How Agentic-AI Will Capture Latent or Unarticulated Needs
The Next Frontier of AI: Capturing Unarticulated User Wants
We’ve reached a pivotal moment in the evolution of artificial intelligence — a breakthrough concept that redefines the very nature of Agentic AI. Traditionally, AI has been designed as a reactive tool, responding to user inputs and fulfilling explicit requests. But what if AI could go beyond that? What if it could actively discover and generate our needs and wants, even before we become aware of them ourselves?
Enter Quoria AI Agent©, a transformative vision that entirely changes Agentic AI's potential. Quoria AI Agent© doesn’t just serve as a service or assistant; it becomes a proactive partner in our lives — a catalyst that shapes everything, everywhere, all at once. By embracing this new paradigm, we unlock the potential for AI to revolutionize commerce, redefine user experiences, and enrich our daily lives in unprecedented ways.
“Make something people want.” This standard startup advice resonates because it distills a founder’s core job into a simple phrase. But much like telling an investor to “beat the market,” it prompts the question: “How?”
This article serves as the priority date for the copyright claim for the phrases “AI Shapes Everything Everywhere All At Once©,” and “Quoria AI Agent©”, dated October 19, 2024, Sydney, Australia.
Clayton Christensen’s 2003 book The Innovator’s Solution launched his ‘Jobs to Be Done’ theory as a theoretical concept and a practical roadmap for product development. He proposed that customers ‘hire’ products or services to perform specific jobs in their lives. This means that people don’t just buy products; they ‘hire’ them to do particular tasks. By adopting this approach, we can identify a clear set of problems that need to be solved and then connect these problems to the needs of our customers, guiding us in what products to build.
This is a scientific approach to product development but also profoundly human. The mysterious process of innovation becomes an empirical activity: discover needs and create ways to fulfill them. It means engaging with customers—on the street, through surveys, online, in bars—wherever and however you can find them to understand their needs and connect with them personally.
But as any experienced founder knows, this process is fractal. The deeper you delve into asking questions about a person’s day, the more detail you can observe. Actual needs — the burning, unfulfilled ones — often get lost in the infinite contours of individual lives.
So, what actually works? I’ve found that dropping objectivity and leading with a particular idea or bias changes everything. When you present your view of the world or a perspective on what you intend to build, the squishy mess in people’s heads hardens into visibility. It’s like a non-Newtonian fluid subjected to force: a liquid until you press on it with the strength of your perspective.
You’ve probably had an analogous experience in other areas of your life. Consider the question: “What do you want for dinner?” For me, I draw a blank. But suppose you ask me about that new Thai place down the street. In that case, I can and will unfurl an incredibly long string of sentences about why it’s the best in the city — particularly the hor mok.
Why is there a difference in my ability to respond?
Needs Arise in Context — they Are Not Context-Free Facts
When we search for needs or jobs as objective truths, we assume that there’s a discoverable set of hidden desires. We’re adopting the rationalist view that these inherent properties can be enumerated formally.
Christensen’s foresight in recognising the role of context in identifying needs, challenges the notion of objective truths in product development.
He understood that a need or a ‘job’ emerges from specific circumstances and prompts. This prompt, or provocation, is a creative act. It’s the process of generating sequences of words and behaviours that evoke responses from prospective customers. By trying to be purely objective, we may lower the chances of observing what we hope to find.
Take, for example, my writing of this article. It would be impractical to ask what kind of article you wanted me to write beforehand. Presumably, you’re reading it because it appeared in your feed or inbox — not because you had an underlying need for it that you could articulate. The need only arose because I formed a sequence that generated your response. This response seems like a need you’ve had all along, but it didn’t emerge until the context was created.
This underscores Christensen’s concept’s insight, especially in our AI-driven economy. In an era where AI shapes everything everywhere all at once, capturing the latent or unarticulated needs of potential users becomes even more critical. AI systems, particularly those with agentic capabilities, have the potential to create contexts in which new needs arise. They don’t just respond to existing demands; they can lead with innovative ideas that prompt users to recognise subconscious desires.
Learning to Generate the Right Sequences
Now the question becomes: “How do you learn to generate the right sequences?”
If you’re thinking, ‘Figure out the kinds of sequences that generate good responses,’ you’re still seeking essences — a list of words that excite someone. Instead, the process of making something people want involves learning, through experimentation and error, to be the kind of person who can generate needs, wants, and jobs in others.
Christensen’s insights guide us here. He emphasised understanding the circumstances that compel people to “hire” a product or service. It’s about noticing the subtle cues and contexts that influence decisions.
