Successful Multi-Agency Collaboration

Hiring two agencies on the same project? Here’s what it takes to make it work.

It’s not hard to imagine why organizations can be wary of hiring two (or more) outside parties to work together on one project. Will the managerial strain be out of hand? Will they communicate as well as they need to in order to get the job done? Will you have to spend hours a week putting out fires and resolving disputes?

Fearful of the coordination that comes with hiring more than one consultancy or agency, many organizations opt for full-service agencies that can manage all aspects of the work. While that may be the absolute right call for some projects, other efforts will really benefit from combining the unique specialized skill sets of different organizations — because of course, no one organization can be an expert in all things.

If you think your work may benefit from specialists that span multiple agencies, we’re here to tell you that it doesn’t have to be quite so scary. As a consultancy that’s collaborated with other agencies many times before, we’re big believers in the value of diverse skill sets for solving complex problems. And here’s what you can do to set your organization up for multi-agency success.

Hiring for group-work amongst agencies

Finding a consultancy who can do great work alongside another consultancy is partly common sense. Naturally, you’ll want to identify parties that are strong communicators — which also means they are good at listening. It also helps to suss out egotism… does the agency seem more focused on doing good work, or impressing you? If you sense a group that appears very concerned about the optics of their work, that could lead to a jockeying-for-position or credit-taking game that turns other consultancies off and puts them on the defensive. 

Another thing to look for is complementary styles of problem solving among agencies. That doesn’t necessarily mean they should do things the exact same way — diversity in problem-solving techniques will usually enrich a project. But will there be enough common language to discuss ideas and reckon with differences in process? We’ve had many successful collaborations with agencies who operated very differently from ours, but when we each saw each other’s approach as a strength we could learn from, the divergence was a net positive. 

And of course, it’s always good to straight-up ask agencies whether or not they’ve worked alongside other agencies in the past. How did that go? How did it shape their point of view on what makes for successful collaborations? You may even be able to talk to parties they’ve worked alongside in the past.

Setting multiple agencies up for success (without creating a managerial nightmare)

When it comes to making sure the groups you’ve hired will be as successful as possible, it all comes down to delivering as much up-front clarity as possible. All agencies should be crystal clear on why they have been hired, and the value they are expected to be contributing. This should also be clear amongst parties — each agency should understand their own contribution in relation to the contributions of parties around them. Some overlap in responsibility is completely fine, as long as this overlap is named and explained. And this doling out of responsibilities and expectations should come from the person hiring, so as to keep things as clear and undeniable as possible.

It’s also extremely helpful to do some situation-planning ahead of time, discussing things like how decisions will be made and how disagreements should be resolved. We all know any complex project is bound to experience change and surprise, but having expectations around how those will be handled can ease tension and help each party be their most effective and collaborative self.

Once you get the agencies going, don’t be afraid to leave them to it. While your initial presence is key, eventually, consultants need to develop their own rapport with one another and ease into a rhythm. Their relationship should become their responsibility in a way that does not need to be mediated by you. 

We don’t deny the benefits of full-service agencies — there are times when ease of operation indeed outweighs a need for specialists. But managing multiple agencies doesn’t have to be a headache. It’s how we’ve done some of our best work. 

Scoping a project that may benefit from our collaboration? We’d love to hear from you!

Get in touch

A Checklist for GenAI Readiness

This is the first in a multi-part series about Generative AI, focused on how to set up your Generative AI project for success. Whether you’re new to GenAI, or have your own tactics to share, there’s more we can all learn about implementing this new technology.

With the many offerings available now in the GenAI landscape, from OpenAI’s DALL-E and ChatGPT – already at a 4o version—to Meta’s LLaMA, to Microsoft’s Orca, to Google’s multiple AI offerings, Generative AI Large Language Models (GenAI LLM) now feels a bit inescapable. It can be easy to get caught up in the excitement about adding a GenAI LLM-enabled tool to your company’s portfolio, but it can be difficult to know where to start, and what needs to be in place to succeed. So before we discuss the various offerings or how to implement LLMs, let’s take a look at how you can set your team up for success—whether you’re in Engineering, Product or Design—before embarking on your next GenAI LLM project.

  1. Know what you can change with LLMs – and consider how you can change the rest 
    This is a question we think about all the time at Grand Studio: what problems can – and should – be solved with a given technology? With technologies as complex as LLMs that involve trillions of tokens, years of training, and millions of dollars, designing a new LLM might be a bit out of reach for many. But even for those who can access these solutions, it still doesn’t mean that all aspects of their problems should be solved with a GenAI modality. That’s why exploring what the problems are, how users behave and what tools they use, as well as what combination of solutions may most holistically address the issue(s) is an important first step. And if a GenAI LLM is in fact the right solution,  there may still be quite a few elements of a problem  that can be solved for and improved outside of a GenAI. 

