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! 

Stretching Lean Budgets Strategically

Every business hits times when the budget gets tighter — it’s an inevitable part of being in it for the long haul. For a lot of industries, their short-term futures are a bit unpredictable right now, leading to questions about how to best set up their business to weather any twists and turns. 

In the face of uncertainty, many organizations scale back as quickly as possible to alleviate the pressure on their overhead. While understandable, rushed decisions can sometimes be short-sighted decisions, making it harder for those businesses to rebuild once lean times have passed. 

Just as strategy is important in times of growth, it’s also key in times of reduction. Whether you’re the one facilitating trims or absorbing them as best you can, read on for our take on putting strategy into leaner times. 

Center existing customers

While you can’t completely lose sight of expansion, the math is simple — it’s much less expensive to retain an existing customer than it is to acquire a new one. In moments when efficiency with available budgets is essential, the best move is often to invest the majority of your efforts in customer retention through the products, services, and/or tech systems your teams may already be running. This means maintenance, yes, but it also means uncovering new ways to provide benefits for them, ensuring they will return to you. Growth is important, and should not be forgotten, but it’s important to balance such endeavors with true investment in preserving what’s working for you today. 

Step back and learn, and go “lightweight”

We’ve seen huge payoffs for organizations that take budget setbacks as opportunities to zoom out on their business and take a closer look at their products and services. What makes the most sense to focus on in this new climate? Where is infrastructure/development urgently needed, and where can it wait? Which projects are going to best prepare the company for when the market forges ahead? In all likelihood, a change affecting your business also means changes for the partners and clients around you. How might these circumstances affect your short- and long-term success strategies? 

In lean times, it’s also very important to get to the learnings quickly so you can pivot if needed. Consider stepping back to ask what the scrappier, more agile version of your process might look like. You want to be investing efforts in the right places, so getting that feedback loop on a quicker cycle is key.  

Consider how projects are shelved

When an organization tightens the belt, it’s almost certain that internal priorities will need to shift. This often involves shelving longer-term projects, and refocusing resources to work on lower-hanging fruit that will generate income in the short term.

Once the worst of the budget drought has passed, though, most organizations will want to pick up where they left off on those shelved projects. The problem is that many times, the  employees with the institutional knowledge to restart those projects have been shuffled around in a reorg, laid off, or have left the company out of fear for the business’s future. Countless times, we’ve seen work either need to get redone because there was not enough context to pick it back up again — or, get restarted from scratch only to realize midway through that much of what they’ve worked on had already been done.

While it may not be realistic to avoid any kind of turnover or layoffs, consider using the lower-budget times to thoroughly document any mid-flight work that needs temporary shelving. This includes the work done to date, by whom, what was learned and the impact moving forward, and what still needs to be learned or done. Taking the time to do this in “quieter” times is hugely important to not wasting effort when your business is finally in recovery and expansion mode. 

Judicious use of outside help 

It’s hard to justify spending any money when your budget is limited. That said, given the overall fear of making the wrong decision that can pervade stressful times, it can be helpful to call on outside eyes for perspective and strategic support. Things like day-long prioritization workshops, short research sprints, or new tech trainings can be sensible ways to spend less money but still get a lot of impact and keep initiatives moving forward.

Another smart way to use outside support in tighter times is as short-term personnel augmentation. When you can’t commit to retaining FTEs for each role you need, hiring an agency can be a smart way to access a wide array of skill sets for less money.

Plan like the storm will pass — with the right strategy, you can help make sure it does. And if you’re looking for a partner in weathering that storm, we’d love to hear from you.

Leveraging AI in User Research

Grand Studio has a long history of working with various AI technologies and tools (including a chatbot for the underbanked and using AI to help scale the quick-service restaurant industry). We’ve created our own Human-Centered AI Framework to guide our work and our clients to design a future that is AI-powered and human-led and that builds on human knowledge and skills to make organizations run better and unlock greater capabilities for people. When ChatGPT hit the scene, we started experimenting right away with how it could improve our processes and make our work both more efficient and more robust. 

Given our experience with what AI is good at doing (and what it’s not), we knew we could use ChatGPT to help us distill and synthesize a large amount of qualitative data in a recent large-scale discovery and ideation project for a global client. 

Here are some takeaways for teams hoping to do something similar: 

1. Don’t skip the clean-up. As they say: garbage in, garbage out. Generative AI (GenAI) tools can only make sense of what you give them – they can’t necessarily decipher acronyms, shorthand, typos, or other research input errors. Spend the time to clean up your data and your algorithmic synthesis buddy will thank you. This can also include standardized formats, so if you think you may want to go this route, consider how you can standardize note-taking in your upfront research prep.

