Beyond ChatGPT: How GenAI and LLMs are reshaping business value creation in 2025 and beyond
In 2023, I wrote about a transformative shift from screen-centric to conversational interfaces. Looking back, that prediction captured only part of what's now unfolding — a far more profound transformation is reshaping how we think about technology, business, and human interaction. While much of the industry has focused on chatbots and conversational AI, what's emerging is something much more significant: a fundamental reimagining of how businesses create and deliver value in an AI-first world.
The data tells a compelling story. In early 2024, IBM reported that 42% of enterprise-level companies deployed AI in their businesses, with an additional 40% of experimenting with the possibility of deploying AI. We're seeing traditional web forms and static FAQs rapidly give way to dynamic AI interactions — 73% of business already use or plan to use AI-powered chatbots, Forbes reports. And 82% of customers actually prefer chatbots to human representatives. But these numbers only hint at the deeper transformation underway.
The shift from chatbots to autonomous AI agents
At Demand.io, we're seeing this evolution firsthand through our work developing ShopGraph and our AI-powered shopping platforms. What's emerging goes far beyond simple chat interfaces or recommendation engines. We're witnessing the rise of truly autonomous AI agents that don't just respond to queries but proactively engage with both customers and business processes. These aren't the simple chatbots we've grown accustomed to — they're sophisticated systems that understand context, maintain long-term memory, and seamlessly integrate multiple modes of interaction from text and voice to images and video.
For businesses, the implications are profound and the stakes couldn't be higher. This isn't merely about adding AI features to existing products or automating customer service. It's about fundamentally rethinking how businesses operate in a world where AI increasingly mediates the relationship between companies and their customers. The companies that grasp this shift early and adapt accordingly will find themselves with significant advantages as we move through 2024 and into 2025.
I see several key factors accelerating this transformation:
The key question for business leaders is no longer whether to embrace AI-first operations, but how to do so in a way that creates sustainable competitive advantage. This requires not just technological adaptation but a fundamental rethinking of business models, customer relationships, and organizational structures.
As a founder and early adopter of AI, I'll explore both the immediate practical steps businesses must take and the longer-term strategic implications of this shift. Drawing from our experience at Demand.io and observations of companies at the forefront of this evolution, I'll share actionable insights that can help organizations navigate this transformation successfully. The future isn't just arriving on its own — it's being actively shaped by the strategic decisions companies make today.
The new customer relationship paradigm: AI-mediated engagement
What's most fascinating about this shift isn't just the technology — it's how it's fundamentally changing the way businesses and customers interact. At Demand.io, we're seeing firsthand how AI is creating entirely new paradigms for customer engagement that go far beyond what traditional interfaces could achieve.
Beyond chatbots to multimodal engagement
The most sophisticated AI systems we're working with now can seamlessly integrate voice, image, video, and text into natural conversations. When a customer shares a photo of a product they're looking for, our ShopGraph AI, which is currently in development, can analyze it while maintaining a natural conversation about their preferences and budget. This multimodal understanding creates a much more intuitive and human-like interaction than traditional search or navigation ever could.
What's particularly powerful is how these systems maintain context across different interaction modes. Rather than treating each customer touchpoint as an isolated event, AI agents can now build a comprehensive understanding of customer intent across multiple interactions and channels. This enables the kind of nuanced, personalized assistance that previously required human experts.
The privacy paradox
What's fascinating about this transformation is how it's reshaping privacy attitudes. Our experience at Demand.io reveals an interesting paradox. Pew Research reports 70% of people who have heard about AI "have little to no trust in companies to make responsible decisions about how they use it in their products". But user behavior tells a different story. When companies are transparent about AI usage and provide clear value, consumers are increasingly willing to engage.
This is particularly evident among younger demographics, where about 60% of those aged 30 to 49 express willingness to share information for improved AI-driven experiences from brands, only 20% of people 50 and up feel the same way, according to a survey by Jack Morton. They're not necessarily less concerned about privacy — they're just more pragmatic about the value exchange. This insight has profound implications for how we design AI interactions.
