Reflections from the AI Trends 2026 LinkedIn LIVE with MathCo leadership
As 2025 draws to a close, the conversation around artificial intelligence has fundamentally changed. What began as widespread experimentation and curiosity has evolved into something far more consequential: enterprises are now asking whether AI can be trusted, scaled, and embedded into the very fabric of decision-making.
In a recent LinkedIn LIVE session titled “AI Trends 2026”, MathCo leaders—Nabeel Ahmed (Head of AI GTM), Shridhar Guntury (CTO), and Srivatsa Kanchibotla (Chief AI Scientist)—came together to reflect on the lessons of 2025 and share their outlook for 2026. Together, they represent the full AI value chain: from research and model innovation, to enterprise integration, to real-world adoption and ROI.
What emerged from the discussion was a clear narrative: 2025 was the year AI proved it was real. 2026 will be the year enterprises decide whether they are truly ready for it.
2025: The Year AI Became Real
At the start of 2025, many organizations were still asking a fundamental question: Is AI hype, or is it here to stay? By the end of the year, that question had been decisively answered.
AI is real. And more importantly, it is already reshaping how organizations operate.
Across industries—from retail and CPG to pharma, manufacturing, and healthcare—enterprises embraced AI with unprecedented enthusiasm. Unlike previous waves of technology adoption, there was little hesitation. Organizations jumped onto the AI “hype train” early, launching pilots, proofs of concept (POCs), and internal tools at scale.
Several positive trends stood out:
- Widespread executive buy-in: AI was no longer confined to innovation labs. Senior leadership actively pushed for AI-driven outcomes.
- Decision augmentation: AI began supporting, and in some cases automating, operational and strategic decisions.
- Rise of autonomous agents: Particularly in manufacturing and operations-heavy functions, agent-based systems gained traction.
- Improved data and tech literacy: Enterprises invested heavily in upskilling, making AI a more accessible tool across teams.
Importantly, this momentum was not limited to “tech-forward” industries. Even sectors traditionally viewed as conservative—such as pharma—emerged as strong adopters of AI.
Why Pharma Surprised Everyone
Pharma’s enthusiasm for AI may appear counterintuitive at first glance. The industry is often associated with long development cycles, regulatory scrutiny, and cautious adoption of new technologies.
Yet these very characteristics may explain its success with AI.
Pharma organizations are accustomed to long-term thinking, rigorous validation, and patience. They know how to assess value over extended timelines. As a result, when generative and agentic AI began demonstrating tangible benefits—across drug discovery, clinical insights, commercial analytics, and operations—pharma leaders recognized its potential and moved decisively.
Rather than chasing novelty, they focused on value. And that mindset proved critical.
Agentic AI: The Breakthrough of 2025
From a research and technology standpoint, 2025 marked a major inflection point: the rise of agentic AI.
Earlier waves of generative AI captured public imagination but struggled to deliver enterprise-grade impact. They were powerful, but often isolated—producing impressive outputs without deep integration into business systems.
Agentic AI changed that.
By design, agentic systems can orchestrate workflows, interact with multiple tools, and operate across existing enterprise assets—data platforms, BI systems, and operational tools. This made them especially attractive to organizations with mature data ecosystems.
In many ways, agentic AI is what transformed AI from a consumer curiosity into a serious B2B technology. Enterprises did not need to start from scratch; agentic AI could plug into what already existed, unlocking value from years of prior investments.
The Challenges Beneath the Surface
Despite the excitement, 2025 was far from perfect. Many organizations learned the hard way that enthusiasm alone does not translate into ROI.
Three recurring challenges stood out.
Bias and Trust Failures
Some organizations rushed AI agents into sensitive areas such as hiring and customer interaction. In several cases, these systems amplified historical biases or produced inconsistent responses. The result was reputational risk and loss of trust.
Poor Customer Experiences
Conversational AI deployed without sufficient context frustrated users. Hallucinations, irrelevant answers, and lack of organizational grounding undermined confidence—especially in high-stakes domains like healthcare and commerce.
The Integration Gap
Perhaps the most significant challenge was integration. Many AI solutions worked well as POCs but failed to connect with downstream decision-making systems. These “isolated intelligence assets” made it difficult to scale adoption, limiting real business impact.
The lesson was clear: AI that does not integrate does not deliver ROI.
A Maturity Shift: Tech Meets Realistic Expectations
Interestingly, the challenges of 2025 were not purely technical.
As Srivatsa Kanchibotla pointed out, human expectations also evolved.
In the early days, AI was expected to “wow” users—to behave like a near-human intelligence capable of surprising insights. Over time, organizations became more pragmatic. They began evaluating AI as a system that augments human intelligence rather than replaces it.
This alignment—between improving model capabilities and more realistic expectations—created a healthier environment for adoption. AI did not need to be magical; it needed to be reliable, explainable, and useful.
Looking Ahead: What 2026 Will Be About
If 2025 was about proving AI’s relevance, 2026 will be about redefining how organizations operate.
Several themes emerged as defining forces for the year ahead.
Autonomous Decision-Making Revolution
Enterprises will increasingly allow AI systems to make decisions—not just recommendations—within well-defined boundaries. From supply chain optimization to pricing and demand forecasting, autonomy will become a competitive advantage.
Natural Language as the Primary Interface
Technical barriers to data interaction are fading. Business users will increasingly engage with data and systems using natural language, reducing dependence on specialized technical skills.
Infrastructure at Scale
The industry’s bottleneck is shifting from GPUs to electricity. Massive investments in data centers and energy infrastructure will make AI inference cheaper and more accessible, accelerating adoption across use cases.
Foundations Before Features
Governance, compliance, transparency, and explainability are no longer optional. Organizations will need strong foundations to support ethical AI use, manage bias, and comply with emerging regulations.
What Enterprise Leaders Should Focus On
For decision-makers planning their 2026 AI strategy, three priorities stand out.
Governance and Readiness
As AI becomes ubiquitous through cloud platforms and enterprise tools, the key question will be: Are you ready for it? Leaders must establish governance frameworks that address ethics, compliance, bias, and accountability before scaling AI across the organization.
Integration Over Experimentation
The era of disconnected pilots is ending. Enterprises must deeply assess their data ecosystems, decision workflows, and tools—and make them integrable with evolving AI technologies. Flexibility will matter more than any single model choice.
Learn by Doing
Waiting for perfect, off-the-shelf enterprise AI solutions is a losing strategy. Most real-world use cases require “last-mile” business context that only organizations themselves can provide. Incremental experimentation and learning will outperform passive observation.
Making AI Reliable at Scale
A recurring theme throughout the conversation was reliability.
In 2025, many organizations asked a critical question: Can I put my reputation on this AI system?
The answer increasingly depends on how AI is implemented—whether it is grounded in high-quality data, integrated into enterprise workflows, and continuously improved through feedback and learning. Reliability is not a feature; it is a system-level outcome.
Closing Thoughts: From Excitement to Execution
2025 will be remembered as the year AI crossed the threshold from hype to reality. But 2026 will determine which organizations truly capitalize on that reality.
The winners will not be those chasing the flashiest models or the loudest promises. They will be the ones who invest in foundations, integrate deeply, govern responsibly, and move forward with clarity and intent.
As the conversation concluded, the tone was unmistakably optimistic. The AI revolution is no longer hypothetical. It is underway.
And for enterprises prepared to meet it, 2026 promises not just innovation—but transformation.