2. Protect your – and your client’s – data. While ChatGPT doesn’t currently claim any ownership or copyright over the information you put in, it will train on your data unless you make a specific privacy request . If you’re working with sensitive or private company data, do your due diligence and make sure you’ve cleaned up important or easily identifiable data first. Data safety should always be your top priority.

3. Be specific with what you need to know. ChatGPT can only do so much. If you don’t know what your research goals are, ChatGPT isn’t going to be a silver bullet that uncovers the secrets of your data for you. In our experience, it works best with specific prompts that give it clear guidelines and output parameters. For example, you can ask something like: 

“Please synthesize the following data and create three takeaways that surface what users thought of these ideas in plain language. Use only the data set provided to create your answers. Highlight the most important things users thought regarding what they liked and didn’t like, and why. Please return your response as a bulleted list, with one bullet for each key takeaway, with sub-bullets underneath those for what they liked and didn’t like, and why.” 

Doing the upfront human-researcher work of creating high quality research plans will help you focus on the important questions at this stage.

4. It’s true, ChatGPT gets tired. As with any new technology, ChatGPT is always changing. That being said,  the 4.0 version of ChatGP that we worked with demonstrated diminishing returns the longer we used it. Even though the prompts were exactly the same from question to question, with the input of fresh data sources each time, ChatGPT’s answers got shorter and less complete. Prompts asking for three synthesized takeaways would be answered with one or two, with fewer and fewer connections to the data sets. By the end, its answers were straight up wrong. Leading us to our final takeaway:

5. Always do an audit of the answers! Large language models like ChatGPT aren’t able to discern if the answers they provide are accurate or what you were hoping to receive. It’s also incredibly confident when providing its answers, even if they’re wrong. This means you can’t blindly rely on it to give you an accurate answer. You have to go back and sift through the original data and make sure that the answers it gives you line up with what you, the researcher, also see. Unfortunately this means the process will take longer than you were probably hoping for, but the alternative is incomplete, or incorrect answers – which defeat the purpose of synthesis in the first place and could cause the client to lose trust in you. 

Outcome: Did using ChatGPT speed up our synthesis significantly? Absolutely. Could we fully rely on ChatGPT’s synthesis output without any sort of audit or gut check? Not at all. We’ll keep experimenting with ways to incorporate emerging technologies like Generative AI into our workstreams, but always with research integrity and humans at our center. 

Interested in how GenAI might work for your organization? Drop us a line – we’d love to chat!

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. 

Using AI in Enterprise

AI is everywhere these days. There’s no escaping it whether it’s a board room conversation, a conference, or a meme on social media. And with good reason. As Bryan Goodman of Ford Motor Company said recently at the 2023 Reuters Momentum conference on AI, “I can’t imagine anyone being competitive without AI.” The resounding perspective across industries is in agreement with him.

The question amongst many people, particularly those in larger enterprise organizations with less scrappy flexibility and more risk, is how do we use AI in a way that is responsible to our business units, our shareholders, and humanity at large?

Download our whitepaper and read more about the best use cases and challenges to consider for enterprise AI usage.

How to Innovate for Long-Term Vision

Many of us have been in a position where, amid our day-to-day tactical work, there surfaces a nagging suspicion that some long-term organizational vision is needed. You know something major needs to change, that some kind of high-level ethos-tinkering could push your team forward… but nobody quite knows what that thing is.

Even if you manage to set up a brainstorming session to get some fresh ideas out, what often happens is that a few wild ideas will be thrown out, then immediately nixed due to (very real) feasibility or viability concerns. How does anyone get past these blockers to arrive at exciting — yet grounded — innovation?

What does a successful visioning process look like?

This is a common problem we tackle frequently at Grand Studio. While every project will have constraints to take into account, successful visioning typically boils down to 3 main things:

  1. Allow time to think expansively
  2. Keep your ideation separate from your editing
  3. Choose activities that match where your team is at 

We’ll take you through each one of these in a little more detail.

Think expansively 

The purpose of visioning is to let go of constraints and dream up potentially surprising solutions, knowing we can always come back to earth afterwards. The idea is to think big, to make the kinds of lateral, creative connections that generate game-changing ideas.

To do this, we recommend setting expectations and reminders with your team that constraints don’t matter right now. We also recommend helping your team practice expansive thinking with a warm-up. Very often, with our day-to-day jobs, our brains are trained to think pragmatically, and making the cognitive shift into a visioning session can be challenging.

