Andrew Gold December 9, 2024

How to Embrace Design in 2025: UX Trends You Need To Know

A new year is around the corner, and with it comes a wave of new opportunities for organizations to step up their game in digital strategy and product design. Whether it’s delivering unforgettable user experiences, empowering employees with better tools, balancing needs across omnichannel experiences, or ensuring accessibility for everyone, the trends shaping the future of design and UX are all about creating meaningful, impactful solutions.

If you’re looking to design products that truly resonate—whether for your customers, employees, or the world at large—these are five big trends to keep on your radar. Let’s explore how each can make a difference and what you can start doing to stay ahead.

1. Personalizing user experiences with AI

Let’s be honest—personalization is no longer a luxury. People now expect apps, tools, and services to know what they need and deliver it before they even ask. AI is the engine behind all of this magic, enabling products to tailor experiences based on user preferences, behaviors, and goals.

Why it matters in 2025
The evolution of generative AI models, such as ChatGPT and Bard, has transitioned from experimental phases to now practical applications. AI-powered personalization makes users feel understood and valued, which builds trust and loyalty. Whether it’s a business tool that learns a user’s workflows or an app that suggests relevant next steps, personalized experiences are becoming a baseline expectation. In 2025, expect enterprises to integrate these AI-driven personalization tools into large-scale platforms, like CRMs and ERP systems—moving beyond pilot projects into full-scale deployment.

How to get started

Be transparent about data: users are more likely to embrace AI-driven personalization if they trust you. Be clear about what data you collect, how you use it, and how you’re keeping it safe.
Think multi-channel: users interact across multiple devices and platforms, so make sure your AI systems provide consistent experiences everywhere.
Start small, but think big: begin with focused personalization features that add clear value, then expand as your systems and data capabilities grow.

By leaning into AI-driven personalization, you’ll not only meet user expectations but also create a smoother, more intuitive experience that keeps them coming back.

2. Turning data into action with analytics

Data has been a buzzword for years, but in 2025, it’s all about what you do with it. Advanced analytics tools are giving enterprises the power to understand users better, predict behaviors, and make decisions faster. It’s like having a crystal ball for UX.

Why it matters in 2025
Customers and employees expect seamless, problem-free interactions. Advanced analytics can spot issues before they happen, helping you design systems that are proactive, not reactive. Imagine catching a bottleneck in your user journey before anyone complains—that’s a sign of better control over your systems. The introduction of new tools and APIs in late 2024 has made real-time analytics more accessible. This development enables organizations to implement predictive analytics practically, thereby facilitating better proactive decision-making.

In addition, the growing adoption of AI in analytics provides businesses with prescriptive guidance, not just raw data. In 2025, enterprises will leverage AI to receive more actionable recommendations, a capability that was still somewhat emerging in 2024.

How to get started

Focus on meaningful metrics: don’t just track everything—identify the user behaviors that matter most and use analytics to monitor and improve them.
Bring everyone on board: make analytics insights accessible to all your teams—designers, developers, and decision-makers. This way, everyone can act on the data.
Embrace iteration: use analytics to test, learn, and tweak your designs. The best products are always improving.

When you use analytics to guide your decisions, you can create experiences that feel smooth, intuitive, and perfectly in tune with what your users need.

3. Digging deeper with digital ethnography

If you want to design products people truly love, you need to understand not just what they do but why they do it. That’s where digital ethnography comes in. This method lets you study how users interact with your product in real-life situations, giving you insights that surveys and focus groups simply can’t.

Why it matters in 2025
User behaviors are more complex than ever, and traditional research methods often miss the nuances. Digital ethnography lets you see your product through the user’s eyes—literally, if you’re using tools like video diaries or screen recordings. Post-pandemic, remote and hybrid work models have mostly stabilized, allowing researchers to fully embrace digital ethnography. In addition, the development of new mobile and wearable technology in 2024 has enhanced the ability to collect rich, context-aware user data in real time. By 2025, these tools will become widely available, making digital ethnography scalable for enterprises.

How to get started

Make it easy for users to share: use tools that let participants capture their experiences naturally, like mobile apps for documenting tasks or workflows.
Act on what you learn: turn insights into actions—whether that’s simplifying a confusing workflow or addressing an unmet need.
Keep listening: user needs evolve over time, so make digital ethnography an ongoing part of your design process.

By truly understanding your users, you’ll be able to create products that not only meet their needs but feel tailor-made for their lives.

