Kizen’s AI Agents: Build, Train & Scale In Under 30 Days. Learn more.
Kizen’s AI Agents: Build, Train & Scale In Under 30 Days. Learn more.
Agentic AI
AI Industries
min read
In 2026, New York Tech Week by a16z brought together more than 50,000 founders, builders, innovators, and investors across 1,500 events.
For the first time, Kizen joined the official program at New York Tech Week to help shift the conversation from AI curiosity to real ROI and tangible impact. At our New York AI Lab, Kizen hosted a fireside chat featuring Maya Washington, Head of FDE at Kizen, alongside Kizen co-founder and CEO John Winner, and Adrian Trzaskus, COO of BluPrintX and founder of the AI Exec Board. Together, they explored topics like data readiness, governance, and what enterprise AI implementation and impact look like in practice.
"Achieving five to ten times faster outcomes with the help of AI isn't the difficult part. The real challenge is continuous integration. You have to keep going and connect AI to everything else in your organization,” Adrian Trzaskus stated.
A task that used to take two to three hours has been shortened into minutes by the advent of agentic AI. But constantly shedding time isn’t the real challenge when it comes to implementation. Rather, it’s about connecting AI across the entire organization to find the real impact, he said.
The initial deployment can deliver impressive efficiency improvements, but the organizations that generate compounding value are those who treat AI as infrastructure by connecting and integrating agents across functions, teams, and workflows.
CTOs, CIOs, and business leaders driving AI adoption have probably noticed two distinct groups of stakeholders they need to cater to: the absolute believers and the sceptics, Adrian explained.
"The people who are hesitant are often easier to convince once you show them what AI can do for them," Adrian explained. "Contrary to popular belief, the more challenging group to convince is actually the true believers who are already so committed to their favorite AI tools that they resist adopting whichever programs the business has chosen."
John Winner highlighted a leader's role in bringing people along: "Whether you're an early adopter or not, fostering curiosity about AI right now is an opportunity to build real advantage. The job of leaders is to show their teams what the future can look like with the help of AI, and simultaneously create a safe space to fail and explore together."
The playbook for successful implementation and true impact will look different for the two groups. True believers need adoption analytics and evidence that the business has chosen tools that can exceed what they've been learning independently. New users need structured training programs that show them impact to their day-to-day operations. The sooner enterprises recognize that one size won’t fit all, the sooner they’ll be able to reap the benefits for the entire organization.
Maya Washington asked what it actually means for an AI solution to be enterprise-ready:
John highlighted the industries Kizen serves: "In highly regulated industries, especially across mission-critical processes in healthcare, insurance, financial services, a small disruption doesn't exist. Every issue becomes a massive disruption across the entire organization. Security, scalability, and compliance are foundational guardrails that need to be built into every AI solution and implementation, and cannot be an afterthought.”
Enterprise-readiness is a commitment to both organizational design and the technology itself, in order to ensure stability while enabling innovation and real ROI.
To unlock the value of AI, leaders have to shift their perspective on why they are implementing AI in the first place.
"I think we spend too much time framing AI as a cost-savings play," John noted. "The question we all should ask ourselves is: what does it help us achieve and how does it make our work more impactful?"
Saving time is only valuable if you have a plan for it. The organizations that are successful with AI are those that have answered the question: if we get ten hours back per week per person, what should we do with those ten hours?
If we want to reframe the business case we need to rethink how experimentation is funded. The most successful teams go beyond just deploying AI as they thoughtfully build space to experiment, fail, learn, and iterate. John encouraged organizations to fund experimentation separate from production. If we create dedicated experimentation budgets, teams can remain curious and iterate without risking disruptions to their core operations.
AI agents are only as good as the data they work with. Clean, structured, accessible data isn't a prerequisite for getting started with AI, but it will determine how successfully you can scale.
"You can't wait until all your historical data is perfect," John said. "Focus on the most important data first; clean it, use it, and tackle the rest in phases."
The most impactful opportunity lies in changing how data is created in the future. By designing operations so data becomes an asset in real-time, enterprises can scale AI successfully and avoid accumulating further data debt in the future.
One concept from the conversation that we're monitoring as the technology evolves:
Self-learning architecture: it’s not just the AI or the agents that are getting smarter, but entire teams are improving alongside the technology. The most long-lasting AI deployments are built as frameworks where the data gets better, the agents get better, and the people leveraging them get better, together.
The conversation during New York Tech Week confirmed what we've discussed since we started Kizen: AI in itself doesn't transform organizations. People with the right tools, the right data, the right learning programs do; and leaders building the infrastructure for continuous improvement and curiosity will pave the way for that transformation.
"If anyone tells you they have AI figured out, they're not being honest. It's evolving too fast," Adrian stated. "The real work is driving adoption, meeting people where they are and enabling value quickly. AI isn't just a tool upgrade; it's existential for how organizations operate."