Whilst AI has been topical for some time, its ramifications might have felt nebulous and distant. But, breakthroughs in generative AI, like GPT-4 and ChatGPT, are rocket ruel to this emerging trend. Now, we have AI at our disposal that can meet or exceed the achievements of top students on many academic and professional exams, such as the SAT, GRE, and AP exams.
The sudden onset of these smart AI tools poses a very real threat to the existence of the research expert. Don’t believe me? In moments, generative AI can spout questions for a consumer survey based on prescriptive goals; it can analyze and model large data sets in moments; it can organize and connect existing data, replacing a survey entirely; and it can draw conclusions from complex data sets. There isn’t a stage of the research process it won’t disrupt, from desk research and proposal writing to concept creation and analysis.
And it’s only the beginning; ChatGPT was developed in just two weeks. By the end of 2023, there will be 10 ChatGPTs available to the public. For researchers, the revolution isn’t coming; it’s already here, so you had better catch up fast.
So… Now what?
The idea of the knowledge worker was first developed by Peter Drucker, a management consultant writing in the late 1950s. He defined knowledge workers as those who ”apply theoretical and analytical knowledge acquired through formal training, to develop products and services”. Are we now entering a new age where the knowledge worker is under threat? And what does this mean for those with jobs with knowledge based skills? Do we all need a career pivot out of research? Will research businesses be replaced by machine learning tech giants?
I would argue it doesn’t have to be that way, but we need to be better prepared. There will be organizational, cost and talent implications. We will need to rethink how we structure our businesses, what we charge for different types of work and how we recruit, train and retain our talent.
Reshaping your organizational structure to be AI fit
Insight departments will look very different in the next three years. Historically, the market research process has been notoriously analog and service-driven, dependent on people to do the heavy lifting of building, analyzing, and interpreting large data sets.
Typically, teams have centers of excellence (architects), supported by research operations (builders) that create the underlying structure of the research mechanism and a large number of business unit consultants (owners) translating data into insights and sharing them across the organization. These owners act as project managers, helping to gather and interpret data to aid business decisions. But in the future, AI will master this function.
In order to survive, the role of the owner is going to have to change. Many will need to move into new roles and for those who remain, their responsibilities will change. Diane Yoon, People Director at OpenAI, nicely sums this up when she warns us that “Your job will be lost to someone who knows how to use AI, before it is lost to AI”.
Project managers will have to adapt as the job becomes less about singular data points that relate to specific business units, and more about sitting above all the data. They will be focused on conducting meta analysis of trends over time and using data from the past to inform future decisions.
Those that choose to upskill or pivot could become architects or builders. Architects will connect the dots, creating processes to enable data accessibility and strong data governance within organizations. Builders will be the technical champions of the underlying research process, using platforms to democratize insights to every department across the organization. They’ll work with the platform providers and data architects to put plans in place to maximize the connectivity, power, and agility of the data system.
By 2026, we could see AI shift the balance of the modern insights department from one where “owners” act as project managers and translators of data, to a new version where they sit above and below data, influencing business strategy and democratizing insights across the organization.
Rethinking how you charge clients
Sam Altman, CEO of OpenAI predicts that “AI will cause the price of work that can happen in front of a computer to decrease much faster than the price of work that happens in the physical world. This is the opposite of what most people expected, and will have strange effects.”. Businesses will need to rethink what they charge clients.
We will see downward cost pressures on certain types of work. In the long term, commercial and new business leaders will need to find ways to make money out of projects that can’t be delivered by AI. Richard and Daniel Susskind agree – in their book ‘The Future of Professions’ they predict “a shift from a reactive to a proactive approach to professional work; and the more-for-less challenge”.
Embrace the opportunity of abundant knowledge
Artificial Intelligence equals an abundance of insight. That’s a huge opportunity. Instead of thinking of AI as a threat to the generation of insight, we should look at increases in volume and real time availability of insight as an opportunity. A market flooded with data will need human expertise to bring empathy and connection to insights.
This higher-level human expertise will put the emphasis on strategic thinkers. By taking advantage of the opportunities that AI provides, teams will bring value to the business in new ways. They will be more focused on the big business questions instead of managing projects and grading homework.
We have no choice but to evolve. We’ll need far fewer people to manage data and far more to live above the data. Now, more than ever, it’s vital that businesses take steps to democratize and make full use of the data they already have. This will leave them better prepared for the future and able to take on more exciting strategic challenges that genuinely add value.