4. Building better tools for employees

Let’s not forget—employees are users, too! They need tools that are as seamless and intuitive as the customer-facing products you create. Unfortunately, enterprise tools often lag behind in user experience, which can lead to frustration and inefficiency. At this point in 2024 there is now a heightened recognition and acknowledgment that applying consumer-grade design principles to employee tools directly impacts productivity, retention, and business outcomes. We expect this investment in employee tools to continue on into 2025.

Why it matters in 2025
In a hybrid work world, employees rely on technology more than ever to stay connected and productive. In 2025, refined solutions will integrate productivity, collaboration, and well-being features into cohesive ecosystems, addressing the evolving needs of the workforce. By investing in tools that prioritize ease of use, collaboration, and well-being, you’ll not only boost productivity but also show your employees they’re valued.

How to get started

Ask employees what they need: don’t guess—conduct quant and qual research to understand workflows, pain points, and opportunities for improvement.
Blend function with delight: employees are used to slick consumer apps. Bring that same level of polish to your internal tools.
Measure and refine: use analytics to track how employees engage with tools and refine the experience to better meet their needs.

Great and easy-to-use tools lead to happier employees—and happier employees create better outcomes for your customers and business.

5. Designing for accessibility and inclusion

Accessibility isn’t just about ticking a box—it’s about making sure everyone can use and enjoy your product. From people with disabilities to those in different cultural or linguistic contexts, designing for inclusivity creates better experiences for all users. Designers have been voicing the importance of this for years and now we are at the point where it is becoming more of the norm inside design processes to consider a wider variety of user types.

Why it matters in 2025
Accessibility is no longer optional. Updates to the Americans with Disabilities Act (ADA) and European accessibility regulations in late 2024 have certainly brought accessibility to the forefront. It’s a legal requirement in many places, but beyond that, it’s simply good business. When you design with inclusivity in mind, you expand your audience, build goodwill, and create more equitable experiences.

How to get started

Test early and often: don’t wait until the end of the design process. Test for accessibility at every stage to catch and fix issues before they become major problems.
Think beyond compliance: standards like WCAG are a starting point, but true accessibility means creating delightful, intuitive experiences for all users.
Educate your teams: make sure everyone involved in product development understands the principles of accessible and inclusive design.

Furthermore, AI-driven accessibility solutions have become more viable, enabling companies to detect and address accessibility issues in real time during product development—a capability that was not reliably available in 2024. Accessibility isn’t just the right thing to do—it’s an opportunity to innovate and create products that work better for everyone.

Looking ahead to 2025

These trends—AI-driven personalization, advanced analytics, digital ethnography, employee experience tools, and greater focus on accessibility—are reshaping the way organizations think about user experience. By embracing these ideas now, you’ll be ready to build smarter, more inclusive products that delight your users and drive your business forward.

At Grand Studio, we’re here to help you navigate these trends and design solutions that make a difference. Let’s make 2025 the year you transform the way your users experience your products.

Grand Studio October 21, 2024

The Ideal GenAI Design Process

This is the second 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.

Hopefully you’ve already read our Checklist for GenAI Readiness and you are following our best practices: from managing expectations, to cleaning up your data, to building in time for testing and embracing the whimsy of an exciting emerging technology. Let’s move on to the next step and talk about tips that can properly define an ideal design and project process for your GenAI product.

Here at Grand Studio, we tend to follow the classic double diamond design thinking process, defined by the British Design Council in 2005. We use this to guide our clients through a design process that focuses not just on making sure we’re designing something that works and is reliable, but something that people trust and are excited to use.

Let’s dive into some of the major phases that can define an ideal GenAI design process.

Phase 1: Definition + Discovery

Does a GenAI solution effectively solve user problems?

We’ve said it before, and we’ll say it again: While GenAI is a useful, exciting technology, it isn’t guaranteed to be the answer to every problem under the sun. Here are some examples of what it can do now:

Automate repetitive, tedious tasks such as writing boilerplate emails, or scheduling meetings
Create outlines, write basic copy, and provide inspiration and ideas into the process
Create customized images – some of which can even be generated from any of your existing illustrations

And here are some examples of what GenAI is not quite suited for just yet:

Replacing human decision-making processes, or making suggestions that require long-term, contextual strategic thinking
Fact-check itself against hallucinations or bias due to the technology’s limitations in understanding the “truth”
High degrees of nuance and problem-solving. Although many LLMs have passed crucial benchmarks set by different data sets and exams that we use to test people (such as the MKAT, SATs, Bar Exams), they’re not really capable of fully autonomous thinking (yet…).