    One recent example came up as we were designing a GenAI LLM solution: one of the use cases we wanted to tackle had to sit outside the solution’s access point due to security measures and therefore could not be addressed by the GenAI. We were able to do a UX/UI heuristic pass and create a set of digital UX adjustments that reduced the issues with that use case so much that the amount of money the enterprise was spending dropped an entire contract tier.  So don’t underestimate the impact of UX/UI within a holistic solution.
  1. Clean up your data
    We’ve said it before and we’ll say it again: your GenAI will flourish or fail depending on how clean and organized your data set is. The general data sets that inform current LLMs are massive and in order to get answers that are relevant and accurate for your company, or even your industry, you’ll likely need to help the LLM focus in some way. Data lakes – essentially centralized areas for your data that an LLM can be required to check first before generating answers, and carefully crafted back-end directions on what information to present – and how – (called system prompts) can help your LLM prioritize certain data before going into its general knowledge. The trick is that data has to be organized, well-written, and clean of errors first. This can be a big ask if you are the kind of company that has a huge knowledge base archive that maybe hasn’t been overhauled in years.

    One way to tackle this is to start small(er). You won’t be able to get away with only 100 clean pieces of data, but you might be able to get away with ~ 1000. Starting small and establishing a content governance structure can help you out in the long run, as knowledge becomes more relevant and up to date, both for your new GenAI buddy and for the employees in the business itself. (And if content governance is new to you, that’s something that consultancies like Grand Studio can help with.)
  1. Testing, testing, testing
    GenAI is a new – and therefore unpredictable – technology. People have a lot of mixed feelings about GenAI; some people are excited about what they see as a tool of the future, while others are skeptical or even afraid of what GenAI will mean for their job security and place in the workforce. Building multiple moments of user-centered research and  testing into your project plan can help you build empathy with your target audience, with an added benefit of not only spotting technical bugs and glitches, but also helping people start to build trust and understanding of what this technology is capable of. Thorough research with the right users can also help your internal comms or external product marketing teams create a finely-tuned product launch messaging and rollout plan. (As it happens, Grand Studio is so committed to user-centering all products and services that we’ve created a public-facing framework to help put this into action).
  1. Embrace the whimsy
    Finally, as you’re gearing up to get started on your exciting new GenAI-enabled product, it’s important to set some grounded expectations and cut through the marketing hype. GenAI, and LLMs in particular, are not silver bullets. They are emerging technologies that are still being experimented on, developed, and tested out every day. There are limited functionalities as to what these LLMs are capable of; they’re not truly “intelligent” and they can’t read your – or your users’ – minds. And there’s still a learning curve to understanding how to get the best out of these technologies.

    Bias and hallucinations are real risks that could open you and your company up to potential liability depending on your target audience and industry. Company security is an additional concern given that data once fed into an LLM – even a company’s proprietary LLM or Wrapper – is impossible to remove once it’s in there so there will need to be additional protections in place. Having these hard conversations about why your solution should include a GenAI-enabled product and what the expectations of this technology are before you get started will save everyone a lot of time and pain later on as these limitations make themselves known.

Overall, GenAI is an exciting thing that has a whole world of potential and possibilities attached to it. We believe that being honest about the technology’s limitations and setting yourself up for success as best as possible will give you the greatest chance to make the best use of this emerging technology and its capabilities.

Stay tuned for the next part of this series: The Ideal GenAI Design Process

4 Things You Won’t BELIEVE Design Can Learn From Buzzfeed

July: a time for pools, slushies, bike-riding and hanging out with friends. What better way to celebrate mid-summer than to look for inspiration in one of the quintessential lighthearted media outlets?

Without further ado, here’s what design – at all levels – can learn from the Buzzfeed approach.

  1. Bite-sized content works.  People read listicles and short articles because they are brief snippets they can parse quickly and move on. Often in design, we try to pack too much in, and it gets lost in the process. Bullet points of quick takeaways, illustrative impact quotes or screens, and executive summaries work really well – with an offer to dive deeper for those who genuinely want more.
  1. Nothing engages like gossip. Put another, more design-y way, stories anchor everything. We all want that tea spilled and frankly, when details are grounded in a narrative that starts with a bang and sets the stage, tension that builds, and an ending that wraps up that portion of the story (even if the overarching narrative will continue on), we’re listening the whole way through. Along those lines…
  1. Juicy headlines draw people in. Is it clickbait or is it cutting through the noise to grab your audience’s attention? (Both?) We can do the same in design when communicating important research insights with leadership or naming design options with stakeholders managing busy schedules. Marketing exists for a reason and oftentimes Design doesn’t do a good job of utilizing it for ourselves. Juicy headlines or naming conventions can help our business stakeholders understand what problem is being solved, or what they or their users will get out of a particular solution from the get-go and bring them along in a productive, collaborative way. 
  1. Embrace the whimsy. Buzzfeed always has a silly quiz on things like “what your favorite sandwich says about your future” – and people love those. Sometimes design takes on the personality of business and the thing is, we really can’t take ourselves too seriously for two reasons:
    1. We need to take the work seriously but take ourselves lightly in order to really enable creativity to flow. Putting on formal structured thinking and expression can feel quite confining to many designers. Which leads to… 
    2. We’re the “creatives.” (Yes, everyone is creative but we’re the people who are expected to bring the outside-the-box thinking and artifacts). We’re not only allowed but expected to bring some amount of rule-breaking and whimsy to the table. 

