I clearly recall the first time our team rolled out a modern AI-powered assistant to a team of users. We were pumped – our testing showed the tool nailing every target benchmark we established at project kickoff. But not five minutes in, someone in the beta group said, ‘this is really clever, but I don’t understand why it provided these outputs or what I’m supposed to do with them.’ The room got awkwardly quiet. That moment crystallized the issue for me: If people can’t easily grasp or trust an AI solution, then all the fancy numbers about accuracy are meaningless. Without that crucial human connection, even the best AI initiatives are set up for failure.
The evidence for this is everywhere you look:
- Around 85% of AI projects never launch or don’t create lasting value, based on Gartner research
- Almost 90% of companies experience inconsistent success with AI implementations, according to Qlik
- More than half of the individuals participating in a recent KPMG study indicated they don’t trust AI recommendations
Contrast this disappointing reality-check with how design-led approaches historically perform.
Human Centered Design (HCD) is a problem-solving discipline that puts people’s needs, emotional states, and life context at the very center of every design decision. Studies have repeatedly shown this leads to faster adoption, quicker returns, and far less rework. HCD is not a theoretical value add, it can, through rigorous adoption, make the difference between a project that gets shelved and a product that becomes indispensable.
Is the Algorithm the Star or Supporting Actor?
AI can process data at impossible speeds; that’s pretty much its superpower. But AI only produces real-world value when humans can act on its outputs. In HCD, the very first thing we do is gain a precise understanding of user needs, problems, and environmental constraints before even considering what a solution might look like. This helps digital engineering teams:
- Solve actual problems, not invent tools nobody asked for
- Determine exactly where explanations or safety rails matter most to users
- Design features that simplify instead of overwhelm
Through the process of interviews and journey maps, HCD enables us to learn how people interact with technology day-to-day. This, then, helps us make decisions about which outputs an AI tool should produce, how it should communicate interactively with users, and, ultimately, how it should support human goals in ways that are genuinely useful.
HCD Helps AI Projects Thrive
Why are so many AI initiatives abandoned before they show value? Usually, it’s because the objectives weren’t clear, the data foundations were extremely messy, or users simply ignored the delivered solution. HCD directly confronts each of these potential pitfalls:
- It translates ambiguous goals into clear, measurable outcomes
- Workshops with SMEs and end users reveal which data truly matter (hint: more is not always better when it comes to training data)
- Early prototype feedback surfaces issues around trust and adoption well before they become expensive to rectify
Teams that quickly loop through cycles of “build, test, learn” have been shown to deliver features faster and more efficiently. This approach is especially important in times of fiscal uncertainty when investment decisions are confronted by a wall of doubt and concern. HCD helps provide proof, at the earliest project stage, of return on investment.
Building Trust in an AI Accelerated World
Trust is now the single largest obstacle to scaling AI solutions. Some are calling this the “last mile problem of AI”: people hesitate to act on recommendations they do not fully understand. Human Centered Design helps close this trust gap by wrapping every implementation in a layer of transparency and control:
- Explainability built in, not bolted on – Plain language tooltips, confidence metrics, and “Why am I seeing this?” tooltips transform a black box into an open book
- Rightsized autonomy – Users decide when to accept, tweak, or override AI suggestions so that the system feels like a partner, rather than an autopilot
- Bias prevention through diverse cocreation – Bringing a wide range of stakeholders from different backgrounds into the design process exposes blind spots before they become entrenched
When users can see how a recommendation was formed (we believe this is crucial), and feel empowered to correct it, their adoption rates skyrocket.
Turning Principles into Practice
Design ideals only matter if they survive the treadmill of the delivery process. We see three habits that separate successful teams from those with stalled proofs of concept:
- Storyboard the decision, not the screen – Before any wireframe work, map the precise moment a particular user role will engage with an AI output. Let that micro story dictate interface, timing, and tone
- Prototype in days, iterate in hours – Lightweight clickthrough demos paired with small “trust tests” identify potential adoption barriers long before expensive model training cycles
- Instrument for continuous learning – Automated sentiment analysis, passive data capture, and periodic expert reviews feed the enhancement backlog, ensuring the user’s voice never goes unheard and the solution always keeps improving
Together, these habits transform HCD from a “nice-to-have” concept into a repeatable delivery muscle that scales with every iteration and ensures outcomes are measurable and consistent.
Ready to Put Humans at the Center of Your AI Strategy?
Trissential’s Digital Engineering practice helps organizations blend Human Centered Design with cutting edge AI delivery. Whether you need a rapid Healthcheck or a full scale design to deployment partnership, we meet you where you are and accelerate delivery of your strategic goals. Let’s have a conversation and learn how HCD can help turn impressive algorithms into irresistible solutions.
Learn more about Trissential’s Digital Engineering Services and get the Healthcheck
Talk to the Expert
Brian Zielinski – Sr. Director, Digital Engineering
brian.zielinski@trissential.com
