Market research and technology: the future

Creativity-meets-AI

My last post here examined how tech tools can help in an ever-increasing set of research scenarios (checking hypotheses on the fly, qual at scale, easier analysis of ethno data & remote qual analysis). In this one, I look at where this might lead us. 

New skills, new roles, new tasks

So what’s the payoff from all this tech then Simon, I hear you ask?

Tech can free us up to think, not do.

For researchers, the era of Artificial Intelligence may be more about automating tasks, not jobs. You don’t need to be a techno-utopian to envision a future where drudge work is reduced and we have more time to reflect on our data and what it means.

Our skillset needs to evolve to fit this new ecosystem

The World Economic Forum’s report The Future of Jobs highlighted the 3 skills most prized in modern workers:

  1. Cognitive flexibility
  2. Critical thinking
  3. Creativity.

In the future when inspiration strikes on the train to a briefing meeting you’ll be able to use a free AI tool to generate some hypotheses from a “big dataset”, then bang the results into software to create an animated visualisation of your viewpoint… all without breaking a sweat.

Good researchers will be specialists who have an understanding of related disciplines.

Much has been written about “T-shaped people” – rightly so, because it’s a vivid metaphor. In my experience, clients want business solutions and trust you to combine the right tools and techniques to get there.

I’m never going to grasp the finer points of algorithm design or boolean logic. But I might sit across the office from someone who does and chat with them over lunch. And they might provide a breakthrough on my project. Your collective mind works a lot faster.

Good researchers will be hyper-aware when automation starts to erode their skills.

Because when systems fail, a skilful response is required.

We should be conductors harmonising an orchestra of tech, unintimidated if required to turn our hand to different instruments.

In the future I will set my research AI going to collate and theme sources the day I am commissioned on a project, but if the AI fails I have the skills to explore not just Google, but my library, and can call in favours from my peers ahead of the deadline.

Good researchers will be stretched by the advent of AI.

It has been 21 years since IBM’s Deep Blue first beat Garry Kasparov.

As Kevin Kelley writes“The advent of AI didn’t diminish the performance of purely human chess players. Quite the opposite. Cheap, supersmart chess programs inspired more people than ever to play chess, at more tournaments than ever, and the players got better than ever. There are more than twice as many grand masters now as there were when Deep Blue first beat Kasparov.”

Similarly the world’s best medical diagnostician is not a computer or a doctor, but a team of both. There’s a mutuality and complementarity here. Machines have unrivalled processing power.

But lateral thinking and eureka effects do not reside in binary code.

Insight is a deeply human act

Insight is a creative process

Human interpretation is art as well as science. Consider …

body language (like when a participant leans forward and their eyes light-up)

cultural nuance (knowing the subtext when an Englishman says “I’m fine. Thanks.”)

empathy (your niece’s crocodile tears sounding different to real tears)

common sense (thinking ‘hang on a minute’ when Facebook claimed it reached more UK 15-24 year olds than actually exist)

or even hard-won knowledge (having a feel for causality when considering feedback loops).

The upshot? Insight is a creative process.

Your AI <> big data interface can explore ten thousand data runs a day, but it can’t and won’t tell you what it means, how to combine it with human data, or what to do next. Gut-feel, wisdom and working with clients to inspire cultural change aren’t going to be automated anytime soon.

Insight is a deeply human act. Or to put it another way, insight is a hard AI problem.

Maybe that’s the same thing?

So in summary:

  • Let’s look forward to a future where we automate boring tasks, to focus on interpretation and meaning;
  • Researchers will be conductors of an orchestra of tech, weaving together a symphony of platforms in real time to create harmony;
  • Clients will be our patrons, commissioning each opus to their needs and preferences;
  • We’ll need to adapt our skillsets but we should relish this challenge.

Thanks for reading.

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