When the combustion engine was invented, it did more than make travel faster. It fundamentally reshaped mobility, revolutionized industries, and sparked decades of innovation, leading to new developments such as the interstate highway system and affordable air travel – transformations that have altered the way people live and work. Today, AI is stepping into a similar role as a transformative technology.
With its ability to process vast amounts of data at unprecedented speeds, automate complex tasks, personalize experiences, and continuously learn, AI enables capabilities previously unimaginable: It can scale human intelligence, create new experiences, and unlock a future of boundless innovation. Recognizing AI’s transformative power, the critical question becomes not just what AI can do, but how we can harness its potential to create products that make these capabilities meaningful.
Today, 72% of organizations are using AI in at least one business function (McKinsey). The urgency is real. Leaders are under immense pressure to show progress, stay competitive, and respond to rapidly evolving expectations. However, in this race, many fall into the trap of launching scattered pilots that lack strategic cohesion, resulting in a portfolio of costly experiments with little or no ROI. This isn’t due to a lack of ambition or funding, but rather a flaw in approach.
To succeed, organizations must go beyond using AI merely as a tool and instead build with AI at the core. AI-first thinking aligns technological potential with meaningful human outcomes and overall enterprise strategy. The key is shifting from AI-enhanced to AI-first thinking: creating products where AI is not just an add-on, but the engine of value.
The AI-first paradigm
The distinction between AI-enhanced and AI-first products is a first step:
- AI-enhanced products improve existing solutions by layering in AI capabilities, such as recommendations, intelligent search, and productivity boosters. These enhancements can elevate the user experience, but the product’s core value remains intact without the AI.
- AI-first products are built from the ground up with AI intrinsic to their functionality, user experience, and value proposition. Simply put, the product cannot exist without it. Take ChatGPT: remove the language model, and there is no product.
AI-enhanced products are less risky to build and offer near-term incremental gains; however, they remain in competition within existing markets and are easily replicated. In contrast, AI-first products unlock new forms of value by solving problems in ways that were previously impossible. They redefine categories that can differentiate your business and deliver hyper-personalized experiences that can’t be replicated with bolt-on features. And with AI at the core, features continuously refine and evolve with every interaction, generating data that makes the product smarter.
From tech-first to human-first
Cutting-edge technology alone does not guarantee trust or adoption. With 78% of organizations adopting AI in 2024, an increase from 55% the previous year (Stanford), the gap between workforce acceptance and resistance becomes a matter of economic survival.
A common and potentially costly error product teams make is prioritizing technology over the human element. This is especially true with AI, as executives are eager to leverage it to gain a competitive edge.
Think of AI as a powerful engine. An engine alone does not make a great vehicle. While the engine is essential for a car to exist, a compelling product emerges when you design the right type of vehicle for a specific human need—whether it’s a truck built for a contractor hauling heavy equipment or a minivan designed for a soccer mom safely transporting children. The engine is a commodity; the winning product is the experience.
The focus must shift from asking “What AI can do?” to asking “What do people need?”
Foundational principles like human-centered design, empathy, and critical thinking remain essential for creating great products. While AI can process large data sets, detect complex patterns, and perform high-speed computations, it lacks the unique human traits of understanding pain points, questioning assumptions, exercising moral judgment, and interpreting ambiguity. Technology can scale capabilities more than ever before, but human judgment ensures these capabilities meet human needs effectively and ethically.
As Dr. Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, reminds us:
“This technology is made by people and it’s going to be used for people. Fundamentally, how we create this technology, how we use this technology, how we continue to innovate, but also put the right guardrails, is up to us humans doing it for humans.” (Source)
Embracing Human-Centered AI (HCAI) innovation
This mindset shift from a tech-focused to a human-centered approach is at the heart of Human-Centered AI (HCAI). It is a framework that prioritizes human experiences from the start, ensuring AI is guided by human needs, values, and experiences throughout its design and development.
The goal of HCAI is to augment human capabilities. It ensures that AI serves as a collaborative tool that empowers users, enhances their skills, and aligns with their goals.
The following guiding principles help put HCAI into practice:
- Start with humans, not the tech
Focus ideation with a deep understanding of human needs, behaviors, and pain points to uncover opportunities where technology can solve meaningful problems. Start with empathy and let technology follow. - Maintain human control and agency
Maintain a balance between AI automation and human control, particularly in high-stakes or ambiguous decision-making situations. Keeping humans in the loop guarantees safety and accuracy, ensuring that AI enhances human intelligence and capabilities rather than replaces them. - Provide transparency and explainability
Avoid the “black box” effect by making AI decisions visible, clear, and easy to understand. Users should be able to understand which decisions are being made by AI, and how those decisions are made. This will foster trust and enhance usability. - Build a human-informed data flywheel
Design a data flywheel that gathers relevant data from real-world user interactions, feedback, and outcomes, then curates and cycles it back into the system to enhance AI models and product features. Building this feedback loop will boost the product’s accuracy, personalization, and defensibility over time, leading to even more data. - Design for ethics, fairness, and inclusion
Actively identify and mitigate biases in data and algorithms to make sure AI systems are fair, beneficial for everyone, respect human values, and promote long-term well-being. Embed trustworthy AI governance to ensure responsible data handling, ethical model operation, and compliance with evolving standards. - Embrace rapid experimentation and adaptation
Root POCs and pilots in real human needs and meaningful business problems. Rapidly prototype, test, learn, and adapt with ongoing user feedback and real-world data. Set up incremental stage gates to evaluate ROI with stakeholders and decide which projects to continue, pivot, or sunset.
AI is the engine, but humans still need to steer
The path to differentiation in the AI era lies in crafting meaningful products that harness AI’s full potential to create new experiences and capabilities that redefine competitive advantage.
Just as an engine needs a driver, even the most powerful AI needs purposeful direction. HCAI acts as the steering mechanism that ensures AI capabilities align with genuine human needs, strategic objectives, and ethical principles. The organizations that outpace others will be those that combine technological strength with empathetic insight and ethical safeguards, building with AI at the core but always guided by a human-centered approach.
AI is the engine; HCAI is the compass. Build what only AI can make possible; let humans decide why it matters.
References:
- Riding the wave: from human-centered design to human-centered AI, by Estefanía Montaña Buitrago
- Building AI-First Products/ A Complete Playbook for Product Teams, by Mahesh Lalwani
- What is Human-Centered AI (HCAI)?, by Interaction Design Foundation
- How to Design for AI-first Products, by UX Design Institute
- Human-Centered AI: What is Human-Centric Artificial Intelligence?, by Alexandra Bardon
