There is nothing as complex as the human brain, capable of performing many complex functions at once. But technology is a catching up, able to automate processes, hasten project timelines, and so much more with machine learning and artificial intelligence innovations continuously improving.
When we mention ‘next generation technology’, this term is usually used to describe innovative technology that isn’t currently used to it’s full potential, or even technology that doesn’t exist yet, but will do very soon. For example, advanced robotics, artificial intelligence (and the singularity), the Internet of Things, quantum computing, extended reality, blockchain, etc. Technology already influencing global trade to a certain degree but still lacking full impact.
To err is to be human, but mistakes have consequences. How can we use next generation tech to streamline research processes, minimise mistakes, and deliver supercharged insights to clients all at the same time?
Fusing Technology and Human Insight
So, with next generation technology currently lacking the impact we need, let’s see what combining next generation technology with human oversight will do to close that gap.
Out of all next generation technologies, artificial intelligence innovation is at an all-time high, with more business intelligence and data analytics software being created and commercialised as we speak. The fuel to this fire underneath AI innovation is necessity; through big data and a growing need for quick insights from clients, we are staring into an ever-building tsunami of data, much more than we could ever handle on our own. We need help, and that is why artificial intelligence is becoming a larger part of researchers’ everyday lives. And that is what makes artificial intelligence the perfect first example when discussing the merger of next generation technologies and human oversight for the benefit of future market research.
The beginnings of artificial intelligence are already embedded into market research processes, in automation integration to make data collection, visualisation, and analysis processes, so insight professionals can focus more of their effort on insight generation and activation.
But we are a long way off creating an artificial intelligence program that can do exactly what the human mind can do throughout all stages of the research process from participant sourcing to insight empowerment, so we rely on machine learning algorithms that are riddled with the biases we put there ourselves without a way to regulate their own actions. That is where human oversight comes in. While we are biased ourselves, we are able to double check our own sight and mitigate any risks those biases might pose, and we can do that with all next generation technologies that work their way into the market research processes — technologies such as:
- Extended Reality (powers revolutionary experiences, and perfect for concept testing research and advanced data visualisation, but can only reach it’ full potential with human insight, interpretation, and analysis).
- Internet of Things (allows for wider access to communication channels, thus wider range of research channels for participants, but it’s up to human insight to decide which channels to use for which research projects and which participants we want to reach).
- Voice Assistants (while they might not be quite right in terms of information security yet, when they do become secure channels of communication they can help us increase the accessibility of market research — but we need human oversight to help create the research tasks to make sure they’re right before they go out)
Balancing the Scales of Oversight
Let me ask you, when was the last time you heard the phrase, “to err is human”? When most people hear that phrase, it’s usually said with an air of humility, asking someone else to forgive an error, as to make a mistake is to be a victim of unavoidable human nature.
Human error is the main cause of security breaches, wrong data interpretation, mistaken insights, and a variety of other damning experiences the insights industry has had to wade through ever since its conception. Zooming out to take a wider look, human error is the cause of mistaken elections, aviation accidents, cybersecurity issues, etc. but also scientific breakthroughs across the world. While some mistakes yield true results, most have dangerous consequences that could have been avoided if we were more careful. To err is human, but in an industry where mistakes have real-world consequences, to err is to potentially cost a business it’s life.
If we stick with the artificial intelligence and automation example, automated processes with next generation technology are the most poignant example of humans trying to make up for their mistakes and can help minimise human error at all stages, but still creating a few errors while trying to fix them. In this example, we perform three crucial roles:
- We must train machines to perform the tasks we need
- We need to explain the outcome of those tasks, the results we desire and the results that are counterintuitive or controversial
- We need to sustain the responsible use of machines at all costs
However, we are the ones creating the automated processes that are meant to supercharge our own capabilities, which means that these automated processes will have errors that we need to fix before they are completely competent at taking over without human oversight. We need to iron out the algorithmic biases, train the AI systems on the best way to interact with humans, and teach how to perform to the best of their ability, all so we can perform to the best of our ability.
The main benefit of combining human oversight with this next generation technology, is that we can catch and fix any bugs that arise before they harm the research process and projects that rely on said technology. But we need to be wary that humans cannot catch every mistake, and when one slips through that is when oversight takes on a whole new, disappointing meaning.
Working in tangent with these automated processes and any next generation technology that is integrated into our operating systems is going to be the best way of creating a cohesive and beneficial research process where we all get what we need: the most accurate, relevant, and impactful insights, as well as streamlined research processes that halve the amount of work needed to produce those insights.
The Benefits to Others
But one thing I still haven’t mentioned throughout all of this, is the benefits of using this combination approach to those outside the industry, and but it’s really quite simple. There are many who value collaboration and the optimised processes that result from this collaboration between humans and next generation technology, especially between humans and artificial intelligence. It allows us to evolve key processes while also responsibly collecting the data we need to make the best decisions.
Through this collaboration, we are able to produce insights faster for stakeholders, generating insights for them to action in real time, making informed, impactful decisions at the drop of a hat that guides them towards success.
This article was originally published on the FlexMR Insight Blog and can be accessed here.