AI in the Enterprise: Beyond the Hype
I've been discussing enterprise AI for well over a year now. Here's a consolidation of my thoughts with links.
AI is the new cloud. The hype is overwhelming, and the reality is far more complicated. Everyone wants to sprinkle AI on top of their enterprise tech stack and call it innovation. But as we’ve seen time and time again—technology without strategy is just noise.
Over the past few months, I’ve been deep in conversations with enterprise IT leaders, vendors, and technologists about the real-world impact of AI on the enterprise. Some of you have asked me, “Keith, is AI just another passing trend, or is this the next real transformation?”
The answer? It’s both.
AI will be as foundational as cloud computing, but success isn’t about which LLM you plug into your apps—it’s about how well AI fits into your existing enterprise strategy. Let’s break it down.
1. Build vs. Buy: Where Should Enterprises Invest in AI?
Enterprises have two choices when it comes to AI adoption:
✅ Build AI in-house – Custom AI models trained on proprietary data. This is a heavy lift, requiring dedicated teams of data scientists and AI engineers. Most enterprises aren’t built for this.
✅ Buy AI-powered solutions – Leverage AWS Bedrock, Azure OpenAI, Google Vertex AI, or AI-enhanced SaaS solutions like Salesforce, ServiceNow, and Workday. This is the more practical option for most companies.
💡 The reality:
Most enterprises should not be training their own AI models. Instead, focus on AI-powered workflows, automation, and analytics that enhance your existing operations.
📌 Further Reading: AI's Enterprise Evolution: Enhancing Cloud Applications with Data-Driven Intelligence
2. AI and the Workforce: Will AI Take Jobs or Create Them?
There’s a lot of doomsday talk about AI replacing workers. The truth? AI will change jobs, not eliminate them.
🔹 AI won’t replace your workforce—but companies that use AI will replace those that don’t.
🔹 The shift isn’t about AI replacing humans—it’s about AI augmenting workflows.
🔹 Organizations that invest in AI literacy and upskilling their teams will win. Those that don’t? Well, they’ll be stuck in 2019.
📌 Further Reading: AI Trends for 2025: Productivity, Integration, and Agentic AI
3. AI in IT Operations: The Rise of AIOps & Automation
The AI story in IT operations is real. Enterprises are already using AI-driven automation to:
🚀 Predict system failures before they happen
🔍 Enhance security with AI-powered threat detection
📉 Optimize cloud costs by dynamically scaling workloads
I’ve had conversations with CIOs implementing AIOps and seeing huge efficiency gains. But here’s the catch—AIOps is only as good as your underlying data and governance model.
📌 Further Reading: Architecting for Agentic AI – Moving from Simple Automation to Autonomous Decision-Making
4. AI is Useless Without Data: Fix Your Data Strategy First
If your enterprise is struggling with data silos, poor governance, or outdated ERP systems, throwing AI at the problem won’t magically fix it.
🔹 AI is only as good as the data you feed it.
🔹 Bad data = bad AI decisions.
🔹 If you don’t have a clear data governance strategy, don’t even think about AI.
Organizations that treat AI as a data-driven decision-making tool—not just a flashy feature—will get the most value.
📌 Further Reading: The Intelligent ERP: AI and Data in Enterprise Software
The AI Playbook for Enterprise IT Leaders
✅ Start with data: AI is not magic—it needs clean, structured, and well-governed data to work.
✅ Adopt AI gradually: Focus on AI-enhanced automation and analytics before jumping into large-scale AI initiatives.
✅ Align AI with business goals: If AI doesn’t have a clear ROI, it’s just another science project.
✅ Train your workforce: AI isn’t just about tech—it’s about people adapting to new tools and workflows.
AI isn’t just another trend—it’s a fundamental shift. But as always, it’s not about the technology—it’s about the execution.
I’d love to hear your thoughts. How is AI impacting your enterprise? Hit reply or share your insights with me on Twitter or LinkedIn.
Until next time,
Keith