AI in 2026.
From Novelty to Operating Model.
AI is a platform. The hard part starts now. Unlocking real value will take time. The winners will be those who understand that AI is a human technology.
We’ve witnessed the fastest technology adoption in history since the release of ChatGPT. In fact, OpenAI reached 100 million monthly users in just two months. At first, the models were limited, and hallucinations were real. In 2025, AI took a significant leap forward. Context windows, which are the working memory of large language models, expanded. Agents gained real momentum, models became significantly more capable, and text-to-action tools emerged, turning AI into a real cognitive force for organizations.
2025 was about momentum. 2026 is about integration. It is more about the plumbing, the workflows, the governance, and the people. The winning organizations this year won't be the ones chasing the newest model, innovation, or technology. They'll be the ones who mastered making AI run and understanding that AI is a human technology.
Here's what we're expecting.
The foundation is shifting.
Data is your moat. Permissioned, proprietary data matters more than ever, serving as your ‘moat’ or unique competitive advantage. The data that captures how your organization actually makes decisions, serves customers, and operates is what creates a lasting advantage. It reflects the unique fingerprints of an organization, including priorities, constraints, and outcomes. That’s why we refer to data as money. It is true in the sense that proprietary data can't be scraped, bought, or synthetically generated at scale. It's irreproducible. From that perspective, smart brands put their data to work on both sides, powering how its people operate and how customers experience the brand.
Models are no longer the bottleneck. A year and a half ago, we were conditioned to anxiously await each new AI model release. Now, we are accustomed to rapid leapfrogging of features, with models that reason and respond to prompts significantly better and with bigger context windows. For example, Gemini's 1.5 Pro model in 2025 handled 1 million tokens, approximately 1,500 pages of text, compared to far smaller tokens in earlier generations.
While models have gotten quite smart, we don’t expect the next big evolution, AGI (artificial general intelligence) or superintelligence, in 2026. Stanford HAI's James Landay made this his biggest prediction, and I agree. What we will see is continued progress in self-learning and self-improving models. We'll see smaller, specialized models gaining ground because they're easier to apply at the organizational level than generalized knowledge. Additionally, sovereign models, built or hosted within national borders to keep data local, are inevitable given today’s geopolitical climate.
2026 is all about execution. Systems thinking, orchestration, and how workflows are designed will matter more than the models themselves.
AI is becoming infrastructure. Some 79% of companies use generative AI in at least one function and will increase AI investment by 92% over the next few years, according to McKinsey's early estimates. A question, like “Do you use Generative AI?” will become absurd by the end of 2026, or by 2027 at the latest. Instead, the discussion will focus on how AI is used and deployed, integrated into workflows, and how it creates a competitive advantage for the organization.
The catalyst behind this shift is the reduced cost of AI inference, which is the ongoing operational cost of running trained AI models. As AI becomes more affordable, the inefficiency will come from deploying it without a system. For example, JPMorgan Chase’s Omni AI is an early example of how companies are building their AI factories for both employees and customers.
Bottom line, businesses will start to value AI as an operational platform embedded in processes and people's work. The ‘AI everywhere’ shift will require engineering, architectural, and governance breakthroughs. In simple terms, plumbing will evolve, and applications will be built natively around AI rather than being bolted on.
The organization gets redesigned.
2026 is the dawn of the hybrid organization. The hybrid organization is the end of the 100% human workforce. At a recent travel and tourism industry meeting, only one-third of C-level executives said they plan to hire next year. Whether that's driven by budget constraints, rising cost of doing business, or AI replacing roles, hiring is slowing down.
We are entering the work redesign era. Machines can think, learn, and make autonomous decisions. How we define work and the workforce is evolving. We recently saw how Ark Invest, an investment management firm, has already started using the term "revenue per robot" alongside “revenue per employee” to evaluate the broader economic potential of automation. That shift in metrics tells you everything about where we're heading.
AI is a cognitive revolution. One useful comparison is the redesign of manufacturing in the early 20th century. Factories had to rework their processes, and machines were reconfigured within the plant. Basically, plants were redesigned around new capabilities: assembly lines, standardized processes, specialized roles. AI is forcing a similar rethinking. In fact, Andrew Ng saw this back in 2017. The idea that AI will reshape and restructure organizations is becoming real, whether organizations are building internal or external capabilities.
Instead of information flowing through layers of people, AI becomes a layer of cognitive processing within the organization. That changes who does what, how decisions are made, how quickly work moves, and how jobs are reimagined.
One of the most exciting takeaways from such a shift is that small teams can have enterprise-level capacity.
The new operating playbook.
AI is judged by outcomes. The coolness factor of AI has come to a close. In 2024 and 2025, AI initiatives were often measured by experimentation. In 2026, AI has evolved into an operational capability. Did it improve outcomes? "Cool" no longer counts. Measurable outcomes do.
Thomas Davenport and Randy Bean noted this in the MIT Sloan Management Review; most GenAI use has been "incremental and mostly unmeasurable" and has aided individual productivity. What are employees doing with the time they save? Nobody seems to know. The organizations that succeed in 2026 will shift from individual-level AI tools to enterprise-level strategic applications with fewer yet higher-impact results. Stanford's Erik Brynjolfsson takes it further, predicting the emergence of "AI economic dashboards" that track productivity, displacement, and new role creation at the task level. The debate shifts from whether AI matters to unlocking real value.
Winners build trusted agents selectively. The organizations that succeed will deploy agents selectively because not everything needs to be agentified. Additionally, security, safety, and reliability remain handicaps in this area. While adoption will take time due to current challenges, we still expect progress in the near term. Organizations will select high-impact workflows, define exactly where agents sit in the organization, clarify human oversight and escalation, and build trust gradually.
