. A look into the roots of AI, the current state of technology and the future reveals the continuing evolution of automation in MR. 

Early steps into AI

Automation in MR has been a slow and methodical process, with an extended incubation period from the 1970s to the early 2000s. Actual MR-specific AI products with varying degrees of readiness for MR professionals have just emerged in the last five to 10 years. Like many other industries, MR technology has evolved on a continuous basis – automating more and more process-based activities over time.

In the past few years, the level of automation capabilities has grown more quickly – moving from simple, process-based work to the deeper, more complex and more cognitive aspects of the research lifecycle. This development presents both the risk of making humans redundant and an opportunity to deliver more results at a lower cost, in less time. For example, predictive analytics, intelligent automated creation of surveys from Word documents and action modelling are now part of MR technology. These AI advances are slowly creating a new “normal” for MR practitioners.

AI solutions gaining traction

With continuing pressure to increase efficiency and speed in all aspects of the MR lifecycle, the introduction of new MR-specific AI solutions has accelerated, delving much deeper into the research process. There’s now greater clarity on the benefits of more sophisticated tools, enabling more agencies to exploit AI beyond operational and process efficiencies and apply it to research product development, analytics and client delivery. 

Knowledge of benefits is driving exponential growth in the implementation of AI tools across all aspects of MR, in areas like predictive analytics, social media analytics and action management. For example, the latest AI techniques such as deep learning neural networks and natural language processing are being leveraged to automatically uncover insights, enabling people to take a deeper dive into the numbers armed with an initial interpretation of analytics data.

Unlike standard text analytics tools that still require businesses to know what issues or trends they’re looking for, tools now exist to automatically uncover concepts hidden within reams of text data. Moving beyond a model of looking at research data within pre-defined category models, organizations are now continually learning with an “automated eye” on what’s changing in the market.

The road ahead

Last year, venture capitalists invested a record $9.3 billion into U.S. AI startups, more than eight times funding levels in 2013. Worldwide spending on AI systems is forecasted to reach $35.8 billion this year. With development spending growing in areas like facial recognition solutions for e-commerce; translation and voice recognition solutions for business; and imaging and diagnostic solutions in the medical field, it’s important to understand what this investment means for the future of AI in MR. While it’s unlikely MR will be significantly disrupted by AI in the immediate future, there’s possibilities for the transformation of key aspects of MR work.

There will be more and more niche AI solutions in MR. Even though these innovations won’t attract mass adoption at the start, businesses must be ready to embrace the potential benefits and safeguard against pitfalls that may pop up as human skills are merged with an increasingly cognitive AI toolkit.

Catalyst, not a replacement 

With AI’s high accuracy rates and strong raw capabilities, one might assume we don’t need people to deliver better, deeper insight – a scary scenario for everyone who works in MR. But AI is actually a catalyst for human knowledge and understanding, not a replacement. AI provides the most appropriate and accurate insights, guiding humans to the answers. The growth of AI tools for deeper learning means people elevate their skills and deliver more accurate guidance and decision-making. Research teams must evolve into a specialist insights hub where data scientists and reports offer strategic business guidance.