The rise of proactive AI assistance
Perhaps the most significant shift I'm seeing is the move from reactive to proactive engagement. Modern AI systems don't wait for customers to ask questions — they anticipate needs and potential issues before they arise. This is transforming how businesses approach customer service and support.
For instance, at Demand.io, we're developing systems that can detect when a customer might be struggling with a purchase decision and proactively offer relevant information or assistance. By analyzing patterns in browsing behavior and historical data, AI can identify the precise moment when intervention would be most helpful, creating a more efficient and satisfying customer experience.
Impact on traditional customer journeys
The implications for traditional customer journeys are profound. The linear paths we've all grown accustomed to — awareness, consideration, purchase, and loyalty — are being replaced by more fluid, AI-mediated interactions. Customers no longer need to navigate through predefined funnels or click through multiple pages to find what they need.
Building trust in the age of AI
One crucial lesson we've learned is that trust in AI-mediated interactions comes not from making AI seem more human, but from being transparent about its capabilities and limitations. When customers understand what AI can and can't do, they're more likely to engage meaningfully with these systems.
The most successful implementations we've seen maintain a careful balance between AI automation and human expertise. For complex or sensitive issues, the best AI systems know when to smoothly transition customers to human agents, ensuring that technology enhances rather than replaces the human element of customer service.
Looking ahead, I believe we're just beginning to scratch the surface of what's possible with AI-mediated customer interactions. The businesses that will thrive in this new paradigm are those that view AI not just as a tool for automation, but as a fundamental reimagining of how they connect with and serve their customers.
Marketing transformation in the age of generative AI
The shift from screen-centric to AI-mediated interactions is fundamentally reshaping how companies acquire, engage, and retain customers. Marketing leaders are discovering that strategies optimized for websites, apps, and social media feeds must evolve for a world where AI increasingly mediates customer relationships.
Beyond the marketing funnel
Traditional marketing funnels are focused on optimizing screen-based touchpoints: landing pages, email flows, and ad creative. Consider how CarMax, the largest used-car retailer in America, approached vehicle listings just two years ago. Their marketing team spent countless hours crafting compelling descriptions for thousands of vehicles, optimizing each listing for search engines and conversion rates. Today, CarMax uses large language models to analyze and synthesize thousands of customer reviews into concise, engaging summaries — reducing content creation time from a projected 11 years if done manually to just a few months using an LLM while maintaining the human touch that resonates with car buyers.
This transformation extends far beyond content creation. The goal isn't just to appear in search results — it's to ensure your brand is properly understood and accurately represented by the AI systems that increasingly mediate customer discovery.
The new customer journey
Perhaps no industry better illustrates the evolving customer journey than travel. Expedia and Kayak have shifted from purely screen-based search to conversational discovery, implementing chat-based travel assistants that help customers find and book travel through natural conversation. These aren't simple chatbots — they're sophisticated systems that maintain context across multiple interactions, remember preferences, and seamlessly integrate maps and visuals into the conversation.
This shift from screens to conversation is reshaping every aspect of customer engagement. When customers can simply ask for what they want — and have AI systems understand not just their words but their intent — the entire concept of conversion optimization changes. It's no longer about optimizing click paths and button colors; it's about ensuring your brand can engage in meaningful dialogue.
Trust in the age of AI
As AI becomes more prevalent in customer interactions, building and maintaining trust takes on new dimensions. Bank of America's experience with their AI assistant Erica offers valuable lessons. Rather than trying to make Erica handle everything, they carefully designed the system to complement human bankers. When customers ask complex questions about mortgages or investment strategies, Erica smoothly transitions them to qualified professionals. This thoughtful integration of AI and human expertise has led to both increased digital engagement and reduced call center volume.