Put another way, if not us then who? ESPECIALLY within your own teams. So have fun. Do a little something silly. Have a team-building activity that’s a little weird (we’ve done Secret Santa lunches sent to each other’s houses and at-home Nailed It challenges). Change your Teams photo to a raccoon meme. Use gifs in communication.

When you embrace the silly you make space for other people to relax, bring themselves and create a more creative and innovative space for work to take place. A place where they can take risks – at first with just themselves but then with the products and ways of working. And smart risk is how you get to great. 

Want help figuring out how to set up and maintain a high-functioning and impactful design team? Drop us a line! 

Scaling Research by Activating the Frontline

Innovation is the name of the game in UX Research; we are often being asked to find creative ways of gathering insights from end users with smaller teams and even smaller timelines and budgets for recruitment. As we continue to seek out ways of reaching people, there’s an often untapped source of research insights who are working with our target users day in and day out: frontline employees. This is a key strategy, particularly when dealing with any protected or vulnerable population, such as patients or children, who are often very difficult to access for a variety of (very good!) reasons.  

Frontline employees are the boots-on-the-ground people who are interfacing with users every day. Depending on the industry and problem space they might be receptionists, call center employees, nurses, cashiers, etc. They spend their time putting out fires and hearing directly from customers about what’s working and what isn’t. 

So, where do I start? 

First things first, activating any group to be part of research often starts with building relationships. Frontline employees are busy people who are usually being managed by busy people who are often concerned about preserving their teams’ bandwidth and protecting their time. To reach them you’ll need allies, and allies start with relationships. Start by getting to know their managers and team leaders (or whatever the equivalent role is). SME (Subject Matter Expert) interviews can be a great method here, both to learn more about pain points and also to help people understand that you’re there to help them and their teams with their jobs. Research is a way of letting people be heard, and that’s a valuable thing you can do for them.

Once you’ve built a relationship with the managers and team leads, you can start asking about getting access to their teams who are interfacing directly with your target audience. 

I’ve built relationships…now what? 

Now that you’ve gotten access to the frontline employees, there are a couple different research methods we suggest considering. This is your chance to get the inside scoop about what kinds of pain points exist for users and employees, what kinds of tools they use, what kinds of ideas or suggestions they have for improvement, and more. Keep in mind that while research can be hugely impactful, if you’re not careful it can also be very time-consuming and extractive – meaning it takes knowledge, expertise, energy, etc from people without giving anything of value back. So consider how much bandwidth, time, and energy people have when planning your research, as well as what you may be able to give back to them. 

Two non-extractive options we’ve leveraged in the past are: 

Diary studies 

Diary studies are an unmoderated research method that asks someone to keep a log about their experience at certain times or in response to certain triggers such as after speaking to a customer or using a piece of software. You can ask people to take photos of key moments, record their emotions or activities during or after certain events, or provide reflections on changes they might have made or ideas they have. Diary studies are a great way to turn your frontline employees into researchers themselves by having them think about and interrogate their own workflows, softwares, and scripts when interacting with end users. 

Diary studies can be very impactful because they are straight from the participant’s unfiltered perspective and are designed to happen in the moment, so they are less likely to be misremembered. Some drawbacks include people forgetting to fill them out at the right times – or at all, especially if they are busy – or providing unclear information that is difficult to follow up on and get additional clarity. 

Passive prompt wall

This is a good method to use if your participants all share a physical space – such as an office  or breakroom. Setting up an installation such as oversized post-it papers with markers and prompts that participants can fill out on their off time can provide you with first-hand insights about how people are feeling, what they’re hearing from users, and what ideas they might have for how to improve the products or services they deal with day-to-day. 

Some watchouts to this method is that you need to be mindful of how you word your prompts so they are easy to understand and you’re surfacing relevant information. There’s always a risk of people responding with unserious, off-topic responses with unmoderated forum-type research, so have a plan in place to vet some of the more suspicious answers you receive (possibly from those SMEs you interviewed earlier).  

I’ve gathered my research, what do I do now? 

Congratulations on gathering research from frontline employees! Now it’s up to you to synthesize your insights and pull out the necessary takeaways. Consider conducting 1:1 interviews or focus groups to follow up on interesting themes and patterns. If you are developing concepts or prototypes out of your insights, frontline employees can be a great group of people to start gathering some validation on your ideas. 

Research is an ever-evolving practice, and finding new ways to learn about what works and what doesn’t can sometimes feel like a moving target. But if you build relationships early and expand your participants to include not just those experiencing the pain points first hand, but to include the people who are experiencing them second-hand as well, you can capture more data in richer and more informed detail than ever before.