The winners will build fewer, trusted agents with outcomes aligned with organizational goals. And they will have clarity and understanding of where those agents sit in the organizational structure and workflow.
The data backs this up. According to Deloitte, only 11% of organizations have AI agents in full production despite 38% actively piloting them. The friction between pilots and production is where the opportunity lives.
Governance becomes non-negotiable. AI systems have access to tools and resources. They are making recommendations, handling transactions, and acting on behalf of people and organizations. As agents gain autonomy, risk increases. Such a shift moves safety and governance to an executive priority.
Currently, agentic systems introduce a multitude of risks, including exposure of private data and financial, compliance, and reputational risks. Solution-oriented frameworks are already in development to address risk, and we expect this area of expertise to continue to evolve. For example, forward-thinking organizations will need new frameworks, such as enterprise AI frameworks that identify key dimensions of agentic governance, including agent lifecycle management, observability, policy enforcement, human-agent collaboration models, and performance monitoring. We see leading with safety as a core competency when applying frameworks.
AI-first organizations will evolve from viewing AI as a tool to recognizing AI as part of the workforce. That perspective means defined responsibilities, auditability, human oversight models, and accountability structures. As AI touches the real world, more is at stake.
Marketing becomes intent-first. Clicks are losing meaning in a zero-click world. Context is gaining power. AI systems increasingly interpret user intent directly, often without traditional web journeys. In many cases, AI is evolving into the interface and user experience.
In that world, AI’s influence is large. It discovers, curates answers, recommends products, summarizes brands, and shapes the user experience and decision-making. Marketing shifts from driving traffic to shaping how AI understands brands and products through the eyes of robots.
This fragmentation of user journeys will also drive renewed urgency around attribution. Consumers are interacting across AI platforms, marketplaces, and conversational interfaces. When old measurement systems break down, marketers will move from tracking clicks to proving actual business value.
Content volume turns into a tsunami. My Instagram feed is already full of AI-generated content since the release of Nano Banana, a multimodal AI image editor and generator from Google, in the summer of 2025. Dogs talking, cats singing, and striking visuals with variations. At first, they are cool, and some are even funny. The volume of synthetic content is becoming enormous.
Basically, the economics of content creation have fundamentally changed. And that’s the paradox.
Content production is approaching minimal cost, even near zero. When everything can be generated instantaneously, what is the edge? Human perspectives, lived experiences, emotions, and real voices become distinct. For brands, storytelling will matter even more as most content becomes a commodity. Such dynamics will accelerate the creator economy. Individuals and brands who deliver authenticity, in other words, those who keep it real, will command attention, differentiation, and trust.
Agentic commerce unlocks monetization. A major shift that took off in 2025 was AI-assisted transactions, as platforms are integrating native payment systems to capture agentic commerce opportunities. Perplexity partnered with PayPal. OpenAI supported AI-agent-assistant payments, and Google also enabled payment protocols in the fall of 2025.
As discovery is shifting from traditional search to agents and AI’s influence, we expect significant action here. Because agentic commerce unlocks entirely new monetization models. With a rapid growth trajectory, agentic shoppers could account for 10% to 20% of U.S. e-commerce spending by 2030. Some will call this estimate conservative. Either way, the opportunity, especially in retail and travel, is significant.
The physical frontier.
Physical AI accelerates. AI is moving to the physical space. Robotics, autonomous systems, and delivery devices are becoming increasingly visible. Progress is uneven with real-world incidents. I saw a delivery robot hit a mailbox the other day.
Chinese firms, in particular, are pushing aggressively into the robotics revolution, and their presence is hard to miss. When it comes to capabilities, most humanoid robots showcase impressive but a narrow scope and limited dexterity. Don't expect a humanoid robot to clean your rooms on your next vacation yet. But expect to see more quiet automation, such as autonomous cleaning robots in hotels, warehouse sorting, and delivery.
2026 won't be the year of widespread humanoid deployment. It will be the year of physical AI integration into specific use cases where it's economically viable and operationally stable. Perhaps, slow but important to watch.
What keeps me up at night.
I'd be dishonest if I painted this as all rosy. A few things I keep coming back to:
When does the AI bubble show up? MIT Sloan's Davenport and Bean draw direct parallels to the dot-com era: sky-high valuations, a growth-over-profit mentality, and an expensive infrastructure buildout. We even heard someone refer to such companies as 'zombie companies'. Stanford's Angèle Christin captures it well: “this isn't necessarily the bubble popping, but the bubble might not be getting much bigger.” A correction, especially at the application layer, is probably healthy. But corrections affect real people and real companies. And they have consequences.
What happens to people displaced by AI? Stanford's research using ADP data has already shown that early-career workers in AI-exposed occupations experience weaker employment and earnings outcomes. Unemployment of younger age groups continues and has real implications. How the K-shaped economy evolves and where AI amplifies advantages for some and accelerates displacement for others matters tremendously.
Who manages the agentic web? As agents proliferate and begin interacting with each other at scale, agents may outnumber people on parts of the web. We face real questions about such distributed ecosystems, collective AI behavior, and systemic risk, all with ramifications for growth and prosperity.
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The defining theme of 2026 is this: AI is no longer a novelty. It has become a platform. This year is about implementation, infrastructure, and the operating model.
Most notably, we see AI as a human technology.
The organizations that win will focus on unlocking real value. Not because the technology is cool or sort of new, but because we're learning how to make it work. And it takes trust, commitment, consistency, and time.