Forced connections can be a great exercise for people new to expansive thinking. With this, you take 2 seemingly unrelated topics or items and force yourself to connect them in some way. You can do this with physical objects in the room you’re in, current events, items in people’s bags… anything at all. 

For example, say your first pairing is an iPhone charger and an apple from someone’s lunch. How could they be related? Maybe there’s a novelty phone charger that uses the energy from an apple to charge an Apple device… kind of like the potato-powered lightbulb of kids’ science experiments. You might start with a few silly pairings to get people in the groove, then start adding in pairings related to your business or user needs. 

Keep your ideation separate from your editing

Wild ideas can always be edited down. But applying too many constraints on imagination can end new ideas before they even begin. We recommend setting aside time for true ideation — even if it’s just a couple of hours — without letting feasibility concerns or fear of the unknown be the leading force in the room. 

To do this, have the group mutually agree upon what the dream-big phase of your ideation session will look like, and put everyone’s mind at ease by also setting expectations for what the editing down phase will look like, complete with agreed-upon deliverables.

Backcasting is a good technique for nailing down specifics that will eventually lead towards your collective vision. Keeping your future vision in mind, start working backwards and identifying the technologies, processes, people, and key milestones that would need to be in place to reach that goal. Keep working backwards in increments until you reach the current day. Now you can use these milestones when developing your roadmap or theory of change.  

Choose your ideation activities wisely

Jumping into the deep end with complex visioning exercises may be a lot for people who’ve never been involved with this kind of work before. We recommend that organizations newer to visioning exercises start off keeping it simple.

For organizations newer to visioning, try some exercises like:

  • Crazy eights: This is a quick sketching exercise that asks people to come up with eight ideas in eight minutes. The speed of this exercise forces people to let go of coming up with a perfect, edited idea, and instead dream up as many possible paths as they can. Looking back at what people shared, you’ll usually see patterns across participants that can illuminate new directions.
  • Exquisite corpse: You may have done this in school. Exquisite corpsing starts with each person creating an idea. You then pass each idea off to another person in the group, who adds on, or ideates a different part of the idea. This can encourage collaborative thinking and riffing off of others’ inspiration, as well as letting go of the impulse to step back and critically evaluate the whole picture.  

For organizations more comfortable with visioning and imagination activation, try:

  • Desired state storyboarding: If you had a magic wand, what would the ideal state of your service or product look like? How might people use it? What’s the ideal impact? How has society changed for the better? Create a narrative telling the story of your ideal state. This can be a drawing, a newspaper headline, or a storyboard. This can help everyone align on what the long-term dream would be, and set the stage to figure out the small steps you can take towards that vision.
  • Four Futures Scenarios: Making scenarios is similar to our desired states exercise above, but more in-depth. You can use these scenarios to think through multiple strategies, product ideas, and services to give your organization a wide range of action plans in order to proactively make your own future, instead of operating from a reactive place. Jim Dator at the University of Hawaii was one of the first to develop and popularize 4 key future scenarios that we can use when imagining alternative futures:
    • 1. Continuous Growth: the company grows infinitely and expands market share. (Tip: Stay critical! Think about when growth can be a negative thing for companies.)
    • 2. Discipline: the company faces significant limitations, either imposed internally or from other forces like government regulations.
    • 3. Collapse: the company struggles to survive due to things like market disruptions, economic recession, or other extreme factors. This future can be useful to think through disaster scenarios and work on “risk-proofing.”
    • 4. Transformation: the company changes something fundamental, such as its mission or ownership. This scenario is most exciting when thinking about ways to innovate and find something new. You can use Transformation scenarios to think about radical new directions your company can pursue in order to stay one step ahead of market trends.  

Now take a step back and look at all your futures. See any common themes or patterns? New product lines or directions your company could go that are exciting and generative? Pick one, or a couple, and try backcasting from it to see what steps you would need to take. 

Of course, there are plenty of ways to expand beyond these ideas into some really creative and out-there visioning exercises, but our advice is to start small to get your organization acquainted with what visioning can yield, and also build their faith that creativity doesn’t mean letting go of practicality. Once there is more of a culture around visioning and imagination, you can try exploring larger, more “out there” techniques. 

Bonus: Get an outside perspective

Sometimes, working with an outside organization can help with visioning. While no one will understand your organization better than you, outside eyes can help push past some preconceived notions and generate new ideas. They can also help set the tone for what visioning can look like, shepherding unfamiliar or suspicious parties towards the light of imagination.

Got a visioning project you’d like to collaborate on? We’d love to hear from you.