Interested in how you can activate your frontline employees? Drop us a line!

Unsolicited Advice for Leveraging a GenAI LLM

At this point, you’re probably pretty familiar with the AI hype out there. You’ve likely read that GenAI (like DALL-E or ChatGPT) is great for generating both visual and text-based content, and AI overall can be good for identifying patterns, particularly in large data sets, and providing recommendations (to a certain degree).

But you may also be familiar with the myriad ways GenAI has gone sideways in recent months (ex: Intuit’s AI tax guidance debacle, New York City’s law-breaking chatbot, the Air Canada lawsuit, and so many more). That doesn’t mean you need to stop experimenting with it, of course. But it does mean that the folks warning about it not being ready quite yet have some valid points worth listening to. 

Having built several AI solutions, including a recent GenAI LLM (large language model) solution, here’s some unsolicited advice to consider when leveraging a GenAI LLM. 

Don’t use GenAI for situations where you need a defined answer.


As evidenced in all the examples above, GenAI chatbots will – and often do – make information up. (These are called hallucinations within the industry, and it’s a big obstacle facing LLM creators.) The thing is, this is a feature, not a bug. Creating unique, natural-sounding sentences is precisely what this technology is intended to do and fighting against it is – at least with the current technology – pointless. 

There are some technical guardrails that can be set up (like pointing the system to first pull from specific piles of data, and crafting some back-end prompts to tell it not to make things up) yet still, eventually, our bot friends will find their way to inventing an answer that sounds reasonable but is not, in fact, accurate. That is what they are meant to do. 

In situations where you need defined, reliable pathways, you’re better off creating a hardcoded (read: not GenAI) conversation pathway that allows for more freeform conversation from the user while responding with precise information. (For the technically-minded, we took a hybrid format of GenAI + NLU for our latest automation and found it quite useful for ensuring that something like following a company-specific process for resetting a password was accurate and efficient – and importantly, in that use case, also more secure.)

Know thy data—and ensure it’s right.


I know it’s been said a million times over but a pile of inaccurate, poorly-written data will provide inaccurate, poorly-written responses. GenAI cannot magically update your data to be clean and accurate – it can, over time, generate new information based on existing information and its style (which should still be checked for accuracy) but asking it to provide correct information when it’s hunting for the answer through incorrect information is an impossible task. It cannot decipher what is “right” or “wrong” – only what it gets trained to understand is right and wrong. 

It’s important then to know what the data that you’re starting with looks like and do your best to ensure it’s quality data – accurate, standardized, understandable, etc. Because barring time to properly train the data (which is a serious time commitment but well worth it for anyone wanting proprietary or custom answers), starting with a clean data set is your best bet. 

Bring the experts in early.


When people have been experimenting with the technology and potential solution for a while, there is a pressure to “get it done already” by the time the experts roll in that doesn’t allow for the necessary exploration and guardrail-setting that needs to happen, particularly in an enterprise setting where there are plenty of Legal, Compliance, Security and even Marketing hurdles to clear. 

From both personal and collected experience, it’s worth noting that often the initial in-house experimentation focuses on the technical aspects without user experience considerations, or even why GenAI might – or might not – be the right solution here.  That’s going to take a little time. So it’s worth bringing in design and/or research experts, whether in-house or consultants, alongside the initial technical exploration to do some UX discovery and help the entire sussing-out process happen in tandem with the technical exploration. This can provide a clear picture of the business case for pursuing this particular solution. 

To help out, the Grand Studio team created a free, human-centered AI framework for an ideal AI design & implementation process.

Interested in knowing how to start a GenAI project of your own? Drop us a line! 

A New Way of Understanding Sports Fans

A lot of sports organizations think about their fan base in terms of subscription tiers. Their business strategy is largely about moving fans up those tiers, converting them to higher levels of monetization. Accordingly, they ask themselves questions like: what would it take for a fan to upgrade to a season ticket holder, an ESPN+ subscriber, or a daily reader of sports news? 

This approach makes sense. After all, a company is in the business of monetization. But to get a fan to upgrade, they must first and foremost be engaged with whatever it is you’re offering — be that a product, a team, or the game itself. To bring them up the tiers is essentially to ask them to increase their level of engagement with you. And if you want fans to engage deeply, you have to deeply understand what it is that they want.  

In other words, the better you can understand the crux of a fan’s engagement — how it is shaped, how it’s maintained, and how it grows (or stagnates…) — the better you can cultivate their inspiration to upgrade. Getting to this level of a fan identity requires an intimacy with their beliefs that goes beyond the details on an account subscription. 

A fan-centric view of sports

As mentioned, while there is clear value in analyzing subscription trends, it is inherently top-down and corporation-centric, placing fan behaviors primarily in relation to their monetary value for the company. If this is the key variable by which segments are sliced and diced, it can limit the organization’s ability to surface the most meaningful characteristics and variations that define their fan base. And, subsequently, limit the organization’s ability to serve such needs, and get the very upgrades they are after.