At Demand.io, we've made a fundamental choice that shapes our approach to privacy and AI: we don't sell customer data to third parties. While the third-party data market remains lucrative, we believe consumers will become increasingly sensitive to how their data is shared and monetized as AI capabilities grow more sophisticated. Instead, we focus on affiliate partnerships where we can create direct value for users while helping them maintain control over their data. This strategic choice has not only built trust but also created a more sustainable business model aligned with evolving consumer expectations.
The lesson is clear: in an AI-mediated world, trust comes not from making AI seem human, but from being transparent about its capabilities and limitations. Of experts surveyed, 84% are in favor of mandatory AI disclosures as a way to facilitate transparency and trust with users.
Measuring success differently
The metrics that once defined marketing success are being reimagined for an AI-first world. While traditional metrics like conversion rates and customer acquisition costs remain relevant, they're being supplemented by new indicators that better reflect success in an AI-mediated landscape:
More importantly, the economics of customer acquisition are shifting. Some companies report as much as 40-60% reductions in costs with a 50% increase in leads when harnessing AI-driven sales, while testing cycles have accelerated from weeks to days, enabling faster optimization and learning.
Marketing in the age of AI
The implications for marketing leaders are clear: success in this new landscape requires fundamentally rethinking how we approach growth and customer engagement. It's no longer enough to optimize for screens and clicks — businesses must ensure they're properly understood and accurately represented across an ecosystem of intelligent agents that increasingly mediate customer relationships.
The winners in this new era will be those who embrace this shift, building marketing strategies that work with, rather than against, the AI systems that are becoming the primary interface between businesses and customers.
AI-first business models: New paths to revenue and value
The shift from screen-centric to AI-mediated interactions isn't just changing how businesses reach customers — it's fundamentally transforming how they create and capture value. Let's look at Salesforce's evolution: what began as a simple customer database has transformed into an AI-powered platform where Einstein generates personalized content across sales, service, and marketing. This isn't just automation — it's an entirely new way of delivering value to customers.
Salesforce Ventures doubled down on AI when it announced its $250 million Generative AI Fund, which invests in promising startups at the forefront of generative AI.
Evolution of traditional models
The most dramatic changes are happening within established business models. Take Adobe's transformation of its Creative Cloud suite. After integrating generative AI features like Firefly, Adobe introduced new subscription tiers and volume-based pricing for AI-driven asset generation. Customers who once paid for software access now also pay for AI-powered creativity and automation. This shift toward value-based pricing, enabled by AI's measurable impact, allows companies to capture more of the value they create.
The banking industry provides another compelling example. The banking industry provides another compelling example. “Things like Explainable AI, Responsible AI and Ethical AI which defend against events like unplanned bias are no longer being seen as optional but required for companies that leverage ML/AI and specifically where they host customer’s personal data,” said Brian Maher, Head of Product, Firmwide AI/ML Platforms at JPMorgan Chase. “In banking, we have a dedicated function called Model Risk Governance to assess the risk of each use of ML/AI to ensure the application of this technology does not introduce risks to our customers or to the firm,” he added.
Once seen as discretionary, advanced adoption of AI in diverse functions is now seen as mandatory by leading banks.
The platform play
Perhaps the most significant development is the emergence of an AI-first platform economy. Microsoft's partnership with OpenAI shows how this can work at scale. By integrating OpenAI's technology into Azure services, Microsoft created a new, high-margin revenue stream within its cloud offerings while helping other businesses access advanced AI capabilities. The model is particularly powerful because it scales efficiently — once built, these AI services can serve millions of users with minimal marginal cost.
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Data as competitive advantage
Companies with unique data assets are taking divergent paths in how they create value. Some, like Bloomberg with BloombergGPT, are building specialized AI models trained on their proprietary data to offer enhanced services directly to their customers. Others continue down the traditional path of selling customer data to third parties — a model I believe will become increasingly unsustainable as consumers grow more aware of how their data is being used by AI systems.
At Demand.io, we've chosen a third path: using our data assets to create direct value for users through better recommendations and shopping experiences, monetizing through affiliate partnerships rather than data sales. This approach aligns incentives between our business, our users, and our retail partners. The key isn't just having data — it's knowing how to use it responsibly to create sustainable value.