Imagine someone who observes that commuters often appear bored during their daily transit. Instead of merely noting this, they create an app that delivers short, engaging podcasts tailored to the length of the commute. They pay attention to people’s satisfaction from learning something new or being entertained in a limited timeframe. By tuning into these reactions, they can design more content that taps into the desire for meaningful use of idle time.
Or consider a person who notices that people struggle with maintaining healthy habits. They develop a smart water bottle that glows gently when it’s time to sip. They observe how this subtle reminder fits seamlessly into daily life without feeling intrusive. By focusing on these experiences, they can expand into other areas of wellness, creating products that help people meet their health goals in unobtrusive ways.
A good metaphor for becoming this kind of person is to think about how language models learn to respond to a prompt. They try repeatedly to generate sequences, learning implicit patterns that they apply in particular contexts. By learning to pay attention, they build an intuition for how to respond in any given situation. Language models don’t have a dictionary of all possible sequences; appropriate responses emerge from the prompt. Similarly, we are constantly training ourselves, and new responses continually emerge.
The AI Economy and the Emergence of Needs
In our AI-powered economy, Christensen’s foresight is more relevant than ever. AI technologies can interact with users in ways that reveal latent desires and preferences. By designing AI systems that lead with innovative ideas or biases, we can evoke unarticulated needs and provide solutions that users didn’t even know they wanted.
Consider how recommendation algorithms have transformed content discovery. Users didn’t necessarily articulate a need for AI-curated content. However, once presented with personalised recommendations, a new expectation was set — a need that wasn’t recognised until the context was created.
Agentic AI possesses a degree of autonomy and can interact with users in dynamic, context-sensitive ways.
This can lead to the emergence of new needs as the AI creates experiences users hadn’t imagined. By capturing these latent or unarticulated needs, we make something people want and redefine what they consider possible.
Embracing Christensen’s Insights to Innovate
Making something people want is an alternate way of seeing and responding to the world. It’s about leading with perspective and creativity, shaping contexts where new needs can emerge. We can better connect with others by embracing our ideas and biases and acknowledging the wisdom of thinkers like Christensen.
In essence, the key is to become — or develop — the right kind of person (or AI) — someone who doesn’t just observe the world but interacts with it in ways that evoke responses and reveal hidden desires. It’s akin to how we learned to walk: trying, awkwardly, one step at a time, falling and getting back up until our bodies learned the thousands of tiny adjustments necessary to keep us upright without conscious thought.
By applying Christensen’s “Jobs to Be Done” theory with this renewed perspective, especially in the context of AI that “shapes everything everywhere all at once©”, we can better navigate the complexities of innovation. We move beyond simply meeting existing needs to uncovering and creating new ones, capturing potential users’ latent or unarticulated needs.
In an economy increasingly influenced by AI, acknowledging Christensen’s prescient insights isn’t just advantageous — it’s essential. It’s not enough to ask people what they want; we must understand the contexts that generate needs and show them something they didn’t know they needed. By doing so, we can truly make something people want, even before they realise they want it.
A Breakthrough in Agentic AI: From Reactive Tools to Proactive Partners
We’ve reached a pivotal moment in the evolution of artificial intelligence — a breakthrough concept that redefines the very nature of Agentic AI. Traditionally, AI has been designed as a reactive tool, responding to user inputs and fulfilling explicit requests. But what if AI could go beyond that? What if it could actively discover and generate our needs and wants, even before we become aware of them ourselves?
This transformative vision changes the direction of Agentic AI entirely. It positions AI as a service or assistant and a proactive partner in our lives. This catalyst shapes everything, everywhere, all at once. By embracing this new paradigm, we unlock the potential for AI to revolutionise commerce, redefine user experiences, and enrich our daily lives in unprecedented ways.
Having explored how needs arise not as static facts but within the proper contexts and sequences, I’ve contemplated how we might design an AI agent that embodies this understanding. Specifically, how could we create an AI agent that doesn’t just respond to explicit commands but actively discovers and even generates needs and wants? In doing so, we’re looking at a transformative vision that could reshape commerce entirely.
Imagine an AI agent that embraces proactivity over reactivity. Instead of waiting for us to articulate a need — which, as we’ve discussed, we often can’t do — the AI initiates interactions based on a deep contextual understanding of our behaviours, preferences, and circumstances. Much like a close friend who knows when to suggest a good book or a walk in the park, the AI anticipates our needs before we know them.
