What if artificial intelligence didn’t just work for us, but with us?

In the last few years, AI has transformed from a hypothetical concept into a practical tool used to drive real-time decisions across various industries. From detecting illnesses in hospitals to assessing financial risk and personalizing shopping experiences, AI is everywhere.

But are these AI systems truly designed for people? Or, are we just adjusting to machines?

This is the reason more and more organizations want to adopt Human Centered AI (HCAI) development. It builds human values, usability, and oversight into the AI development process, without replacing humans, but empowering them with smarter, safer technology. 

In this blog, we’ll explore what people-first AI really looks like and how it differs from typical AI, where it is already being applied, and why it is essential to creating trust and usability for people.

What is Human Centered AI?

HCAI is the approach to designing AI systems that put people first, prioritizing human needs, values, and experiences from the very beginning. Rather than focusing solely on speed or accuracy, it considers how AI will interact with real users in real-world contexts.

This means more than just user-friendly interfaces. Human-centric AI asks deeper questions: Is this system fair? Can people understand and trust it? Does it align with human goals, not just machine logic?

At its core, it is:

• Ethical: It aligns with human values, societal norms, and long-term well-being.
• Usable: Interfaces are designed for real people, not just data scientists.
• Transparent: It avoids the “black box” effect by explaining decisions clearly.
• Collaborative: It supports human involvement and feedback throughout its whole lifecycle.

HCAI views humans as collaborators, rather than as data points or end-users, as traditional AI would generally do. It's about designing systems that are transparent, accountable, inclusive, and responsive to feedback.

In summary, AI, which works in partnership with people, shifts the frame of reference from what machines are capable of doing to considering what machines should do for the benefit of the people relying on them.

Human-AI Collaboration: Strengths, Roles, and Synergy

Humans and AI are complementary to one another. Tasks that can be done based on data will be done faster, at scale, and more precisely by computers, but humans add empathy; humans add a level of critical thinking and moral judgment; humans add the ability to understand what they already know; humans add context. In a thoughtful design, AI can embody and amplify the best of what humans do, and not eliminate.

This collaboration is called "augmented intelligence." Think of this as technology that empowers and helps us think, but does not take over the jobs that we do.

Why Augmented Intelligence Matters

• AI is quick to do the 'heavy lifting' like making sense of and processing millions of data points in seconds.
• Humans guide the system, asking the right questions and interpreting results with nuance.

Together, augmented intelligence uses technology as a partner to provide solutions and improve understanding, especially in industries that involve significant risks, like healthcare, criminal justice, or finance.

Beyond Technology: Building Trust

A computer may include an 'explanation sensor', but trust does not magically happen. They are and will be autonomous agents, no matter what we put into them. That is why "human-centered" AI systems will explicitly create feedback loops so that human users can correct mistakes, validate appropriate responses, or even explore "WHY did this happen." Creating transparency creates trust, and trust encourages adoption

A great example of this is in mental health support. On peer-to-peer counseling platforms, researchers tested an AI that offered real-time suggestions to improve empathy in conversations. The result? A 19.6% increase in conversational empathy, showing how AI can actively enhance human compassion. Source

IBM’s research backs this up. Their development of AutoAI, a tool designed to support data scientists, showed that when AI systems are interpretable and responsive, they help humans produce better models faster, with fewer errors. As IBM puts it, “the future of data science is a collaboration between humans and AI systems.” Source 1, Source 2

Systems must be designed with the ability to capture feedback loops so that they learn from their users (the humans) and change to meet the needs of those users (the humans!). When humans understand the reasoning behind AI’s decisions and feel like they are part of it, they are more likely to adopt it and trust it.

Human Centered AI vs Traditional AI: What’s the Difference?

At first glance, every AI system may seem to be the same: you put data in and get predictions out. However, HCAI takes a wholly different approach from traditional AI. Traditional AI systems often prioritize AI automation, such as optimizing for speed, accuracy, and cost savings, sometimes at the expense of usability, transparency, and ethical oversight. 