As you and your team consider the possible applications for GenAI in your work, make sure you’re not replacing the difficult, nuanced, creative work that humans are uniquely capable of doing with a technology that is not yet well-suited for it. You will end up with unhappy employees, unhappy customers, and certainly unhappy technology.

Phase 2: UX/UI Design

What should the user interface and experience for your GenAI product look and feel like?

Now that you have identified and selected GenAI as the right technology for this project, it’s time to figure out how your users might interact with it. Understanding how GenAI will integrate into existing – or brand new – products is an emerging field of UX/UI design. We’re seeing everything from AI-enabled UI design engines that create UI from a user’s text or sketch input, to straightforward chatbots, to DJ players built on text inputs, to agentic co-pilots.

There are so many creative ways that users can interact with GenAI, and part of this initial building phase is to take the learnings from the Definition + Discovery Phase to understand what kinds of interaction will make the most sense to your users, solve their problems, and delight them. You and your team should ensure that the interface follows basic UX/UI heuristics, is user friendly, and intuitive. Don’t be afraid to use existing patterns, brand guidelines, voice, tone, and personality from familiar applications and products – but think carefully about what is unique about the interaction between your user and the AI and how it can become an extension of your brand that delights users. We’ve found that it’s best to implement incrementally and integrate into existing systems, as opposed to making something brand new. Even though this is an exciting, emerging space for UX/UI design, the same foundational principles still apply!

Phase 3: Beta Test

How do users interact with and understand the product, and what is required to build trust in the technology?

For anyone who has introduced a new tool or technology to users, you know that people don’t always react or understand the technology in the same ways, or even ways that you could have anticipated! Add the current range of attitudes regarding GenAI – and the complexity of emotions people have when using it – and this effort starts to invite risk upon launch.

Fortunately, to mitigate all of those risks we would recommend conducting a round of thorough beta testing to understand how your users will understand, interact with, and trust (or mistrust) your GenAI product. The goal is to learn more about your product and your users at the same time. Here are some things to monitor as you run a Beta test:

Prompting: What are the prompts people are using to interact with the GenAI? Do they understand how to edit or adjust their prompts when they get an unexpected, or unhelpful response?

Consider: Think about creating a basic prompt library of pre-written prompts according to common use cases. These can help empower users and ease their comfort levels using GenAI. Another plus is that the product will work more reliably!

Benchmarks: Speaking of reliability, it’s crucial that everyone is on the same page about how success for this product will be measured. Beta testing is a perfect opportunity to start seeing how well the product solves the problems you’ve designed it to solve.

Consider: You can set some pre-established benchmarks by checking your model against existing industry model benchmarks and comparing how your model measures up. Keep in mind that these benchmarks will change if you are using a customized model, and that the models themselves are constantly changing and improving.

Change Management: People have lots of feelings about GenAI, and you can’t necessarily blame them. GenAI is constantly in the news as either the answer to the world’s problems, or signaling the end of people’s jobs. You’ll want to be sensitive to this when launching your product, and beta testing is a good opportunity to ask your users how they feel.

Consider: Plan to add interviews or an open-ended survey to your beta test to surface what level of experiences people already have with GenAI, what they think about it, how much (or little) they trust the technology and its outputs, along with anything else that you think will make a difference in your adoption strategy once you reach product rollout.

Phase 4: Refine + Deploy

How does the product work in the real world?

The final phase of your GenAI product is getting everything you’ve made, tested, and refined out into the real world. You will want to make sure you’ve incorporated all the research findings you’ve uncovered along the way.

Make sure you’ve built in methods of continuous improvement and measurement. The last thing you want is any ill confidence around your product’s ability to function correctly or – even worse – causing problems somewhere else. One thing we’ve done in the past at Grand Studio is build in short surveys at the end of GenAI-enabled chatbots that ask for feedback specifically about how the GenAI performed. We can then use that feedback to refine our model and measure reliability and user trust.

Finally, ensure you have your best testing-informed change management plan in place once the product is deployed. In our experience, complex matrixed organizations can face a lot of tool fatigue (when new internal products and tools are constantly being rolled out so fast that people aren’t sure of what they do, or how to use them). An effective change management process can help guide people toward trust and understanding once the tool is out there.

Need help with the design process for your GenAI project?

Grand Studio can help. We use a tailored approach to work with you to define and fully understand your problem space and how GenAI can be best utilized to solve problems for end users. Be sure to stay tuned to the final part of the series: Advocating for the Human in a GenAI World.

Grand Studio September 25, 2024

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

Grand Studio August 23, 2024

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.

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.

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.)

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).

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