AI in insurance will be a big market, but the winners won't be those who just bolt on chatbots.
It will be those who wire AI into the operational backbone—submissions, triage, fraud, portfolio steering, etc.

#TMinsights

Modular, task-specific AI agents will beat monolithic “super agents”.

Narrow scope = fewer hallucinations.
Clean handoffs = easier orchestration.

Companies trying to build one agent to rule them all are wasting resources.

Think small, focused, composable.

#TMinsights

In 2026, expect AI asset management to become as standard as CMDBs for infrastructure.

That means building inventories of models, agents, prompts, datasets, and integrations across the enterprise.

#TMinsights

Many organizations still treat AI as an incremental add-on. Ideally, they should use AI to challenge the shape of org charts, processes, and even business models.

That mindset shift is the real strategic unlock.

#TMinsights

Generative AI was act one. Act two is agentic AI—autonomous systems that don’t just generate; they strategize, plan, and execute multi-step tasks without constant human intervention.

Your competitive advantage won’t come from chatbots. It’ll come from orchestrated agent networks.

#TMinsights

Stop the "pray and spray" pilot spending. It's time to build measurement rigor into every AI initiative.

#TMinsights

The next generation of identity is cryptographically provable, consent-driven, and privacy-shielded.

#TMinsights

Don't treat privacy as a compliance exercise—it can be your business differentiator.

#TMinsights

The cost of a data, privacy, or cyber violation is rarely just the fine; it’s the opportunity cost of delayed deals, lost partnerships, and the intangible erosion of brand equity.

Invest in a holistic AI governance stack—observability, consent management, ethics boards.

#TMinsights

The fusion of AI with BFSI promises unprecedented efficiency, but it also disrupts the domains of identity, privacy, consent, and cyber ethics.

Your focus and strategies will determine whether these pillars are upheld through robust governance or violated in the rush for innovation.

#TMinsights

The new leadership competency isn't just deploying AI—it's explaining it. This is where technical leadership meets storytelling.

Future leaders must interrogate models, understand confidence intervals, detect model drift, and decide when to override AI with human judgment.

#TMinsights

In enterprise AI, depth is beating breadth.

#TMinsights

The 2025 plan: hire a handful of AI experts and hope they transform the company.

The 2026 plan: turn every role into an AI-augmented role.

This shift—from specialist-centric to workforce-centric AI—is how enterprises can escape the talent bottleneck.

#TMinsights

Most enterprises say data quality is their #1 blocker to scaling AI. These are not data‑poor companies—they’re data‑rich and context‑poor.

You can’t out‑model broken data. You have to redesign pipelines, ownership, and governance or the models will simply expose your mess faster.

#TMinsights

Consumer-style prompts don’t translate to mission-critical, multi-step processes.

Architect for that gap or pay for it in production.

#TMinsights

The attack surface is moving from the firewall to the prompt.

#TMinsights

The most concerning metric from the year-end reports I'm reading is: “Atrophy of critical thinking skills due to GenAI.”

We need to retain focus on “First Principles Thinking.”

If you can’t think without Copilot, you’re a liability in a crisis.

#TMinsights

The “Agent Marketplace” concept is evolving faster than SaaS did.

The competitive advantage shifts from building AI to assembling agent teams.

The shift from “One Giant Model” to “Router Networks” (routing queries to the cheapest/fastest model) will be the way to save on compute costs.

2026 could be the year of the “Model Router.”

Optimization is the new innovation.

Deep Domain Expertise + Commodity AI >> Shallow Expertise + Commodity AI.

Enterprises don’t want to pay a large LLM for every internal query. They want a “good enough” model that runs cheap and fast.

This will result in us seeing more model distillation: train on a massive frontier model, then distill the knowledge into a tiny model.

2026 could see "corporate SLM.”

Prompt engineering is essential. But equally important is “agent management.”

We should be able to evaluate the AI’s reasoning, and intervene when it deviates.

However, in a world where so many professionals lack effective management skills, upskilling in logic and delegation is critical.

We spent the past decades securing “users” (identity management).
Now we have to secure “agents.”

When an AI agent has permission to read your email, query the database, and send a Slack message, it is an insider threat by design.

CISOs should start talking about “non-human identity management.”

As AI coding tools become widespread, models are effectively replacing mid-level software engineers.

This can lead to “software inflation”: if code is cheap, we will build more software.

Every department might be building its own mini-apps and agents.

The CIO becomes “governor of chaos.”

Your employees aren’t waiting for IT to approve a tool. They are using their own $20/month subscriptions to automate their jobs.

This “Shadow AI” creates a massive governance hole. But it also creates a massive signal.

Don’t ban it. Map it.

That is your roadmap for enterprise adoption.

The decoupling of revenue from “seats” is the biggest disruption to the B2B business model in the past two decades.

If you're a SaaS leader, you need to figure out how to cannibalize your own seat-based revenue before an agentic startup does it for you.

The moats of 2026 aren’t in the model weights; they’re in workflow integration.

If you’re a founder, don’t build a “better LLM.” Build a “better lawyer,” “better doctor assistant,” or “better underwriting engine.”

The “Vibe Check” era of AI investment is over. If you can’t show P&L impact (Revenue Lift or OPEX Reduction), your project gets cut.

Unglamorous, boring AI is where the money will go.

Boring is profitable.

The biggest force holding back enterprise AI isn’t the model capability—it’s the “gravity” of legacy data silos.

Your data strategy should no longer be about “analytics”; but about “affordance”—making your data legible to a machine agent.

2026 will see a hard pivot from "chatting with AI" to "assigning work to AI" .