Predicting the future of AI is a good way to look foolish, especially at the pace things are moving. So I’ll start with a disclaimer I shared on a recent episode of The Responsive Podcast: I reserve the right to change my mind.
That said, 2025 gave us clarity. Not clarity in the sense that all the questions are answered, but clarity about what doesn’t work, what was overhyped, and where real value is starting to emerge. As we kick off 2026, AI is moving out of the experimental phase and into something far more consequential: production systems that need to earn trust, deliver ROI, and fit into how people actually work.
Watch the episode below to hear my thoughts, or keep reading for my three predictions for where AI and Strategic Response Management (SRM) are headed in 2026, all of which are grounded in what we’re already seeing unfold.
Prediction 1: AI agents start working together
If 2023 was the year of GenAI tools like ChatGPT, and 2024-2025 were the years everyone talked about AI agents, then 2026 will be the year agents start delivering value together.
Over the last year, agents have dominated the conversation. Much of the hype centered on autonomy: the idea that agents would replace humans or take over entire roles. That didn’t play out the way many predicted, and honestly, it shouldn’t have.
What did happen in 2025 was a growing realization that agents are most effective when they’re focused on specific workflows, tasks, and decisions, with humans firmly in the loop. Agents can do a lot of the heavy lifting, but they shouldn’t make final calls without context or oversight.
I often use a simple personal example. Could I build an agent that finds the best restaurant for Friday night and makes a reservation? Absolutely. Would I let it do that without asking me first? No. I still want to decide where I’m in the mood to eat. That same principle applies in business.
What excites me about 2026 is the next step: agent-to-agent interaction. Today, many agents live inside individual tools or applications. And even within discrete tools, some agents operate within their own silos. They’re powerful, but they’re isolated. When agents start talking to each other — sharing context, coordinating actions, and orchestrating workflows across systems — that’s when things get interesting, as long as people still remain responsible for the decisions that matter.
Technically, this is already possible. Frameworks and standards are emerging that make agent interoperability feasible. What hasn’t happened yet is broad, thoughtful implementation. In 2026, we’ll start to see organizations figure out where connected agents actually create value, rather than just proving they exist.
That’s the core work for organizations this year: moving from vision to reality, focusing less on adding agents and more on building better systems.
See what connected agents look like in practice
Prediction 2: AI is judged by performance under pressure
One of the quieter but most important shifts heading into 2026 is that AI is becoming more dependable in real work, not just impressive in demos.
Over the last couple of years, a lot of attention has gone into LLMs, prompts, and prompt engineering: figuring out how to ask AI the “right” way to get a good answer. That helped teams get started with AI, but it also created a fragile reality. If results depend on the perfect prompt, that approach doesn’t hold up reliably in real-world use.
In 2026, AI will be judged less by how clever it sounds and more by how reliably it supports real deadlines and deals. For revenue and proposal teams, this shows up in very practical ways. Instead of wondering, Will this answer be accurate this time? Or Why did it work yesterday but not today?, teams will expect AI to perform consistently across different questions, users, and scenarios.
A good example is RAG, or retrieval-augmented generation. (Bear with me while we get technical for a second.) At a high level, this is how AI extracts answers from your content rather than making them up. Early versions often involved giving the model a large block of content and hoping it produced the right answer. When it didn’t, hallucinations became a major concern, especially in high-stakes responses like RFPs, security questionnaires, and executive summaries.
What’s changing now is how these systems are built behind the scenes so that teams see fewer surprises on the front end:
- AI is being routed more intentionally, instead of treating every task the same way
- Outputs are designed to be repeatable, not just a one-off success
- Responses are evaluated continuously, not just during pilots or demos
The result is steadier, dependable AI. That matters because proposal and revenue teams don’t get ten tries. You often get just one chance to get it right under a tight deadline. An AI system that works “most of the time” isn’t good enough when credibility, compliance, and revenue are on the line.
This also changes how organizations should evaluate AI. A tool that performs perfectly in a controlled demo can still fail under real-world conditions. In 2026, buyers will need to look past surface-level results and ask harder questions about consistency, governance, and how AI behaves when inputs vary.
The upside is significant. When AI is engineered for reliability, teams stop thinking about using AI and start relying on it. It becomes a seamless part of the workflow and not something you need to double-check nervously. 2026 is the year AI becomes something you trust to support real decisions and real revenue.
See how trust becomes measurable
Prediction 3: Governance becomes the deciding factor for AI at scale
In 2026, readiness to operate AI at scale becomes the defining question for organizations.
As AI becomes more embedded in revenue and response workflows, governance stops being an abstract concern and starts to shape day-to-day usage. It shows up in how confidently teams use AI under deadlines, how broadly it’s rolled out across functions, and how much oversight is required before outputs can be trusted.
For proposal and bid teams, governance determines whether AI helps you move faster or creates more review cycles. If answers can’t be traced back to approved sources, or if there’s uncertainty around accuracy and compliance, organizations hesitate to expand AI beyond small, controlled use cases. That slows teams down, especially in high-stakes responses where there’s no margin for error.
For sales and revenue teams, governance influences scale. AI that works in a small group but can’t be used consistently across sales, proposals, and customer-facing roles won’t deliver meaningful impact. Without clear guardrails, organizations hesitate to expand usage, which limits the return on what should be a force multiplier.
And for IT, security, and compliance teams, governance is about anticipating reality. Not every user will follow best practices every time. Someone will undoubtedly try to use AI in a way it wasn’t intended (intentionally or not). The question isn’t whether rogue or unapproved AI use will occur, but whether systems are designed to handle it responsibly.
This is where AI creates new operational expectations. Teams not only need accurate answers, but they also need to know how those answers are governed, reviewed, and approved before they’re shared externally. In 2026, organizations will expect these guardrails to be built into the tools they adopt.
Regulation and standards will accelerate this shift. As frameworks around AI security and governance take shape globally, they’ll increasingly influence how AI systems are designed, approved, and used. Governance moves beyond policy documents and into how AI operates within workflows.
When this is done well, something interesting happens. AI fades into the background. Teams don’t need to consider whether to use it. They just use it because the guardrails are already there.
What this means in 2026
If you take a step back, there’s a common thread running through all three of these predictions. AI is growing up. It’s moving out of the phase where experimentation alone is enough and into a phase where it has to hold up under real-world conditions.
In 2025, we saw progress, but adoption was uneven. Some teams embraced AI deeply and started to see real gains. Others stayed cautious, using it only for low-risk tasks or isolated experiments. That tension is still there, but it’s getting harder to sustain.
In 2026, the difference won’t come down to who has access to the latest tools. It will come down to who has done the work to connect AI to real workflows, make it dependable, and put the right guardrails in place so teams can use it with confidence.
The notion that AI will replace people is now outdated. Instead, a winning AI philosophy is centered on building systems that support how work actually gets done, especially in the moments where speed, accuracy, and trust all matter at once.
That’s the bar in 2026. Not more hype or experimentation for its own sake, but AI that earns its place in the work that actually matters.