What many sports companies could use is a complementary bottom-up approach to segmenting and analyzing fans. This approach would start by understanding how and why a fan engages in a sport. Is it all about supporting a particular team? Is it a larger appreciation for the sport? Is it about the culture? Belonging? Nostalgia? Hometown pride? Is it about going to games because all their friends do? Starting on the ground to understand attitudinal and behavioral differences across fans can set organizations up to learn something deeper and more important about that fan than their subscribership status — things that ultimately do more to determine how they can serve each segment. 

Stories from the stadium 

In a previous project, a major sports league asked us to overhaul their mobile app. Their goal was to get fans to spend more time on the app so they could generate more ad revenue. When we started doing research on their fan base, we uncovered surprising trends that ended up influencing the league’s overall engagement strategies. For example, there was a significant portion of the fan base who were what we called “adopted fans.” Instead of inheriting a team from traditional family ties, they adopted a new team when they moved to a new state, or adopted one based on its underdog status. As newer fans, they looked to national news for sports intel. Diehard fans, on the other hand, primarily went straight to their local beat reporters for sports news. This stratification uncovered opportunities for the league to serve each group differently and personalize their experience on the app, increasing the engagement opportunity for each group. 

In another project with a major sports team, the avidity level of a fan turned out to be among the most important characteristics to analyze. We uncovered, for instance, a segment of the population we called “tag-alongs” — those who attended a sporting event because someone had invited them. Many of these tag-alongs didn’t know much about the sport to begin with, but loved the experience of going to a live game and rooting for the team. For this sub-group, the atmosphere and amenities at the stadium made a big impact on their likelihood to return. Once this group was uncovered, the team was able to do more to convert these tag-alongs into fans in their own right. 

Doing the “field” work: meeting fans where they are

Let’s assume your organization has bought into the value of uncovering the unique fan archetypes within their population. What comes next? 

One important way to research fan attitudes is, of course, going to games. Observing fans interacting with their sport or team, and also observing them in community with one another at games, is not to be overlooked. 

But fans are not only fans during sports games. They are also fans when they are reading the news, keeping up with players or stats or the league at large. They are fans when they’re out at a bar with friends and see their favorite player’s jersey on the wall. They are fans during off-season as well, even when there aren’t as many ways to show it. 

Understanding a fan means understanding the rhythm of their fandom, the ebbs and flows in addition to the moments of peak excitement and engagement. How they stay connected to their team or sport when games aren’t going on can be just as informative as how they behave during a game. There are cadences to the experiences of different sports fans, and understanding that richness of detail is key to understanding how their needs can best be met. We’ve found that understanding these harder-to-capture aspects of fandom require different research methodologies — for instance, perhaps you need diary studies to check in on fans during off-season or lulls in action. Perhaps you need to post up in a sports bar and catch people stretching out the emotion of a game by connecting over it. Perhaps you need data points from people as they read sports news throughout the week. 

Sports mean a lot to people. For some, their fandom is a key piece of how they see their own identities. Taking the time to understand these segments with multidimensional attributes with care can pay off greatly for fan satisfaction as well as overall engagement metrics.

Looking to better understand and serve your fans? We’d love to hear from you!

The 2024 Design Forecast

We did it, everyone. We made it through another year. To be perfectly frank, this year was a bit of a weird one, mainly due to a few key elements:

  • A real will they, won’t they dance with a potential recession that constrained the budgets and resources of many organizations this year
  • Generative AI coming front and center and taking over everyone’s conversations and questions about the future in product and business
  • More layoffs across industries, impacting the design & product communities as well as what the remaining team members can accomplish

So let’s look ahead to next year, shake our magic 8 ball, and make our predictions about what trends we’ll see in 2024.

Generative AI Implementation Begins 

2023 many folks spent learning and hiring for generative AI leadership in their organizations. Certainly both these things will continue into the new year, but orgs are starting to feel ready to “try something” in generative AI and set up their first initiatives and pilots to test out what AI can do for them. 

One unsolicited piece of advice we’ll give from our previous experience: take your time to really define and scope the problem you’re solving, make sure AI is the right solution for your user group, and start small. It’s easy to skip past these steps but most everyone is in a test-and-learn moment right now, so if ever there was a time to understand before blasting a technology everywhere, this is the time.

The Return of Innovation

Businesses at large took a collective pause from innovating to focus on optimizing what already exists. Part of this is burnout from the last few years, part of it was budget slashing and belt-tightening. And to be honest, this will likely continue into the first half of 2024, especially given the geopolitical and economic tensions currently simmering. 

However, our bet is that the second half of 2024 will begin to see a return to pushing innovation, particularly for incumbents who may have newcomers nipping at their heels. It’s always nerve-wracking to feel like you’re losing momentum in the market, and innovation is the way to stay ahead of the pack.