A new era of personalization
Retailers are finding novel ways to monetize AI-driven personalization. Stitch Fix uses AI to offer personalized styling at scale, while Sephora's AI-powered Virtual Artist tool provides personalized beauty advice and product recommendations. These aren't just better customer experiences — they're entirely new business models that would be impossible without AI mediation.
Building sustainable advantage
The key to sustainable success in this new landscape isn't just implementing AI — it's building business models that create compounding advantages. The most successful companies are creating virtuous cycles where their AI services generate both revenue and data, which in turn improves their AI capabilities, attracting more customers and generating more revenue and data.
From AI capability to business value
This shift represents perhaps the most significant change in how businesses create value since the advent of the internet. Just as the web transformed how companies reach and serve customers, AI is transforming how they create and capture value. The winners in this new era will be those who build their business models around three key principles:
The opportunity isn't just to do things better with AI — it's to do entirely new things that weren't possible before. The companies that understand this distinction are the ones reshaping their industries today.
Building AI-native organizations: infrastructure, culture, and capabilities
The transition from screen-centric to AI-mediated interactions requires more than just technical know-how — it demands a fundamental rethinking of how organizations operate. Success requires addressing both technical and organizational hurdles while maintaining a clear focus on business value.
Starting with strategy
The most successful AI implementations begin with clear strategic alignment. Rather than pursuing AI for its own sake, organizations need to identify specific business problems where AI can create measurable value. Consider Walmart's approach: rather than chasing trendy AI applications, they focused on a specific challenge — optimizing their massive supply chain. Their AI-driven inventory forecasting started with clear business objectives and expanded only after proving tangible value.
Three elements consistently appear in successful AI strategies:
The data foundation
Perhaps no aspect of AI implementation is more crucial than data infrastructure. Without clean, accessible data, even the most sophisticated AI models will fail to deliver value. This means investing in modern data architecture, governance frameworks, and data quality processes before rushing to implement AI applications.
Mayo Clinic's methodical approach to AI diagnostics illustrates this principle well. Before deploying any AI models, they established robust data governance frameworks and clear protocols for data quality. This foundation enabled them to scale their AI initiatives while maintaining compliance and trust.
Organizational transformation
The shift to AI-first operations requires significant organizational change. Companies need new skills, new processes, and often new organizational structures. Success requires:
Technical architecture
Building for an AI-first future requires flexible, scalable technical infrastructure. Most organizations find success with a hybrid approach that combines cloud-based AI services with on-premises systems. This provides the flexibility to scale AI workloads while maintaining control over sensitive data and critical systems.
Key infrastructure considerations include:
Managing risk and compliance
As AI becomes central to business operations, risk management becomes crucial. Financial institutions like JPMorgan Chase show how to handle this well — they established centralized AI governance boards and comprehensive monitoring systems, enabling rapid innovation while maintaining compliance and trust.
Measuring success
Success in AI implementation isn't just about technical metrics — it's about business impact. Organizations need to track both technical and business KPIs:
People-led AI
The journey to AI-first operations is challenging but achievable. By focusing on clear business outcomes, building robust technical foundations, and driving organizational change, companies can successfully navigate this transformation and capture the value of AI-mediated interactions.
The technology may be about artificial intelligence, but success is ultimately about human intelligence in how we implement and apply it. The organizations that understand this — balancing technical excellence with organizational change management — are the ones seeing real results from their AI initiatives.
2025 and beyond: preparing for autonomous AI futures
The transformation from screen-centric to AI-mediated interactions is just beginning. By 2025, we'll see capabilities that make today's AI systems seem primitive by comparison. But this future isn't just arriving on its own — it's being actively shaped by companies making strategic moves today.
The next wave of AI capabilities
We're entering an era of truly autonomous and multimodal AI. Rather than simply responding to queries or performing isolated tasks, AI systems will increasingly act as independent agents capable of handling complex workflows end-to-end. Consider JPMorgan's early moves in this direction: their AI agents already analyze financial regulations, deal history, and market conditions to propose optimal structures. By 2025, such capabilities will extend far beyond finance, transforming how work gets done across industries.
