In Paul Hudson’s blog ‘3 Ways to Embed Insights into Stakeholder Organisations,’ he states that ‘driving engagement and action from research is one of the most important, yet most difficult steps of the research process.’ Paul argues that by leveraging agile frameworks, real-time data, and frequent collaboration touchpoints, research can deliver frequent and impactful insights that are both timely and actionable. It’s easy to say that this should be obvious; however, while methods of data collection and analysis have all become increasingly more streamlined and accessible, there is a peculiar inertia in terms of the industry’s willingness to alter and evolve its reporting processes.
Data collection is quicker and more efficient than ever before, with most sample groups easily reached and integrated into research in a matter of days or, at most, weeks. Some are even leaning into the dystopian world of AI consumer personas. It’s easier than ever to run the recruitment/sampling, data collection/fieldwork, and analysis stages of a research project to a quality that can be considered as ‘sufficient’. You’re not getting ‘great,’ or even particularly ‘good,’ out of simply streamlining everything to the most quick and automated solutions possible, but you’ll get ‘sufficient’. Bargain basement AI solutions are, for better or worse, going to be the approach of choice for over-worked and over-stressed research buyers who need a quick and cheap answer to a question.
Software tools and AI have also made data analysis increasingly quick and simple, vastly lowering the barrier to entry in terms of training, expertise, and experience. AI text summaries, quantitative analysis software, and many other tools mean that someone with no research background can deliver ‘passable’ quality work at the push of a couple of buttons.
The role of the researcher, then, is increasingly to ensure the right questions are being asked, to the right audience, in the right way. Updating our reporting methods to better encourage collaboration and iteration is key to the success of this new reality.
The Evolution of Research Reporting
Despite the advancements in technology and methodologies for research, there seems to be a stubborn resistance within the industry to fully embrace new tools and processes for insight delivery. This resistance isn’t entirely unwarranted; giving up tried and tested methods, especially when the most senior stakeholder in the room is likely to be someone who has delivered hundreds or thousands of PowerPoint-based debriefs in their career, can feel like a big risk.
However, I would argue that using various tools and approaches to speed up every other stage of research, but then spending an age building a PowerPoint report (replete with little icons and formatting that no one is ever going to be actively invested in), is counter-intuitive. If research analysis is now taking half the time that it did previously, why take the extra time on an inefficient report?
There’s an element of ‘masking’ to this: Hide the ease and speed of the research process behind an output that makes it look more difficult, time-consuming and, therefore, costly.
There’s also a ‘time and a place’. That PowerPoint report and debrief have their own value and benefits (although are also frequently where a good researcher falls down and fails to deliver impact to convince the audience of the value of their insights).
In traditional research, value was most often found in the deep, detailed analysis conducted by experienced researchers. These experts brought not just technical skills but also contextual knowledge and interpretative abilities that can’t easily be replicated by AI.
As more and more clients opt for the ‘sufficient’ approach to analysis, prioritising speed over depth, agencies and client-side researchers still have a responsibility to ensure that reporting is not only efficient but also still produces actionable and impactful insights.
Leveraging Agile Frameworks
Paul’s suggestion to leverage agile frameworks is particularly compelling. Agile methodologies that prioritise flexibility, collaboration, and customer feedback, can be effective in research contexts where the actual fieldwork and analysis processes are automated. By breaking down the research process into smaller, iterative cycles, researchers can continuously integrate feedback and make real-time adjustments. This not only enhances the relevance and timeliness of the insights but also ensures that the research is aligned with the evolving needs of stakeholders. This synergises well with the ‘lower-cost, lower-input’ options of AI and automated research, as the researcher is still adjusting and fine-tuning the approach to ensure these tools are being used to the greatest benefit.
One practical application of agile in research is the use of sprints, similar to those in software development. Each sprint focuses on a specific aspect of the research, allowing for concentrated effort and quicker turnaround times. Regular sprint reviews with stakeholders can keep everyone aligned and engaged, fostering a collaborative environment where insights are not just delivered but discussed and acted upon.
Real-Time Data and Frequent Collaboration
The use of real-time data is another critical element in modernising research reporting. Real-time data allows researchers to monitor trends and patterns as they emerge, providing a dynamic and up-to-date view of the subject matter. This immediacy can be invaluable in fast-paced industries where decisions need to be made quickly.
Moreover, frequent collaboration touchpoints with stakeholders ensure that the research remains relevant and actionable. Regular check-ins, workshops, and collaborative analysis sessions can bridge the gap between data collection and strategic decision-making. By involving stakeholders throughout the process, researchers can ensure that the insights generated are not only understood but also embraced and acted upon. The time saved through automating or speeding up design, fieldwork and analysis processes should (at least in part) be used to foster collaboration and iteration.
The Role of AI in Research
AI has undoubtedly transformed the research landscape, making it possible to process vast amounts of data quickly and efficiently. AI-driven tools can handle everything from data collection and cleaning to analysis and reporting. For instance, sentiment analysis algorithms can parse through thousands of social media posts to gauge public opinion, while predictive analytics can forecast trends based on historical data.
However, the reliance on AI also comes with challenges. The quality of insights generated by AI largely depends on the quality of the input data and the algorithms used; a significant point which is increasingly disregarded in practice. Poorly designed algorithms or biased data can lead to misleading conclusions. The deprecation of experience as expertise, coupled with the quasi-gaslighting approach of cheap solutions guaranteeing users that they are now ‘experts’ at the click of a button, reinforces the notional validity of poor data. Therefore, while AI can significantly enhance the efficiency of the research process, it should be used as a complement to, rather than a replacement for, human expertise.
Evolving Reporting Processes
Driving engagement and action from research is indeed one of the most challenging aspects of the research process. However, by leveraging agile frameworks, real-time data, and frequent collaboration touchpoints, researchers can deliver insights that are both timely and actionable. While the industry has made significant strides in streamlining data collection and analysis, there is still a long way to go in terms of evolving reporting processes and fully embracing the potential of new technologies.
By overcoming the inertia that currently holds the industry back and fostering a culture of continuous learning and innovation, research organisations can embed insights more effectively into stakeholder organisations, driving engagement and action in ways that were previously unimaginable.
In practice, I suspect that many businesses are going to adopt the low-cost AI solutions that have spent a small proportion of seed money on developing a UI over an OpenAI API and a large proportion of seed money on marketing that convinces users that they are experts undertaking top-quality research. Our job as researchers is not to stand in the way of progress, but rather to modernise our own approaches to prove that a skilled researcher is a necessary part of a hybrid human/AI approach.
Pressing an analysis button and just taking what it gives you will give you, at best, ‘sufficient’. Collaborating with clients to curate and inform the questions being asked, to whom they should be asked, and how they are asked, gives us the opportunity to produce ‘good’ or even ‘great’ research and insights while still using the same cost and time-saving methodologies that are being used by some to deliver poor and misleading insights.
Ultimately, the goal is to move beyond delivering insights that are merely ‘sufficient’ and strive for those that are truly impactful and transformative. This requires not only the adoption of new tools and methodologies but also a fundamental shift in how research reporting and collaboration is approached and valued within organisations. By doing so, we can still save money and work faster; it doesn’t have to have a downside.
This article was originally published on the FlexMR Insight Blog and can be accessed here.