Where traditional AI attempts to solve the problem in the quickest manner possible, user-focused artificial intelligence prioritizes usability, equitable (and fair) affordances, and ethical alignment. Here’s how the two approaches differ:

And this shift isn't just theoretical, it’s becoming a business imperative. A McKinsey survey found that 40% of organizations saw a lack of explainability as a top risk in adopting generative AI, yet only 17% were actively addressing it (Source). That gap highlights the urgency of shifting toward more human-aware systems.

Top 5 Challenges of Adopting Human Centered AI in Enterprises

Designing AI that truly serves human needs is a powerful vision, but it comes with real-world complexity. While the benefits of user-focused AI are clear, implementing it at scale presents several nuanced challenges.

1. Bias in Data and Design

One of the most enduring challenges in artificial intelligence is the presence of systemic bias, both in the data used to train models and in the design assumptions embedded in algorithms.

In December 2024, The Guardian exposed a UK government AI system used to vet Universal Credit welfare claims. An internal analysis revealed that the system disproportionately flagged individuals based on age, disability, marital status, and nationality, raising serious concerns of unfair bias. Though the Department for Work and Pensions (DWP) asserted that human caseworkers make final decisions, critics pointed out that the algorithm lacked a comprehensive investigation for biases related to race, sex, or religion. Source

Such biases not only lead to discriminatory outcomes but also expose organizations to legal liabilities and reputational damage, especially when AI is used in sensitive domains like hiring, law enforcement, or healthcare. 

2. Ethical Trade-offs vs. Performance Metrics

Businesses often want to maximize accuracy and efficiency as they aspire to reach the pinnacle of their markets. However, the absence of early inclusion of ethics can backfire on them. The operationalization of an explainability feature or fairness constraints could negatively impact raw model performance; however, it could improve user trust and help align expectations with regulators. Ethical design will require teams to expand the definition of success beyond just precision.

3. Complex Design & Development Cycles

People-first AI isn’t a one-shot effort. It necessitates an inclusive data set, ongoing user input, and cycles of iterative design. So it is a slower process that uses more resources in terms of time and money than a traditional AI that focuses on “output” rather than “usability”. This means time and budget need to be allocated to usability testing, accessibility, and user onboarding considerations, all areas we often neglect.

4. Cross-Disciplinary Collaboration

In order to develop systems that are rigorously technically sound and ethically aligned, collaboration must occur across developers, designers, ethicists, and users. But this is easier said than done.

Oftentimes, even when developers, designers, and ethicists come together, they throw up walls of misunderstandings that slow the momentum of creating these solutions. If the various disciplines don’t have a common lexicon or goal, though they might have the best intent, they stop in their tracks. It’s a cultural shift as much as a technical advancement.

5. Lack of Explainability

Today, many AI systems are still "black boxes." It is difficult to have trust in a decision, whether it is a loan denial or a medical recommendation when a user does not understand the basis of the decision. Transparency is a major barrier to adoption, even more so in high-stakes domains, such as with human lives, rights, or finances.

How Human Centered AI Drives Trust, Adoption & Business Value

Even with extra work and complexity, user-focused AI has far-reaching effects. When people are put at the core of the AI design process, the benefits are about much more than a better AI system; people also get better relationships, reputations, and outputs.

1. Builds Trust and Drives Adoption

Trust is everything. When users understand what an AI-enabled system can and is doing and see that it is working in their best interest, they are far more likely to use it. This is especially true in industries such as finance and healthcare.

At Mastercard, they combine AI with a human component to add accountability to a very fast AI-enabled process for fraud detection, with dozens of AI agents analyzing billions of transactions. Source

2. Reduces Risk of Misuse

AI systems lacking ethical guardrails can create possible unintended and harmful outcomes. Human-centric AI encourages ethical principles like accountability, transparency, fairness, and governance as a baseline that acts as a mitigant against violating laws, creating biased AI outcomes, or suffering reputational repercussions. In addition, an emphasis on responsible AI design also provides organizations with a safeguard against both public relations fallout and the costs of non-compliance.

