Welcome to AI4J™!

AI is no longer a side experiment. It is quickly becoming a standard part of enterprise IT, both in how systems get built and how teams get work done. For CIOs, CTOs, and team leads, the hard part is figuring out which AI efforts will actually pay off without creating unnecessary risk for the company. In this session, you will get a practical way to pick the right first pilots, define success metrics that matter, and avoid the most common traps. Those traps include leaking sensitive data, getting unreliable output, having no clear owner, and running pilots that never turn into real ROI. We will talk about how AI tools fit into everyday team workflows, how to balance value and risk so you know where to start, and what guardrails to put in place from day one. That includes data boundaries, human oversight, auditability, evaluation, and safe fallback behavior. You will leave with a simple checklist and an action plan you can use right away to launch a secure, measurable AI pilot that your team can ship and your organization can scale.

The AI space is moving incredibly fast, it seems new methodologies and technologies are coming every week. How’s a technology leader (whether your a VP Engineering, Software Dev Manager or Team Lead) supposed to understand what are the true building blocks for this new class of applications. How do you scope an AI development project, both in terms of developer time and cloud & AI infrastructure? Should you buy AI hardware or pay for API access to OpenAI, Claude, Gemini, etc? Do you have sensitive information that you want to keep from leaking out to an external LLM provider? In this session, we’ll tackle these issues and also discuss the evolution of applications and the difference between: existing applications that have added AI capability as an accessory this new class of applications that are built with AI in mind from the start This session is intended to be interactive – I’ll start by laying the foundation for building AI applications today, and we’ll discuss the experiences of the tech leaders in the room so everyone can share and learn from each other.

Python is the language of data science and dominant in AI research. However, it is not the language of enterprise apps, and there are good reasons for this. In this session, Rod will discuss when to use what language and stack for AI success in enterprise. He’ll discuss the key adjacencies for success: LLMs, existing data and business logic, and how to choose what language, stack and framework for a particular problem.

Modern AI coding assistants are significantly hampered by bloated codebases — every unused, abandoned, or “dead” method that gets loaded into an AI’s context window burns tokens and shrinks the reasoning headroom available for the work that actually matters. When an AI must process code nobody runs, it produces weaker suggestions, more hallucinated API calls, and longer, less precise prompts. The challenge is that much of this problematic code isn’t technically “dead” in the traditional static-analysis sense — it simply never executes in production, making it invisible to conventional tools. In this webinar, we’ll show how runtime evidence can identify which code is truly unused, and how to leverage AI itself to safely remove that dead weight — restoring the context headroom your AI needs to perform at its best.

AI is redefining how engineering organizations operate, shifting from traditional development to agentic development, where intelligent, context-aware agents partner with teams to drive measurable business outcomes. This presentation gives leaders a clear framework for understanding how agentic development improves cycle time, reduces operational risk, enhances quality, and scales organizational capacity without adding headcount. We will examine how to move beyond pilots, achieve meaningful adoption, embed governance and security controls, and connect engineering effort directly to enterprise KPIs. Leaders will leave with a strategic roadmap for guiding their organizations through this transformation with clarity, confidence, and control.

What if AI could not only support development but actually help bring an entire platform from concept to delivery? In this session, we’ll explore the journey of building Hivemindd, a resourcing platform connecting startups and SMBs with fractional experts. From day one, AI wasn’t just a feature, it was a team member. We’ll walk through how we used AI to: Translate early ideas into functional requirements and user stories. Accelerate technical design, architecture, and decision-making. Support engineering teams with code scaffolding, testing, and documentation. Drive go-to-market content, investor storytelling, and community engagement. Along the way, we’ll share what worked, what didn’t, and how AI shifted the way we think about product development. Whether you’re curious about using AI in your own projects or leading teams through the uncertainty of new product creation, you’ll leave with practical strategies and a real-world case study of AI helping a startup move at startup speed.

AI enablement isn’t buying Copilot and calling it done–it’s a system upgrade for the entire SDLC. Code completion helps, but the real bottlenecks live in reviews, testing, releases, documentation, governance, and knowledge flow. Achieving meaningful impact requires an operating model: guardrails, workflows, metrics, and change management; not a single tool. This session shares SPS Commerce’s field notes: stories, failures, and working theories from enabling AI across teams. You’ll get a sampler of adaptable patterns and anti-patterns spanning productivity, systems integration, guardrails, golden repositories, capturing tribal knowledge, API design, platform engineering, and internal developer portals. Come for practical menus you can pilot next week—and stay to compare strategies with peers.

In today’s fast-paced technological landscape, organizations must stay ahead of the curve to remain competitive, particularly when it comes to adopting emerging technologies like Artificial Intelligence. But integrating cutting-edge tools—such as GitHub Copilot, an AI-powered coding assistant—into development teams isn’t as simple as flipping a switch. Resistance from developers, unfamiliarity with the tool, and the pressures of delivering work on time can all pose significant barriers. In this talk, we’ll dive into the strategy behind our successful initiative to accelerate the adoption of GitHub Copilot within our development teams. From overcoming initial resistance to achieving productivity gains, we’ll explore the lessons learned and the structured approach we used to foster adoption, collaboration, and long-term success. Attendees will leave with actionable insights on how to create a culture of experimentation, incentivize learning, and create evangelists within their teams—viewing the challenge of integrating new technologies as an opportunity for growth.

The threat to your job isn’t that an AI can replace you — it’s that a manager will think AI can replace you. This talk covers how to build a relationship with your manager that makes you their primary technical ally. The idea is to become the person they trust to evaluate both the benefits — and the costs — of using AI tools, showing where they can help and where you really need to be careful using them. In a rapidly changing world, you can become the person your manager relies on to give them good advice, all while you get to enjoy playing with the latest toys.

Most companies have spent years building out large API libraries — now the question is how to expose them to autonomous AI agents. Using the Open Bank Project as a real-world test case — a suite of 717+ REST APIs enabling core banking integrations including PSD2 compliance — this session explores practical approaches to bridging existing API infrastructure with agentic AI. Two architectural patterns are examined side by side: the widely known MCP (Model Context Protocol) and a second, lesser-known alternative demonstrated live. Attendees will walk away with concrete strategies for making their existing API investments agent-ready.