Think of all the terms you didn’t know: gain of function, messenger RNA, incubation period, PPE, shelter-in-place. And think back on all of the adventures everyone had as they were forced to shift to remote — work, school, celebrations, church. We were lucky to have the technology to keep in contact even though we couldn’t be close in-person.
The economy also went crazy, and after a lot of turmoil, we find ourselves in what’s called a “post-ZIRP world”. The shift to remote meant massive hiring sprees for many tech companies under the belief that the world had changed permanently. As COVID-19 became less prevalent, we saw a lot of things return to “normal” which has had a major impact on the tech industry. People resumed exercising in gyms (bye, Peloton), shopping in stores (bye, StitchFix), meeting in offices (bye, Zoom), etc. This boom-then-bust swept through tech and other industries, leading to massive layoffs over the past 2.5 years.
The summer of 2023 saw several thought-provoking articles about the future of our little corner of the industry: User (/UX/Product/Design/whatever) Research. One in particular seemed to catch everyone’s eye, and amidst all the chaos at that time, I wanted to provide a centered look at what was happening to research. Now one year later, let’s revisit that article to see what’s changed, stayed the same, and still ahead.
Before we dig into the current state, let’s review the key points made in 2023:
How much has changed in the past year? As we revisit these themes with an updated look at the state of the industry, we’ll see that some things are starting to change but we haven’t hit an inflexion point. At least not yet.
If there’s one thing the general public knows about when it comes to AI, it’s ChatGPT. OpenAI and ChatGPT burst onto the scene in November 2022, gaining over a million users in its first week and peaking at 1.8 billion monthly visitors in early 2023. It really seemed like the sky(net? 😅) was the limit and, yes, AI really might take everyone’s job.
But as more and more people began testing the boundaries of GPT-3, it became obvious that AI wasn’t quite ready for primetime.
In the year-and-change since, virtually every company has pivoted toward investing in AI capabilities. This is true in Research as well, and it’s taking two main forms:
The former category has companies like Maze, Dovetail, and Great Question augmenting their feature set with AI assistants. The latter has hot new startups such as Outset, HeyMarvin, and Looppanel attempting not only to disrupt the market but to redefine how research happens. As AI solutions continue to evolve, there are exciting new possibilities for Researchers on the horizon.
By delegating repetitive, time-intensive tasks to AI, Researchers are empowered to focus on more strategic work and drive greater impact in their organizations.
Dovetail’s “Magic” features hold the promise of making it easier and more efficient to analyze data, create highlight reels, protect sensitive data, and more. These tasks have historically taken hours for each study, but if the tools can get to a level of efficacy and trust, Researchers are freed up to do more strategic thinking, dig deeper on specific topics, or try out different analysis angles.
Outset is another AI solution creating new possibilities for researchers. Outset’s product is what they’re calling AI-moderated interviews (a halfway point between moderated and unmoderated). Branding aside, there are some really interesting use cases for how to leverage this capability. Much like the prospect of an AI therapist, having an interactive way to gather feedback from users that’s available 24/7/365 is a unique prospect. Outset comes with a handful of analysis and synthesis tools to help you cut through all of that added noise. Another huge capability boost is Outset’s ability to leverage AI-powered translation to reach users worldwide in their native language.
If there are ways to expand our capabilities with AI and even get 80% of the way there, this is a game-changer for Research.
Last year we contrasted the Research Tools Maps of 2019 and 2022. The 2023 map included over 400 tools across 5 major (and 30 sub-) categories. That’s up from 230 in 2022, so calling it an explosion last year seems accurate. 💣
The continued march of technological progress makes it easier than ever to spin up a company. What used to be thousands of dollars in startup cost to get a website and proof-of-concept can now be done in a weekend, and while we’ve seen some consolidation in the market (looking at you, UserTesting 👀), we’re far from consensus. There are still a lot of options for most major research tool categories. If anything, there are still more popping up:
There are two factors that make me doubt the longevity of this trend, though. On one hand, the market can only support so many competitors. Some companies will ultimately fail, and those failures will become acquisition targets for their larger competitors. The other, however, relates back to the “post-ZIRP” discussion earlier.
In an attempt to curb a spike in inflation since the pandemic, centralized banks across the world have consistently increased interest rates. Higher interest rates make it more expensive to borrow money, which can limit venture capital activity. Venture Capital investments dropped 30% in 2022 and another 45% in 2023. This, along with many tech companies making moves toward fiscal responsibility and tightening budgets, could spell disaster for the less-established players in the Research world.
All of these new tools seem to cluster around similar problem spaces, though, and are leaving entire portions of the Research process untouched. Where are the AI solutions for recruiting, scheduling, or incentive/reward delivery? These are still heavily manual, time-consuming processes and can be rife with bias, so help from AI would be huge. There’s also a gap in AI support for niche methodologies (e.g. diary studies, card sorts) as well as quantitative analysis (it’s 2024, why can’t I ask ChatGPT to analyze my spreadsheet for me? 🤔).