Consumer-Focused Mixed Reality Hits the Shelves

In a prediction that merges both innovation and emerging tech, we’re seeing a trend towards more mainstream utilization of mixed reality and spatial UI design (things like Vision Pro and Quest3), particularly in the enterprise side of things on tasks like training and digital twinning. 

This trend is still looking for its footing on the consumer side, which leaves 2024 wide open to folks looking to lead in that space. We foresee the emergence of tools involving B2C applications in this realm, though it won’t yet be a saturation of the market. Frankly, if the economy continues in an up and down pattern, it will likely remain a luxury novelty in the consumer space until people have the money – or a reason – to invest in it. Likely 2024 will be a learning year for all of us watching this space to see what plays out with consumers.

Research Execution Will Extend Beyond Researchers

While many design and product teams were hit hard with layoffs in late 2022 and throughout 2023, it’s worth noting that research teams seem to have taken the hardest hits. Because of this, needed research is going undone and we’re seeing product managers and UX designers taking up the mantle of executing research – in particular, evaluative research that is necessary for the optimization work that’s been on the forefront this year. 

We see 2024 being a year where research continues to be a deficit and many organizations will look to contractors and agencies to fill the gap – but also to their internal folks with less of a background in how to execute. For this reason, we may also see an uptick in attendance for research conferences, books (we love Steve Portigal’s Interviewing Users and Caroline Jarrett’s Surveys That Work), online courses, and trainings to help support these additional asks on folks. 

Want to talk about your design & research support needs for 2024? Reach out to us!

Great First Impressions: Leveraging UI for Critical Product Moments

Your user interface is your digital first impression. And as we all know, a good first impression can change everything. 

UI design is often thought of as the creation of pleasing aesthetics. While this may be a part of what we focus on, the broader concern of the UI designer is to manage how someone feels while interacting with your content or page. Such feelings will translate, both consciously and subconsciously, into how people feel about you and your brand. Even minor confusion or frustration about what to click or how to input information can erode trust — who’s to say working with your business won’t be similar to using your website?

With ever-higher expectations for our digital world, and such high stakes, UI should not be an afterthought. Grand Studio’s UI team has compiled some tips for any teams taking on a new — or evolving — digital product. 

Tip 1: When everything calls out for attention, nothing calls out for attention

In an effort to get important components noticed, many designers try to crunch a lot of content above the fold (at the topmost part of a page, before needing to scroll down). Unfortunately, this most often ends up backfiring. Even if your user can see all the pieces in one glance, you risk overwhelming them, and generating confusion about what they should pay attention to.

We recommend placing one (or maybe two) areas of focus up top, sending the rest more into the background. Use color, contrast, size, and placement on the page to guide a user cleanly from primary to secondary focus. White space is your friend. 

Tip 2: Your top priority is helping your user know what to do when

It may sound obvious, but it’s far too easy to get caught up in components of the design and lose track of its primary purpose — helping the user take the action they need to take on the page. Consider the user’s paths through the page, and make sure they have everything they need to complete their journey. Never let your user wonder things like, “is this text interactive?” “what does this icon mean?” or “how do I fix the error I’m seeing?” 

At its most basic level, a visual interface is a means to communicate efficiently with your user so you can guide them through what they need as elegantly as possible. You want your UI to be a good communicator.

Tip 3: Everything that can be consistent should be consistent

If we think about a visual interface as a means of communicating with the user, it is critical that we are always using consistent language to do so. Though it may sound minor, using a color to mean one thing (e.g. red button = “remove”) at one point and another thing (e.g. red = “edit!”) at another point can be cognitively taxing on a user. As you establish your visual language, make sure there is always just one meaning attached to any given visual component, whether that’s an icon, a color, a word, or a button. 

These things not only save your user time, they help the user feel better on your page. Smooth sailing = trust generated. 

Tip 4: Consider your breakpoints, and don’t skip testing on the end device!

While of course there may be slight differences between different breakpoints or devices, there should always be visual and functional similarities across them. The idea here is to create an experience that is seamless and consistent for everyone, regardless of the device and its size. 

To create the type of layout that will be clear and usable no matter the size of the window, we recommend designing for breakpoints — points at which a design will “break” if you stretch your browser window wider — to ensure all of the page’s elements will nicely fit the available screen space, no matter the size. Considering breakpoints will naturally optimize content for viewing on different devices (even ones that haven’t been invented yet), ensuring everyone gets a clear and usable view.

That said, it’s still very important to view your design on the end device to be able to visualize it in a realistic way. You can do this by downloading your design and opening it up in your browser or device, like a mobile phone. Seeing your design in context with the device will ensure that elements or text are sized correctly and comfortably.

Tip 5: Accessible designs are better for everyone

Making sure your design can be used by as many types of people as possible not only increases the number of people who can interact with you, it can also help you find and resolve points of confusion that go on to help everyone. Accessibility isn’t icing on the cake; it is a critical component of good design. 