3. Promotes Inclusion and Accessibility

AI that recognizes how diverse users experience their surrounding world creates a more accessible and inclusive digital world. AI products geared toward diverse users, like Microsoft's Seeing AI, which reads the world for visually impaired users, illustrate how intentional design can help underserved communities while also extending the reach of the product.

4. Supports Long-Term Scalability

AI that is designed & developed with the users in mind is much easier to evolve and scale over time. These human-centered frameworks provide feedback loops, modular design, and ethical algorithms, enabling organizations to more easily update AI while aligning with changing users' needs. Such AI scalability makes AI systems more sustainable & relevant across use cases and markets.

5. Enhances Brand Loyalty & Mitigates Legal Liabilities

When customers are treated with respect and receive protection, they remain loyal. AI designed for real-world impact drives brand equity by exhibiting responsibility and care. It mitigates branded risk due to potential legal disputes about bias, discrimination, or transparency issues, making it easier to hold organizations accountable by regulators across the world.

Human Centered AI Implementations in Practice

HCAI has implications for more than one sector or industry. It's altering how organizations operate throughout healthcare, finance, retail, education, and the public sector. By adhering to principles of transparency, usefulness, and inclusiveness, businesses are developing AI systems that genuinely support their end users. Here are some ways in which it is transforming each sector:

Healthcare

In healthcare, from diagnosis to post-operative care, human AI is changing how care is delivered, not by substituting physicians, but by empowering them. These systems strengthen the physician-patient relationship using empathy, transparency, and more intelligent insights.

Doctronic is an AI platform that captures and analyzes an abundance of patient data to contextualize what the doctor will be facing in their workflow.  By the time the doctor begins their appointment, they will be armed with contextual insights that the patient has reported and which the AI has provided to the doctor, which they will be reviewing in a very short time. These appointments can be more productive and feel more personal, while also reducing time. Source 

Another company with a similar aim is Surgery Hero by C2-Ai. Surgery Hero recognizes that surgical wait times are a growing global challenge. Rather than have patients waiting around anxiously, Surgery Hero uses AI to evaluate individual health risks and gives patients the ability to prepare and improve their health while they are waiting. The advice is personalized, actionable, and feels supportive rather than an automated response. The focus is on empowering patients to control their health prior to entering the operating room. Source

These examples illustrate the progression of people-first AI in healthcare. Human Centered AI enables increased access to information and supports more personalized care, both of which lead to healthier outcomes and trust between patients and providers.

Finance

In finance, the decision points for AI can fundamentally change individual futures - it could be approving a loan, flagging a transaction, or steering investments. For that reason, trust and transparency are essential. User-focused AI is helping financial institutions do the work to make their systems not just smarter but also more explainable and ethical.

Take UBS, for example. The global financial services firm has created AI-designed analyst avatars - digital replicas of real analysts who present their research findings through video. The avatars reflect the tone and communication style of these analysts. This increases efficiency while keeping the experience human and relatable to clients; an actual face in a world of data. Source

Capital One, through the USC-Capital One Center for Responsible AI and Decision-Making in Finance, is taking a bottom-up approach to the challenge. In collaboration with the University of Southern California, the company created a Responsible AI Center with the vision to advance transparent, fair, and accountable AI best practices. The initiative is placing emphasis on integrating governance with innovation, not in the back-end or as an afterthought, but from day one. Source

These initiatives reflect a broader trend: financial institutions are realizing that transparency and fairness are a competitive advantage. When customers understand how decisions are made and feel confident in the fairness of the process, trust grows. And in finance, trust is everything.

Retail

Retail thrives on personalization, and human-aware AI is taking it to the next level. Instead of treating shoppers as data points, today’s AI solutions are designed to understand preferences, predict intent, and adapt in real-time. 