If there’s one thing everyone (investors included) thought during the pandemic, it’s that remote work would be the new norm in business when practical. But what started as a necessity in 2020 and 2021 has become a luxury in 2022 and 2023. Major tech companies struggled with the burden of their oversized real estate footprint, and in response, began implementing a variety of return-to-office (RTO) policies which were often met with backlash.
Dodging the discussion of how deeply problematic most RTO mandates are (Patagonia is the latest to step on that landmine), there’s another trend that seems troubling. As a recent jobseeker, anecdotally it seemed a lot of roles posted were associated with specific locations, and the job descriptions often included some coded language around in-office expectations. A quick LinkedIn search suggests that less than half of all jobs posted support location flexibility.
Which is the basis of my favorite conspiracy theory:
Why bother with the blowback of RTO mandates when you can just lay people off and rehire their role with an in-office requirement? 🧐
The other hot topic with jobs is, unfortunately, layoffs. While there may be more jobs posted now than the past two years, does that mean layoffs have slowed? Sadly, no.
So far in 2024 there have been 106,630 employees laid off across 362 companies. If that trend continues, we’re on pace for ~750 companies and 222,000+ layoffs in 2024, beating out 2022 (165,269 employees across 1,064 companies).
What’s interesting about this, though, is where the layoffs seem to be coming from. By industry, Finance (53), Consumer (41), Healthcare (36), Transportation (36), and Retail (29) have the highest company count in 2024. If you look across the highest number of employees laid off, you see companies outside of tech (Tesla, Getir, Paytm, PayPal, UKG, Farfetch) or from legacy tech brands (Dell, Toshiba, SAP, Cisco, Xerox). Considering tech only consists of 9.3% of US GDP, it’s entirely possible that things are turning around for us, but in terms of numbers, it’s dwarfed by ripple effects felt across other sectors.
If you’ve made it this far, it’s possible to have a pretty pessimistic view on things: AI is on the rise, there are too many tools, layoffs are still high, and funding is low. How are we supposed to keep our Research practices afloat, much less add value in the face of limited resources? If you know me at all, you’re probably already thinking what I’m thinking: ResearchOps to the rescue. 🦸
Democratization got a bit of a bad rap in 2023, as some were scapegoating it as a factor in the “cheapening” of Research and a precursor to the layoffs sweeping the industry. Many ResearchOps champions pushed back, highlighting the difference between the free-for-all stampede of democratization that fear-mongers were describing and the reality of responsible democratization.
We’ve seen tremendous growth in ResearchOps over the past few years, and that trend continues today.
More (and bigger/”more mature”) companies are embracing responsible democratization as a way to do more with less. One of the talks during this year’s Research Leadership Summit titled “Doing More With Less” discussed how to continue to have impact with “only 17 full-time employees”. While the talk leaned heavily on Research Operations, a few of us who have mostly worked in startups joked between sessions that we’d never had a team of 17. 🙃
A lot of democratization best practices were developed by smaller teams, so it’s exciting to see larger orgs starting to come around to this way of thinking. Episode #2 of Cha Cha Club's limited-podcast series features a notable example of this, with Daniel Gottlieb, Head of Research Operations for Microsoft’s Developer Division, explaining their approach to research democratization. Listen to the full episode here.
A strong ResearchOps practice is a great way to do more with less, and with continued constraints on budgets, headcount, and time, this can only be a benefit. As we continue to reshape our teams and practices to do more with less, initiatives like responsible democratization free up our dedicated researchers to be more strategic and free up leaders to strengthen cross-functional relationships. The more we can work cross-functionally, the more value we can add across the business, and the more we can move beyond UX.
What do future Research teams look like? It’s impossible to say, but one hypothesis I’m exploring right now is what I’m calling “T-shaped teams”. Instead of focusing just on T-shaping individuals, maybe Judd was right, and the way forward is for Researchers to become more specialized. As AI tools continue to progress, we can create a team that balances AI-empowered generalists with hyper-focused specialists. The hyper-focused specialists are responsible for dictating how research should happen, and along with their ResearchOps teams, can configure AI tools to spread their knowledge across the entire team.
This, to me, feels like the best of both worlds, and may be a way through the turmoil and existential threat our discipline is facing right now.
Brad (they/them) is a UX Leader, User Researcher, Coach, and Dancer who's been helping companies from early-stage startup to Fortune 500 develop engaging, fulfilling experiences and build top-tier Research & Design practices since 2009. They have helped launch dozens of products, touched hundreds of millions of users, managed budgets ranging from $0 to $10M+, and coached hundreds of Researchers. Born in Buffalo and currently based in Brooklyn, NY, Brad dances with the Sokolow Theatre Dance Ensemble and Kanopy Dance Company, co-organizes the NYC User Research meetup, and served on the Board of ResearchOps from 2018-2021.