Quoria AI Agent© doesn’t just observe; it creates contexts that evoke needs. Presenting us with new ideas, experiences, or perspectives helps unearth latent desires we didn’t know we had. It’s similar to how a skilled musician might introduce us to a genre we’ve never heard before, opening up a new world of enjoyment.
Another crucial aspect is leading with innovative ideas and biases. Quoria AI Agent© doesn’t strive for objectivity but shares its recommendations, crafted from a rich understanding of our unique tastes and the broader trends it observes. This approach aligns with how we, as individuals, inspire and influence each other through shared interests and enthusiasm.
Continuous learning and adaptation are essential. The AI agent refines its understanding of us over time, learning from each interaction to better anticipate and generate responses that resonate. This mirrors how we build relationships, gradually understanding each other’s quirks and preferences.
Now, one might wonder about the feasibility of such an AI agent. Given the advancements in machine learning and data processing, we’re at a point where this kind of sophisticated, context-aware AI is within reach. Technologies like natural language processing, predictive analytics, and pattern recognition have evolved significantly, enabling AI to interpret and engage with human nuances more effectively.
The impact of Quoria AI Agent© could be transformative — a key to future commerce.
By actively participating in discovering our needs and wants, AI could enhance our lives in ways we haven’t yet imagined. It could help us find new hobbies, improve our health, deepen our relationships, and even unlock creative potentials we didn’t know we had.
For example, imagine an AI that notices subtle signs of stress in your daily routine and introduces you to mindfulness practices that fit seamlessly into your schedule. Or consider an AI that detects your growing interest in a particular subject through your reading habits and curates a personalised learning pathway for you. In these ways, AI doesn’t just fulfil existing needs — it helps create new avenues for personal growth and satisfaction.
This approach improves individual experiences; it represents a transformative vision that could reshape commerce. Businesses leveraging such AI agents could engage with customers on a profoundly personal level, uncovering unarticulated needs and creating new markets.
By shifting from a reactive model to one that actively generates and fulfils latent desires, companies can forge deeper connections with their customers, foster loyalty, and open up innovative revenue streams.
Importantly, designing such an AI agent requires careful consideration of ethical principles. Trust, privacy, and respect for user autonomy must be at the forefront. The AI should empower users, not manipulate them, ensuring that discovering new needs enhances well-being and aligns with personal values.
By integrating these human-centric principles into AI design, we create agents that genuinely resonate with us. They become more than tools; they become companions in our journey of self-discovery. This approach doesn’t just make something people want—it reveals what people could want, expanding the horizons of possibility.
We’re looking at a potential paradigm shift in how commerce operates. By acknowledging the wisdom of thinkers like Clayton Christensen and applying it to today’s AI capabilities, we can navigate the complexities of innovation more effectively. We move beyond simply meeting existing needs to uncovering and nurturing new ones, capturing the latent or unarticulated desires that enrich our lives.
In a world where AI shapes everything, everywhere, all at once, this kind of empathetic, proactive AI isn’t just a fascinating concept — it’s an essential evolution. It’s not enough to ask people what they want; we must help them discover what they want. By doing so, we unlock a transformative vision that doesn’t just reshape commerce but redefines our relationship with technology and each other.
My epiphany reveals a new AI-driven platform offering an entirely new user experience. This approach could revolutionise industries, foster unprecedented levels of engagement, and ultimately lead to a more intuitive and fulfilling interaction between humans and technology.
The future of commerce may well hinge on our ability to design AI that understands and influences needs in this way. By leading with innovative ideas and embracing the creative act of prompting needs, we can create a responsive and generative marketplace that continuously evolves to meet the ever-changing tapestry of human desires.
In this vision, AI doesn’t just shape everything everywhere all at once — it becomes an integral part of how we explore, understand, and enhance our lives. And that, I believe, is a transformative vision worth pursuing.
This article serves as the priority date for the copyright claim for the phrases “AI Shapes Everything Everywhere All At Once©,” and “Quoria AI Agent©”, dated October 19, 2024, Sydney, Australia.
About the author: Greg Twemlow, Founder of XperientialAI©.
Greg Twemlow: “Empowering future leaders and organizations by designing and delivering AI-integrated experiential learning programs that blend technology, ethics, and philosophy. Through consultancy, mentorship, innovation coaching, and thought leadership, I help CEOs, business leaders, and individuals ethically and efficiently implement AI solutions while fostering a culture of trust, integrity, and wisdom in an AI-driven world.” Contact Greg: greg@xperiential.ai