Sephora’s Color IQ system takes personalization to the next level. Using a handheld scanner, it identifies a customer’s exact skin tone and matches it to suitable products from a shade library. This unique code connects across in-store, online, and mobile shopping, offering tailored recommendations that boost confidence and simplify decision-making, turning personalization into brand loyalty. Source

Similarly, The North Face uses IBM’s Watson to power an AI shopping assistant that helps customers find the perfect jacket. Instead of sifting through dozens of options, users answer simple, natural-language questions like “Where are you going?” and “When will you wear it?” The system then offers tailored suggestions, removing the overwhelm of too many choices and making the experience feel more like chatting with a knowledgeable store associate. Source

Public Sector

Governments and public institutions face a unique challenge: they must serve everyone, not just specific market segments. That’s why Human Centered AI is especially critical in the public sector to ensure fairness, inclusivity, and transparency in systems that impact millions of lives.

In the UK, the government is set to introduce an AI tool called Consult to help analyze public consultation responses. Normally, sorting through thousands of citizen submissions is slow and costly. Consult uses natural language processing to cluster and summarize feedback, preserving not just content, but the nuance and intent behind what people say. The result is faster decision-making that still feels representative and inclusive. Source

Meanwhile, Deloitte is also pushing boundaries with affective computing, which reads emotional cues like tone and sentiment during digital interactions. Whether it's a housing application or benefits inquiry, this technology allows agencies to respond in a more considerate way that isn't limited to checkboxes and ticks but is real human understanding. Source 

These initiatives are showing that Human Centered AI not only improves efficiency but could rebuild trust in public services. When people feel like they are seen, heard, and respected, they are more likely to participate, which makes the government more effective for everybody.

Education

Education is very personal. Learners learn in different ways, at different paces, and in different circumstances. This is why AI with human oversight in education is largely about making learning more inclusive, accessible, and meaningful to everyone.

No More Marking, a U.K.-based education organization known for its comparative judgment approach, is pioneering AI-supported writing assessments through its CJ Lightning project. In a large-scale trial involving over 5,000 students, the organization demonstrated that AI can reliably mirror human judgments in evaluating student writing. Rather than replacing educators, the system reduces grading time, freeing teachers to focus more on instruction and individualized feedback, while still keeping them meaningfully involved in the assessment process. Source

Audemy has produced a platform designed for blind and visually impaired students. Their AI-enhanced audio-based learning system allows educators to personalize learners' content delivery with its natural language processing and voice recognition software. For learners who, for too long, have been neglected by traditional means of education, this marks a significant shift towards equity. Source

These examples emphasize what's possible when AI is designed to support human capacity, rather than human specification. Complicated HCAI helps learning happen in ways that fit each student, instead of forcing a one-size-fits-all approach. This makes education more flexible, inclusive, and easier to scale.

Real-Life Human Centered AI Examples 

Human-Centric AI is already at work in products and platforms we interact with daily. These examples demonstrate how leading organizations are embedding transparency, inclusivity, and ethics into their AI systems, proving that tech can be both smart and human-aware.

1. IBM Watson Health – AI That Supports, Not Replaces

IBM's Watson Health demonstrated empathy-led AI providing transparent, data-driven insight to support, not replace, clinical decision-making. In high-stakes areas like oncology, Watson Health aimed to inform clinical decision-making by cross-referencing vast resources, including medical literature, real-time patient data, unstructured data (e.g., doctor notes), and social determinants of health. Source

Emphasizing explainability and how AI was best suited to be transparent while working collaboratively with clinicians, provided trust in the integration of AI when making patient care decisions. It illustrated how intelligent systems are designed to augment human-centered decision making rather than being treated as a black box. 

2. Google People + AI Guidebook – Designing With Empathy

This is a hands-on guide for creating responsible, human-centric AI, created by the PAIR (People + AI Research) team. Built on empathy and inclusivity, it provides concrete frameworks for designing AI systems that are understandable, just, and capture actual user needs. Through research, design-based ideation, and open-source resources, PAIR empowers teams to create AI that honors human context, not just machine intelligence. Source

3. Woebot – AI for Mental Health Conversations

Woebot is an AI-powered mental health companion that seeks to make emotional support radically accessible. Built on empathy and clinical research, Woebot facilitates chat-based conversations with users to help them deal with difficult feelings in the moment. Woebot is not meant to replace therapists, but complements care by providing an always-on judgment-free space for early support, meeting people where they are, when they need it most. Source

4. Microsoft Seeing AI – Accessibility Through Narration

Seeing AI, developed by Microsoft, provides a portable virtual assistant to someone who is blind or has low vision. When you point the camera of the device, it automatically begins identifying the world around you. It will describe everything from reading a street sign, to identifying an individual, including their face, to telling what currency you are holding when shopping.

