Home » Insight Collections » The Evolving Role of the Human Analyst
If you are an analyst reading this, you are likely already using some form of AI to support your work, despite your likely reservations about what this means to your role. If you are a leader, you are likely curious about how it can accelerate your work. In this article, we outline the case for the human analyst and explore where AI can accelerate, not consume the value of the role.
Most large organisations are grappling with the role of AI in their operations, not least in where it can add value to decisions. Market intelligence is the fuel that feeds (or should feed) major decisions as organisations go through periods of change, and this is a significant area of debate for whether, how, and where AI can add the most/least value.
As an AI-powered market Intelligence platform, AMPYLFI is well-placed to explore these issues hands-on with our user community. We are constantly probing the edges of what is possible, while listening the very real concerns of our stakeholders about quality, providence and repeatability of what AI produces.
To benchmark the current state of our tools, we set ourselves a challenge:
Our Challenge: Could our Machine Analyst, with minimal prompting, create an in-depth report on a complex subject, in this case disruptive technologies?
Over 3 weeks we set up our experiment and delivered:
| 8 | 3 | 27k | 1.5 Years | 20 mins | 36 hours |
| Disruptive Technologies | Stages of Maturity | Documents Machine Analysed | Human Reading Time | Machine Reading time | Human + Machine Analysis |
Having spent roughly a week hands-on with the tools and data we came to the conclusion:
Our Conclusion: AI is ready to change the Analyst role significantly, but it is not ready to replace human analysts and won’t be until more general intelligence emerges.
This article dives deeply into the evolving role of the human analyst based on our experiences.
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AI Analysis Strengths
Rather than being a silver bullet for market intelligence, AI gives human analysts a huge advantage. Unlike humans, AI never needs downtime; it’s an ‘always on’ tool that works in real time, 24/7. In a hyper-connected and rapidly changing world, having a tool that can constantly run analysis on the latest sources of information helps teams react as quickly as possible.
AI can also structure complex, unorganised information into easily searched, sorted and analysed content, enabling analysts to understand relevant data quickly and accurately. The fact that it’s scalable as well means it can be used in any location for any number of tasks, extending its value across an entire business.
Ultimately, saving human analysts time from sifting through mountains of data is a significant cost-saving. It frees analysts to focus on their true value-add: turning that data into contextual intelligence that drives decisions.
AI Analysis Weaknesses
Despite the many benefits, just like everything else, AI is not a foolproof solution. AI tools can lose context when asked to analyse larger volumes of content; they’re best when working in shorter sections of thought – though this is likely to change. Generative AI, in particular, produces outputs that can be hard to standardise, making constraining data to a format difficult. Because of these limitations, AI has a tendency to repeat itself, resulting in the need for copy editing and quality checking, which takes time.
However, market intelligence teams can undoubtedly transform complex research tasks from weeks-long projects to actions that can be completed within hours if they consider AI’s strengths and weaknesses and complement these with human support and strategic implementation.
Where Human Analysis Fits
First let’s address the elephant in the room. In short, fear amongst white-collar workers that AI is going to see them out of their jobs anytime soon is unwarranted and driven by hyperbolic discourse. However, while you won’t lose your job to AI right now, the immediate risk lies in losing out to a human adept at using it. The evolution of an analyst’s role is to:
- Guide – setting and tuning the configuration, be that content selection, prompt writing, selection of tools or models
- Create – particularly complex connections, where the machine analyst lacks further information
- Contextualise – bringing the insights back into the research context – is this strategic or operational?
- Derive meaning – estimating things, beyond the realms of the machine analysis to know
- Edit – both in terms of content but also in direction of analysis, pruning branches of inquiry and retrying with new guidance
- Engage – other humans in the process, to further broaden all of the points above and actually influence change
As AI improves, this list of responsibilities will likely change too, in the same way that working on a farm changed with the rise of the tractor. As AI tools are further refined for market intelligence, the role of the analyst will continue to change, and this change will, in our view, be to their advantage.
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