These systems will seamlessly integrate text, voice, images, and video, understanding context and intent in ways that feel remarkably human. But more importantly, they'll shift from reactive to proactive — anticipating needs and taking initiative rather than just responding to requests.
The evolution of customer relationships
The way businesses interact with customers will undergo a profound transformation. Today's chatbots and recommendation engines will evolve into sophisticated AI advisors that maintain ongoing relationships with customers, understanding their preferences, history, and context across all touch points. These systems won't just respond to queries — they'll proactively guide customers through their journey, anticipating needs, and removing friction points before they arise.
Tesla's connected vehicle network provides a glimpse of future fleet management. While not yet fully autonomous, their system of real-time monitoring, data collection, and basic automation across thousands of vehicles demonstrates how intelligent transportation networks might eventually operate at scale. This proactive, system-wide optimization will become the norm across industries.
Strategic imperatives for business
To prepare for this future, forward-thinking companies are focusing on four key areas:
This last point is particularly crucial. As AI becomes more sophisticated, the traditional practice of selling customer data to third parties will face increasing scrutiny. Companies need to design business models that create value directly from their AI capabilities rather than treating customer data as a commodity to be sold. This might mean lower revenues in the short term, but it creates more sustainable, trusted relationships with customers in the long run.
Risks and challenges
The path to this AI-mediated future isn't without obstacles. Privacy concerns, regulatory requirements, and ethical considerations will all need to be carefully navigated. Companies will need to balance the power of AI with transparency and trust, ensuring that their use of AI aligns with customer values and societal expectations. Thoughtful implementation will become increasingly crucial as AI systems take on more important roles.
The human element
Perhaps most importantly, the future of AI isn't about replacing human capabilities — it's about augmenting them in powerful new ways. The winners in this new landscape will be those who find the right balance between human creativity and AI capability, creating environments where each focuses on what they do best.
The companies that will thrive in this new landscape are those that view AI not just as a technology to be implemented, but as a fundamental shift in how business operates. They're building the capabilities, culture, and strategic vision needed to succeed in a world where AI mediates most interactions between businesses and customers.
About Demand.io
Demand.io, founded by Michael Quoc, is on a mission to become the world's leading source of e-commerce knowledge. At the company's core is ShopGraph, a proprietary knowledge graph that powers AI-driven consumer products like SimplyCodes and Product.ai, offering unprecedented insights and savings across over 400,000 online stores.
As a profitable, self-funded organization, Demand.io combines cutting-edge technology with deep e-commerce expertise to create value for its entire ecosystem of users, partners, and team members. The company's innovative approach leverages artificial intelligence and community engagement to transform how people discover, compare, and purchase products online.
Senior Executive - CEO - CRO - Enterprise SaaS - Building & leading teams to exceed goals and accomplish great things
1 个月It occurs to me that Brand = Trust when it comes to AI in the customer journey. Thoughts - agree or disagree?
Bitcoin-Powered Real Estate | RWA Fractional Ownership for the Future
1 个月Absolutely agree! The shift to AI-mediated relationships can redefine customer engagement. What do you see as the key challenges businesses will face in this transition? On a different note, I’d be happy to connect—please feel free to send me a request!
Founder | Aurosat Entertainment | Expert in Strategic Media Placements for Entrepreneurs and Businesses??
1 个月I agree!
Visionary Financial Leader | Strategic Financial Advisor | Innovator in Cost Reduction & Operational Efficiency | Ex Office of the President, Office of Management and Budget
1 个月This is a really thought-provoking post, Michael. I especially appreciate your insights on the shift from 'chatbots to autonomous agents', it's an interesting perspective.
That's a thought-provoking perspective on the evolving role of AI in business. It's interesting to consider how companies can leverage these advanced technologies to enhance customer relationships. What do you think will be the biggest challenge in making this shift?