Dan Wasserman, from Sparq Intelligence, wrote a post about the choice many companies face today: do I choose the most innovative technology for the project at hand, or do I go with something more "traditional"?
I loved his post, and I thought I'd build on the ideas of how to decide whether to adopt the latest innovation or not.
Start with why
Simon Sinek hit something when he gave his Ted Talk about "start with why." It applies to so many areas, including selecting a solution to help you conduct market research.
There are many reasons to be looking for new tools.
Your stakeholders are asking for added functionality your current toolset can't support.
Your budget is shrinking even further and you need to find ways to keep doing research for lower cost.
Your team is shrinking and you need technology to support them so that you can meet the demand for data.
Your company is evolving and expecting to have data sources more connected, meaning you need to find ways to consolidate tools.
Your tools are starting to lag with the increased amount of data required to analyze.
Your experience with the vendor has changed such that you are looking for a new vendor to improve the experience.
All of these reasons are valid, but they also may lead to different sets of tools being evaluated. For example, if your tools are starting to lag because of the amount of data analysis required, you may not be looking for tools that use new testing methodologies, but tools that have better data load abilities. This may involve AI for speeding up the analysis speed, but it isn't the key item you are looking to solve.
You aren't looking for an AI tool, you're looking for a tool that will support the amount of data you need to analyze.
Note the difference.
However, if you need tools that will let you automate more of the work your team is doing, then you likely are looking at some of the more innovative tools available on the market. However, even then, your "why" isn't "to use AI," it's to lift the workload your team currently carries.
Look past the fads
John Bird of InfoTools touches on the issue of knowing what innovations are here to stay in their Buyers Guide for Market Research Software. "It can be difficult to know what is a fad, what is hype and what actually deserves extra attention." Many companies handle this by waiting for the innovators and early adopters of the tech adoption curve to use the tools long enough to know what's sticking around and what's actually delivering something worth investment.
However, I would argue that having a solid set of technology requirements for your tech evaluation can help you cut through the noise. As you test tools, keeping anchored in what the tool needs to do for your team gives you the base on which other capabilities can be explored. You might need tools that make generating reports faster for your team, and keeping your focus on that can keep you from adopting a tool that uses the latest innovative technology to do something cool that has nothing to do with the speed of generating reports with great data visualizations.
Beware the do-it-all innovators
Last, when looking at the innovative tools versus something that might not be as flashy, be aware that some of the newer companies in the field may say that the innovation they are selling is applicable to any problem you throw at it. The truth is, that is rarely the case. It has been my experience that the latest innovations have their place, and great companies will know when their solution can truly address your research need, and when to point you in another direction. The best companies will point you away from their solutions when they know they can't support what you need. They know that forcing their solutions to deliver what you need will likely only result in frustrated customers, which has yet to be a great growth strategy.
For example, one of the latest innovations being discussed in early 2025 is synthetic data. Synthetic data is best used to augment existing data, and is generated using existing data.
Does this mean synthetic data should be applied to every research scenario where sample costs are limited? Absolutely not!
Does this mean that synthetic data should be built using data your stakeholders have questioned in the past? Definitely not!
Does this mean vendors who say they can offer you synthetic data should be asking you about the validity tests on your data sources, the use cases you have for the synthetic data, and counseling you on how to be sure it is being applied to ensure quality outputs? YES.
Conclusion
As technology continues to evolve, researchers will continue to feel pressure to keep up and use the latest innovations. Starting with why you need a new tool, focusing on what the tool needs to solve for your team, and being skeptical of overpromised solution applicability can keep you from wasting money on innovations that you don't need.
