The End of AI Experimentation: 6 Predictions for 2026
Having completed hundreds of engineering engagements, diligence assessments, and AI deployments over the years, Kickdrum’s team is on the front lines of technological advancement.
We spoke to our Principals about what lies ahead, and what they predict for 2026: where the industry is heading, which capabilities will matter most, and why 2026 will separate companies that can execute from those that are still experimenting.
1: AI will graduate from early production to true operational maturity
Prediction from Ryan Kennedy
Most companies can spin up an AI demo in days, and many already have AI features running in production. But in 2026, the market will stop rewarding teams for simply deploying AI and will start assessing whether teams can operate AI safely, reliably, and economically in production.
We are already seeing AI initiatives encounter, and work through a variety of hard operational questions:
Data readiness and governance
Evals and quality gates
Model and prompt versioning
Latency and cost targets
Security and privacy controls
Monitoring for drift and adapting to new failure modes as models evolve
Clear human override paths, and more
Expect more investor focus on whether a company has the engineering discipline, risk controls, and operating model to make AI durable, not just impressive.
2: AI will become its own diligence workstream
Prediction from Tom Carter
In 2026, AI will graduate from a sidebar in diligence to having its own dedicated track. PE firms will expect not just an assessment of the current situation, but a strategic plan for AI deployment moving forward. Diligence will shift from evaluating AI capability to evaluating credibility and strategy. Teams that win will have:
A defensible AI strategy
A clear understanding of value creation
Realistic roadmaps tied to data readiness
Defined risks and mitigations
Leadership capable of executing at scale
3: AI value will need to be measurable, not just hypothetical
Prediction from Nainish Dalal and Seth Krauss
2026 will be the year when the market stops caring about “AI potential,” and starts demanding evidence of AI value. Companies will be asked to prove that AI is driving:
Revenue lift
Throughput gains
Reduction in human effort
Error-rate improvements, and more
Measurable improvements in customer or business outcomes
Leaders will move from asking “can we use AI here?” to “is AI delivering ROI here?”
As this shift takes hold, we expect to start seeing examples of outcomes-based pricing. As systems become more predictable and instrumented, vendors will begin tying pricing directly to outcomes such as tickets resolved, documents processed, SLAs met, conversions achieved, and more.
4: AI governance will become a board-level requirement
Prediction from Seth Krauss
2026 will be the year AI governance shifts from voluntary frameworks and industry guidelines to real federal legislation. New laws will formalize requirements around how large language models (LLMs) are trained, evaluated, secured, and monitored, not just how enterprises deploy them.
This will introduce:
Standards for model provenance and data transparency
Requirements for documenting training sources
Safety reporting for model failure modes
Auditability of LLM behavior and evolution
Controls on fine-tuning, retraining, and deployment practices
This regulation will create new business opportunities, with entirely new products and services emerging.
5: Moving from “where to use AI” to “how to ensure AI doesn’t introduce new risk”
Prediction from Jay Kamm
Rather than focusing on where to deploy AI, organizations will focus on how to ensure AI doesn’t degrade security, performance, reliability or cost.
As AI accelerates engineering velocity, it will also accelerate failure modes that were previously caught by humans, especially the non-functional requirements that experienced teams implicitly enforced.
Companies will discover new categories of risk, such as:
Security vulnerabilities introduced by AI-generated code
Performance regressions as models optimize for speed over stability
Data integrity issues, including accidental deletion or corruption
Unexpected cost spikes, echoing the early days of cloud adoption
Loss of NFR discipline as AI bypasses manual quality heuristics
The challenge of 2026 will be building guardrails that allow AI to accelerate delivery without compromising systems, and organizations that develop mature practices around AI safety, observability, constraints, and quality engineering will move faster and ultimately win.
6: AI will become the new “cloud-spend problem”
Prediction from Ryan Kennedy and Jay Kamm
Much like the cloud cost challenges of 2015-2025, 2026 will be the year AI spend becomes a major financial and operational headache for technology leaders. AI will become a line item that must be controlled, justified, and optimized.
Companies will be judged on AI unit economics, including:
Cost per ticket resolved
Cost per onboarding
Cost per document processed
Cost per lead or conversion
Cost per workflow automated
The Bottom Line: 2026 will be the first year companies are judged on AI execution, not AI ambition
The experimental phase of AI is ending, and expectations are rising. The organizations that succeed in 2026 will be the ones that treat AI as a mission-critical capability that requires rigor, measurement, and operational discipline.
Kickdrum’s predictions come from what our senior teams see every day in real production environments, technical diligence assessments, and modernization work. We’ve lived the constraints, and that experience shapes our view of what’s coming.
If you're evaluating the maturity of your AI processes - from operational discipline to unit economics, governance, production readiness and more - please contact us. We’d love to help.