For example, take alt text for images, or text that describes what is happening in an image. Alt text’s primary function is to help those with visual impairments hear through text-to-speech what each image contains. But alt text can also be really helpful for Google searches, and helping people find content on your page. It’s invisible for those who don’t need it, but there for people who do. (For more on this, check out our blog post on accessibility.)

At Grand Studio, our UI team does way more than “pixel push.” Because we see UI as a critical part of a holistic design practice, UI is integrated into product strategy, UX, and even design research. We know that the best user interfaces come out of a deep understanding of not just UI best practices, but an understanding of the particular context and goals of each client we work with. When you’re making a first impression with your users, nothing could be more important. 

Got a user interface project? We’d love to hear from you?

Human-Centered AI: The Successful Business Approach to AI

If AI wasn’t already the belle of the tech ball, the advanced generative AI tools surfacing left and right have certainly secured its title. Organizations are understandably in a rush to get in on the action — not just for AI’s potential utility to their business, but also because, more and more, demonstrating use of AI feels like a marketing imperative for any business that wants to appear “cutting edge,” or even simply “with the times.”

Sometimes, rapid technology integrations can be a boon to the business. But other times, this kind of urgency can lead to poor, short-sighted decision-making around implementation. If the technology doesn’t actually solve a real problem — or sometimes even when it does — many don’t want to change their process and use it. All this to say: a bitter first taste of AI within an organization can also harm its chances of success the next time around, even if the strategy has improved. 

At Grand Studio, we’ve had the privilege of working alongside major organizations taking their first high-stakes steps into AI. We know the positive impact the right kind of AI strategy can have on a business. But we’ve also seen the ways in which pressure to adopt AI can lead to rushed decision-making that leaves organizations worse off. 

Our top-level advice to businesses looking to implement AI: don’t lose sight of human-centered design principles. AI may be among the most sophisticated tools we use, but it is still just that — a tool. As such, it must always operate in service of humans that use it. 

A human lens on artificial intelligence

When implementing AI, it is tempting to start with the technology itself — what can the technology do exceptionally well? Where might its merits be of service to your organization? While these may be helpful brainstorming questions, no AI strategy is complete until it closely analyzes how AI’s merits would operate in conjunction with the humans you rely on, whether it be your employees or your customers.

CASE IN POINT 

In our work supporting a major financial organization, we designed an AI-based tool for bond traders. Originally, they imagined using AI to tag particular bonds with certain characteristics, making them easier for the traders to pull up. It seemed like a great use of technology, and a service that would speed up and optimize the trader’s workflow. But once we got on the ground and started talking to traders, it turned out that pulling up bonds based on tags was not actually their biggest problem. AI may be a golden hammer, but the proposed project wasn’t a nail — it only looked like one from far away. 

As we got more clarity on the true needs of these traders, we realized that what they actually needed was background information to help them make decisions around pricing the bonds. And they wanted the information displayed in a particular way that gave them not just a suggestion, but the data that led them there. In this way, they’d be able to incorporate their own expertise into the AI’s output. 

If we had designed a product based on the original assumptions, it likely would have flopped. To be useful, the AI needed to be particularly configured to the humans at the center of the problem.

The linkage points between human and AI are crucial

We all know that bad blood among employees can spell doom for an organization. Mistrust and negative energy are surefire ways to sink a ship. In many ways, integrating AI can feel a lot like hiring on a slough of new employees. If your existing employees aren’t appropriately trained on what to expect and how to work with the new crowd, it can ruin even the best-laid plans. 

Once you’ve identified where AI fits into your organization, we recommend paying extremely close attention to the linkage points between human and AI. Where must these parties cooperate? What trust needs to be built? What suspicion needs to be mitigated? How can each benefit the other in the best way possible?

CASE IN POINT

Recently, we worked with a financial services technology provider to develop AI that could spot fraud and inaccuracies in trading. We conducted in-depth research into the needs of the surveillance teams who’d be using the software to understand their role and also their expectations for how they’d use such a tool. This allowed us to thoughtfully build a visual interface on top of the AI that could maximally meet the surveillance team’s needs, including helping them with task management.

Taking the time to understand the precise nature of this potential human-AI collaboration helped us use resources wisely and prevent the mistrust and resistance that can cause even the best tools to fail. 

AI integrations require trust and understanding

Your AI also can’t be a “black box.” While not everyone at your organization needs to be an expert on its functionality, simply dropping an unfamiliar tool into a work environment and expecting people to trust whatever it spits out is very likely misguided. This is especially true when AI is helping experts do their jobs better. These roles are defined by the deep training that goes into them — how are they supposed to give an open-arms welcome to a new “employee” whose training they can’t see or understand?

