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May 13, 2026: Scaling AI Is the Hard Part — Here's Where Enterprises Are Getting It Right (and Where They're Still Stuck)

  • Writer: James Sale
    James Sale
  • May 13
  • 5 min read

The conversation about AI at work has shifted. It's no longer about whether to adopt — it's about who can actually scale. New data and real implementation stories out today show a widening gap between organizations running AI in production and those stuck in pilot purgatory. The specifics are worth your attention.


Here's what happened in the last 24 hours.


The Scaling Problem Is Real, and C-Suites Are Feeling It

Infor's Enterprise AI Adoption Impact Index surveyed 1,000 executives across retail, manufacturing, and logistics. The finding: more than half of businesses are struggling to scale AI beyond initial workflow use cases. That's not a technology problem. That's a process, change management, and integration problem.


C-suite leaders in these industries are using AI for workflows — that part's happening. But moving from a handful of power users to organization-wide adoption is where momentum stalls. If your team is in that gap right now, you're not alone, and the issue likely isn't the tool.


The question worth asking is whether you've defined what "scaled" actually means for your organization before you started. Most teams haven't.


Where Agentic AI Is Actually Starting Inside Enterprises

Intellias, citing Deloitte research, offers a clear picture of where enterprises are choosing to start with agentic AI (AI systems that can take multi-step actions autonomously): engineering. Specifically, prototyping, code generation, and testing.


That's not a coincidence. Engineering workflows have clear inputs and outputs, measurable quality signals, and teams that can evaluate AI accuracy without being fooled. Starting agentic AI in a function where you can actually verify the output is smart risk management.


For CIOs thinking about where to pilot agentic systems, the Deloitte data suggests engineering is the lowest-risk, highest-feedback entry point before expanding to other functions. Once you've built the governance muscle there, you'll have a better framework for the messier domains.


Morgan Stanley's Approach to Advisor Productivity

Calls9 Insights reports that Morgan Stanley has deployed GenAI to analyze over 1 million annual conference calls, surfacing insights for financial advisors to improve client interactions. The goal: streamline advisor workflows, not replace advisor judgment.


That distinction matters. AI handling the information synthesis layer — reviewing transcripts, flagging themes, surfacing patterns — while human advisors apply relationship context and professional judgment is a model that works. It's not glamorous, but it's the right architecture for high-stakes professional roles.


If you're in financial services or any domain where client relationships carry real risk, that's the design principle worth borrowing.


Translation Work Is Getting Faster, But the Nuance Still Requires Humans

TIME's coverage of white-collar AI shifts includes a look at DeepL's new AI translation tool. DeepL CEO Jaroslaw Kutylowski highlights a specific feature: custom glossaries and follow-up questions that let translators work faster in niche domains where precision is non-negotiable.


This is a useful example of what good human-AI workflow design looks like. The AI handles volume and consistency; the human expert handles domain accuracy and edge cases. Speed goes up, quality holds, because the tool is built around the human's workflow rather than trying to replace it.


The professional translation market is one of the cleaner examples of AI augmentation done right — and the lesson extends well beyond language work.


Headcount Planning Is Getting a Real Upgrade

ChartHop's AI headcount planning tool now generates multiple hiring or restructuring scenarios in minutes, with instant budget impact modeling and org design visualization. What used to take a people operations team days of spreadsheet work — modeling three to five headcount scenarios against budget constraints — is now a fast iteration loop.


This matters most during planning cycles and any period of organizational change. If your team is still doing headcount modeling in static spreadsheets, you're spending time on structure instead of decisions.


The practical move: evaluate whether your current planning toolset can run scenario modeling at speed, or whether you're artificially slowing down leadership decisions because of tool friction.


IT Leaders Are Running AI Agents in Production — More Than Half of Them

Moveworks reports that more than half of IT leaders are now running AI agents in production environments, with Moveworks agents automating both routine and complex tasks across enterprise workplace applications.


That's not a pilot stat. That's production. And it marks a meaningful shift from AI as a bolt-on to AI as infrastructure inside IT operations.


For business leaders outside IT: this is your best internal case study. Your IT organization has likely already worked through the integration, security review, and change management challenges that your function is still anticipating. Talk to your CIO before you build your own roadmap from scratch.


HR Automation Is Covering the Full Employee Lifecycle

Leapsome's recent breakdown of HR automation lays out exactly where AI is being applied in HR right now: performance reviews, goal-setting, onboarding, learning pathways, and payroll. Not just one piece — the full employee lifecycle.


The implication is straightforward. HR teams that were already under-resourced relative to headcount now have a path to run more consistent, timely processes without adding staff. The risk, as always, is that automation without human oversight produces fast but shallow results — especially in performance and onboarding, where the quality of the experience directly affects retention.


Automate the administrative layer. Keep humans in the loop where the experience actually shapes employee trust and performance.


Y Combinator Startups Have Made LLMs a Baseline Requirement

Bee Techy's 2026 State of Enterprise AI report puts a sharp number on where the startup market has landed: 92% of Y Combinator startups have integrated LLMs into their core product architecture. For venture capital, it's now effectively table stakes.


That has downstream effects on enterprise buyers. Products you're evaluating — whether for HR tech, operations, sales, or customer service — are increasingly built on large language model infrastructure from the ground up. That changes the due diligence conversation. You're no longer asking "does this product have an AI feature?" You're asking how the AI layer is built, what data it touches, where it can fail, and who's accountable when it does.


Procurement and security teams that haven't updated their vendor review frameworks for LLM-native products are working with the wrong checklist.


Consulting Firms Are Using AI to Reimagine Delivery Models

SAP's resources on AI in consulting detail how firms are combining AI tools with human expertise to accelerate transformation project delivery. The focus is on faster time-to-value for clients — less time on information gathering and structuring, more time on decisions and implementation.


Panorama Consulting Group adds specifics on where this is showing up in professional services: capacity planning, project risk detection, and financial forecasting inside ERP and PSA systems (professional services automation platforms that manage projects, resources, and billing).


If you're buying consulting services right now, it's fair to ask your partners how AI is affecting their delivery model — and whether those efficiency gains are being passed to you or absorbed into margins.


What Connects All of This

The through-line today is execution discipline. AI tools are no longer in short supply. The challenge is building the processes, governance, and human-AI workflow designs that let organizations actually capture value from them at scale.


The Infor data says more than half of enterprises are struggling to scale. The Morgan Stanley and Moveworks examples show what working at scale looks like. The gap between those two conditions isn't technology — it's clarity on where humans add irreplaceable value, and building AI workflows that support that rather than work around it.


That's the conversation happening inside successful organizations right now. It's worth having on your team before your planning cycle, not after.


If you want to stay ahead at the intersection of AI, automation, and human performance — where technology meets psychology, processes, and real workplace behavior — subscribe to Agenticism. We cut through the hype to deliver practical insights for leaders focused on making people, processes, and technology work better together.


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