AI Urgency: Adapting to Change in B2B Tech

Oct 2, 2025
4 min read

Updated: Jan 20

AI urgency and economic uncertainty are pushing teams and leaders to adapt quickly. This talk for Agile New England explores how AI is reshaping not just our tools but also our teams, workflows, and what it means to lead. Thank you for having me!

What’s Changing in the Landscape

The macro environment is shifting. High interest rates, layoffs, and restructuring are putting pressure on teams to do more with less. Inside organizations, AI is becoming a teammate, not just a tool.

Roles are evolving, and what’s valuable is changing. It’s not just about building; it’s about defining, validating, and collaborating with machines.

The “ICEO” (AI-powered CEO) is emerging. Leaders are becoming more hands-on, often diving into the technical details to ensure AI work gets done right.

What Makes a Good AI Use Case?

A strong use case delivers real value to both the business and the customer. It uses unique data, starts with a measurable baseline, and solves problems that can only be addressed with AI.

If traditional software can do the job, or if the task requires absolute precision, it’s probably not a good fit.

The Importance of Unique Data

Unique data is crucial for effective AI applications. It allows for tailored solutions that traditional methods can't provide. When you leverage distinctive datasets, you set the stage for innovation and efficiency.

Measuring Success

To ensure your AI use case is effective, start with a clear, measurable baseline. This helps in tracking progress and understanding the impact of AI on your operations.

How We Build at OpsCanvas

At OpsCanvas, we use AI to eliminate cloud waste, improve visibility, and orchestrate smarter infrastructure decisions. Here’s what we’ve learned:

AI agents are powerful but need scoped, precise prompts.
Productivity can jump 2–3x, but validation still takes time.
Small, clear requests work better than broad prompts.
Human-in-the-loop review is essential.

The Role of Human Oversight

Even with advanced AI, human oversight remains critical. It ensures that the technology aligns with business goals and maintains ethical standards.

The New Must-Have for a PM: Evals

Product managers now need to be skilled in evaluating AI performance. This means measuring outputs for bias, accuracy, and value. Monitoring and feedback loops are essential to ensure AI remains consistent, safe, and useful over time.

Building a Culture of Continuous Improvement

Establishing a culture of continuous improvement is vital. Regular evaluations help teams adapt and refine their AI strategies, ensuring they stay relevant and effective.

The AI Builder Playbook

Start with Strategy: Define value clearly for both the business and the customer.
Validate the Use Case: Ensure it meets the criteria for a strong AI application.
Data and Baseline: Make sure you have the data, a measurable baseline, and a clear picture of what success looks like.
Prototype and Test: Use scrappy methods like mockups or light models to test quickly and learn fast.
Keep Humans in the Loop: Design for learning, not perfection.

Embracing Flexibility

Flexibility is key in the AI landscape. As technology evolves, so should your strategies and approaches.

Key Takeaway: The Future of Teams

AI won’t replace your team. But teams that use AI well will replace those that don’t. Embracing AI as a collaborative partner can unlock new levels of performance and innovation.

If you want to explore how to integrate AI into your processes, feel free to email me at jess@hallwaystudio.com or book a call if you want me to talk to your team.