It's effective, natural, and has been developed to increase everyday independence. More than just a cool piece of technology, it is a good example of how to create thoughtful ways of enabling people through AI development that make people feel included. Source

5. Furhat Robotics’ Tengai – Reducing Bias in Hiring

Tengai, developed by Furhat Robotics, is no ordinary interviewer. It’s a social robot designed to make hiring more fair and inclusive by doing something surprisingly simple: treating every candidate the same.

Used by recruitment firm TNG, Tengai conducts structured interviews with a consistent tone and language, helping to reduce unconscious bias that often creeps into human-led hiring. Built on Furhat’s powerful conversational platform, it’s a compelling example of how robots can support more equitable decisions, not by replacing humans, but by helping us overcome our blind spots. Source

6. Salesforce Einstein – Transparent Sales Insights

This integrates AI into the CRM platform to offer lead scoring, forecasting, and personalized recommendations. Unlike some sales tools that offer “black box” predictions, Einstein emphasizes explainability, letting users see why a lead was scored a certain way, increasing adoption and trust within sales teams. Source

These real-world tools show that when AI is designed with humans in mind, it doesn’t just work, it connects. 

Let’s Make AI Work With People, Not Just For Them

With the increasing prevalence of AI in our daily lives, it also becomes abundantly clear that the best systems are not simply fast, but for people. Human Centered AI is not about fancy bells or whistles; it is about creating technology that listens, learns, and honestly wants to help the person using it.

We have seen what is possible: AI, which helps doctors connect better to their patients; tools that make hiring more equitable; and tools for mental health in times of need. These examples are not only examples of "something we want," but they are all examples of empathy, transparency, and trust at the center of innovation. 

Whether you are building AI, managing AI, or figuring out how to traverse this new experience as part of your organization, we invite you to remain curious. Ask how the AI was designed, ask who it was designed for, and provide feedback when something feels off. The more we interact, the more these tools will evolve and improve. 

If you are ready to explore what people-first AI can look like for your organization, or if you simply need to take a small step toward growing your understanding, start the conversation today. If you're looking to implement human-centric AI, we can help! 

Talk to our experts today and see how Tredence AI consulting can help you understand how to design and scale AI with trust, adoption, and sustainable value.

FAQs

1. How do I know if my current AI systems are human-centered?

Assess your systems using the following questions: Are they explainable? Do users have a way to provide feedback? Was usability testing done with real people? If your answers are mostly "no," then your AI could benefit from incorporating more human-centered design principles.

2. Does Human Centered AI take more resources than traditional AI?

There may be more resources "up front," as you'll likely have additional time spent on user testing, stakeholder input, ongoing feedback, and ethical review processes. Rather, human-centered design principles enhanced the value of the investment by reducing possible rework, increasing user uptake, and avoiding reputation and regulatory concerns down the line.

3. Can I use the human-centered design principles on legacy systems?

Definitely. While legacy systems may not have been designed with HCAI principles in mind, it is possible to add in some human-centered design elements over time through enhanced transparency, some feedback mechanisms, and some human-in-loop elements, without a complete redesign

4. What type of teams or roles are needed for Human Centered AI to be implemented?

You will need a cross-functional team that consists of data scientists, UX design professionals, ethicists, domain professionals, and end users. It isn't a tech project - it's a people-first project that requires wide-ranging perspectives.

5. Can small businesses or startups afford to implement HCAI?

Yes, especially if they consider explainability, usability, and inclusive design early on as important elements. Starting with small implementation steps on solid ethical foundations will allow small businesses and startups to avoid redesigning something that is costly and build user confidence from the beginning.

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Editorial Team
Tredence