By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

Introduction

This year has felt like a lifetime in the Generative AI (GenAI) world. Tools, capabilities, and best practices are shifting monthly, sometimes weekly. For supply chain professionals, the message is clear: ongoing development is not optional. Like lean, analytics, or S&OP in prior decades, GenAI proficiency is quickly becoming a differentiator. The question is not if you’ll integrate GenAI into your workflow, but how quickly and effectively. 

The Evolution of GenAI in 2025

When we look back to January, it’s striking how much progress has been made in less than a year. Early in 2025, the conversation centered on agentic AI and larger models. GPT-5 and Claude 4 improved reasoning and context windows, while OpenAI introduced ChatGPT Agent in preview, able to carry out bounded multi-step tasks like retrieving files, browsing the web, and drafting structured outputs. In supply chain, this translated into early experiments with automating shipment steps or running contract reviews in a single query — tasks that were pilot-level at best in January.

By mid-year, multimodal capabilities and enterprise copilots began shifting from concept to daily use. Users could combine text, image, and voice inputs to detect defects or summarize complex documents, and copilots became embedded inside SAP, Oracle, Microsoft, and Google platforms. For the first time, GenAI wasn’t just a tool "off to the side" but something integrated directly into the systems supply chain professionals rely on.

In the second half of the year, new capabilities started layering on: memory, specialized small models, and synthetic data with digital twins. Memory allowed copilots to recall context from prior chats or S&OP cycles, reducing rework. Domain-tuned models made GenAI lighter, cheaper, and faster for logistics, procurement, and planning tasks. And digital twin integration allowed organizations to stress-test networks under disruption scenarios, from weather to labor shortages.

Enterprises also moved closer to operations with AI at the edge, using IoT data for predictive maintenance or real-time routing. At the same time, guardrails and compliance became a central topic, with more organizations creating clear "green/yellow/red" tiers for safe use. And in Q4, collaboration AI and hybrid architectures came to the forefront — copilots that can negotiate contracts in multiple languages, and architectures that blend closed and open-source models to balance sovereignty, cost, and security.

For mainstream individual users, the picture is simpler but still powerful. Anyone with ChatGPT Plus or Copilot today can take advantage of:

• Memory and custom instructions to save preferences and formats across sessions.
• Project-only memory (rolling out) to organize work by context.
• Agent previews like Operator to see how automation might work on bounded tasks.
• Connectors and file uploads to bring internal data into conversations. 

For leaders, the focus is on policy, safe pilots, and scaling. They are:

• Sponsoring agent experiments in low-risk domains (like supplier alerts).
• Embedding copilots in enterprise systems for daily planning and reporting.
• Formalizing AI use policies so employees know what’s encouraged, conditional, and off-limits.

The net result: what started in January as experimentation has, by October, become a layered landscape. Individual users now have practical tools to reclaim time, while leaders are piloting more ambitious integrations and building the governance to make adoption sustainable.

1. Action Planning is Critical

The pace of change makes a one-and-done training activity insufficient. Think of GenAI skills like fitness: it requires steady reps over time. Professionals who set quarterly development goals — experimenting with new tools, building prompt libraries, testing workflows — will not only stay current but pull ahead.

💡 Try This Quarter:

• Build a custom prompt library for routine tasks (e.g., supplier follow-ups, KPI summaries).
• Test one open-source tool such as LangChain or Haystack.
• Use AI to summarize two recent meetings and validate output with your notes. 

2. Prompt Maturity is the New Literacy

I’ve personally learned the most about prompting by asking ChatGPT to critique my style against a 12-step framework. The feedback gave me a process improvement plan I still use today. Prompt maturity isn’t abstract — it’s a measurable, improvable skill.

💡 Applied step: Rewrite one work prompt per week by climbing the ladder. 

3. Unlocking Personal Productivity

One of the fastest returns from GenAI comes from personal productivity. In our short courses this year, I’ve seen learners gain comfort and lower stress as they practice more with the tools. Many reclaimed time by using GenAI for emails, presentations, meeting notes, and data prep.

While the list of GenAI time-saving strategies is broad, some uses are already mainstream and validated by thousands of professionals. The table below organizes these strategies into categories, provides guidance on how to accomplish them, and highlights common watch-outs to ensure they deliver value without risk.

💡 Try this week: Track one workflow where AI saved time and estimate the hours reclaimed.

4. Critical Thinking: Ironically More Important than Ever

We wrote about critical thinking and added it to our curriculum after studies raised concerns about overreliance on AI. The smarter the tools become, the more important it is to validate their outputs.

💡 Applied step: Take one AI output this week and run it through the checklist — you’ll see both strengths and blind spots.

5. Advocating for Strategy and Guardrails

We’ve seen firsthand how AI policies can evolve. One major retailer shifted in less than a year from a rigid “only data scientists experiment” model to encouraging all employees to try safe versions of multiple LLMs. This shift shows why professionals should advocate for strategy and guardrails that evolve with the technology.

💡 Ask your manager: Which of our daily tasks fall into green, yellow, and red today? 

6. Agents: Early but Essential

Many industry partners are actively testing agents. Our software partners are hitting singles and doubles now, with bigger “home run” opportunities still developing. Agents aren’t fully reliable yet, but they are advancing quickly and will increasingly appear in ERP, TMS, and WMS platforms. 

In practice, most organizations today sit between Level 1 (Exploratory) and Level 2 (Task-Specific Agents), with early pilots pushing into Level 3 (Augmented Workflows). Tech-forward enterprises — particularly in retail, e-commerce, and global manufacturing — are building domain-specific agents for forecasting, procurement support, and transportation planning, often embedded inside ERP or planning platforms. These companies are experimenting with multi-agent coordination but keep humans firmly in the loop. By contrast, mainstream companies are still largely in the exploratory stage: individuals using general copilots for drafting documents or ad hoc analysis, without enterprise integration, security controls, or governance. The gap is widening — forward-leaning firms are developing playbooks for orchestrated workflows, while many organizations are just beginning to set policies and figure out where AI fits safely into their operations.

Looking ahead, Level 4 (Collaborative Automation) is where the near-term breakthroughs will happen. In the next 3–5 years, we can expect multi-agent orchestration to become a practical tool for managing recurring disruptions — think transportation rerouting during weather events or automated supplier alerts when delivery milestones are missed. Early adoption will occur in large, tech-forward enterprises with strong governance and secure infrastructure. Level 5 (Autonomous Resilience) remains aspirational: while the vision of end-to-end supply chain automation is compelling, regulatory hurdles, trust, and explainability challenges mean human oversight will remain essential. The more realistic trajectory is that enterprises will selectively automate narrow disruption scenarios while maintaining tight human control, with broader autonomy coming only as governance, standards, and trust mechanisms mature.

💡 Applied step: Identify one repetitive process in your work that could be a candidate for an agent. 

7. Human in the Loop: Non-Negotiable

Competition has improved model quality this year — but hallucinations and memory issues remain. That’s why “human in the loop” is not just a principle; it’s operational reality. AI is still an assistant, not a replacement.

💡 Applied step: Write down one checkpoint you always apply before sharing AI outputs.

Conclusion

These observations — from teaching courses, updating curriculum, and watching partners experiment — motivated this article. GenAI is evolving at extraordinary speed, and our profession must evolve with it. Build your plan, refine your prompts, reclaim time, apply critical thinking, advocate for strategy, explore agents, and always keep the human in the loop. Those who do will thrive in 2026 and beyond.