For example, a doctor trained in reviewing mammograms may well benefit from AI software that can review 500 scans and whittle it down to only 20 that need human assessment. But you can imagine a physician’s resistance to simply taking those 20 images without understanding how and why the software weeded out the other 480. They rely on their expertise to save lives, and need to trust that whatever tools are helping them are supported by similar training and values. 

AI has the power to make big change. But if we don’t center humans in our implementations, the change we make may not be the good kind. 

Contemplating your early steps into AI? We’d love to work with you to help make your leap into the future a smart one. 

Designing Products for Healthcare: 5 Important Considerations

In the healthcare space, the design choices you make can quite literally have life-or-death stakes. Getting it right is important. 

But healthcare environments are unique spaces, and what works in other industries might not always carry over. In addition to regulatory considerations like HIPAA, many healthcare organizations have distinct cultures and ways of doing things shaped by decades of caring for people, often in extreme circumstances. 

Grand Studio has had the privilege of working with several healthcare organizations over the years and has come away with some rules of the road when it comes to product design in these specialized spaces. Read on for five key things to keep in mind.

Tip #1: Involve Clinicians & Other Stakeholders from Day 1

For the best chance of success, bring in key stakeholders right from the outset — particularly clinicians. For one, their perspectives will be critical to developing whatever you’re creating. But also their involvement will also help build buy-in and trust. 

Too often, clinical teams get burned by the debut of some new technology that was clearly built without insight into their day-to-day experience and ends up causing more headaches than it eases. Looping these key stakeholders in immediately and keeping them up to date as the design process moves forward will have a two-pronged benefit: you’ll spot potential problems in the design, and you’ll also do a lot to socialize your effort. You build faith that you’re listening to them, working to understand their unique, high-pressure world. 

That said, keep in mind that they are busy literally saving lives. They may not be available for every collaboration you’d want from them, so as part of your Day 1 involvement, settle on a cadence that gets your team the input you need while respecting their often busy schedules.

Tip #2: Onboarding Must Be a Part of Your Design

Take the time up-front to consider the onboarding process. People working in healthcare environments, from doctors to nurses to administrators, almost always have a great deal on their plates, and what’s on their plates is extremely important. Changing a process or asking people to adopt a new product can feel extremely disruptive. Even something people may ultimately find helpful and time-saving might gather proverbial dust if the channels of a routine run too deep — especially if it’s not explicitly clear why or how people should switch things up.

The single most important thing you can do to help onboarding is getting an internal champion — someone who believes in what you are doing and can support you in socializing it from within. Clinicians tend to place a high degree of trust in insiders who know through experience what their day-to-day life is actually like. Finding the key leverage points in the culture of an organization and getting them on your side will be critical to any onboarding/socialization plan. (In our experience, the most powerful shifters of culture are doctors and nurses.)

And of course, onboarding is not a one-and-done thing. Just as the design requires iteration, so too does onboarding. It’s important to continually go back to the front lines and tweak how the value prop and plan are described, paying attention to what yields the best adoption.

Tip #3: Design for Rapid Action

Healthcare providers frequently need to do things quickly. While efficiency is valuable in any situation, there are particularly time-sensitive moments in healthcare — like responding to a patient with a critical condition exacerbation.

In a recent project with one of the largest private healthcare organizations in the US, our task was designing tools for nurses to remotely monitor patients with postpartum hypertension. In our design, we asked ourselves how we could enable nurses to quickly identify which patients required their attention most urgently — digital triage, in essence. If a patient in a life-threatening condition was identified, we also asked ourselves how we could best support the subsequent action that needed to be taken. For patients with dangerously high blood pressure, we worked with our client on a system by which nurses could immediately alert not just the attending physician but also the patient and their circle of care. Nurses could act rapidly on the situation and also keep everyone connected.

Tip #4: Allow for Personalization

In the clinical field, there is a vast diversity in job functions as well as people’s way of doing things. Instead of making a one-size-fits-all solution, you’re better off locating and enabling key points of personalization that allow people to do their jobs in ways that suit their needs. 

We recommend interviewing wide sets of end users and attuning yourself to the subtle differences in process that could inform how you allow for personalization of the product. Some clinicians, for example, only need to view a subset of the patient population in order to do their job, and anything beyond that will be visual clutter. Some clinicians need to filter down by a particular biometric or health status marker. Some clinicians need to respond to issues in different ways than others. Building in personalization helps you meet people where they are and let them practice medicine the way they know best.

Tip #5: Support the Patient-Provider Relationship

The job of technology in healthcare should always be one of making healthcare providers more efficient and effective — not impinging upon or trying to replace a clinician’s relationship with their patients. No matter how “intelligent,” technology is ill-suited to replace this powerful and healing relationship. 

Focus instead on getting technology to solve lower-level problems so that providers can spend time on patients in need of their skill set. Focus on designing tools that enable high-impact interactions and offload low-level ones. Figure out ways to optimize and clear a path for what care providers do best. 

Want to learn how Grand Studio can help with your next healthcare project and build clarity out of complexity?

Drop us a line! We’d love to hear from you.