Make Your AI Investment Work in 2026

Jan de Vries · 2026-03-03

By Satish Thiagarajan, founder of Brysa, a Salesforce and data consultancy. He helps companies turn data signals into real action, focusing on connecting insight with execution in sales, marketing, and service. Let's be honest—making AI investment work feels like an endless struggle. Year after year, tech leaders watch their plans crumble into systems that never quite deliver. With AI, this problem is getting harder to ignore. It's not that people aren't good at their jobs. They're just getting caught by scope. AI promises the moon, but when you're drawn to a system for what it *can* do, rather than what your company *needs*, you'll never see a worthwhile return. 2026 demands a completely new approach. ### Understanding the AI Maturity Model Before you even think about investing in AI, you need to figure out if your organization is actually ready. The AI Maturity Model helps you see what's best for your current situation. **Stage 1: Foundation** You're here if your data is all over the place, systems barely talk to each other, and teams are stuck with manual fixes. Automation is basic, and any AI efforts are fragile. This is where most companies sit, even the ones claiming to "do AI." At this stage, you're not AI-ready. The priority is getting your data clean, integrating systems, and building operational discipline. Jumping into advanced AI before fixing these foundations? That's a recipe for disappointment. **Stage 2: Enhancement** In Stage 2, your data is mostly clean, systems are integrated, and AI starts helping with forecasting and decisions. Intelligent workflows speed things up, but humans are still running the show. AI is delivering value, but it's just augmenting work, not transforming it. The danger here is confusing efficiency gains with true maturity. **Stage 3: Transformation** You hit Stage 3 when AI can work on its own within clear rules, workflows adapt in real time, and systems optimize themselves without constant hand-holding. Teams move from doing the work to overseeing strategy. AI isn't just a tool anymore; it's a core part of how you operate. Very few organizations are genuinely here, no matter what they imply. ### A 5-Part Framework for Your 2026 Tech Investment Getting to Stage 3 takes more than just buying tech. You need a structured way of thinking. **Start with outcomes** The biggest mistake companies make is buying AI first and then hunting for problems to solve. The result? Underused tools and weak ROI. If you want your investment to pay for itself, every purchase must link to a clear business outcome. Ask the right questions: - How will this boost revenue? - How will it reduce operational friction? - Will it increase productivity or improve decision quality? - How will it support a better customer experience? When outcomes are clear, your decisions get better. You stop chasing shiny AI features and start funding real, measurable growth. **Build vs. buy vs. partner** You can't do everything in-house. But buying everything doesn't always make sense either. And you shouldn't tackle every rollout alone. Getting this wrong can set you back years. Here's a simple rule to follow: - **Build** when it's a true competitive differentiator, and you have the talent and time to maintain it. - **Buy** when the function is standard and won't set you apart—think CRM, basic analytics, or service management software. - **Partner** when speed is critical and your internal know-how is limited. We often underestimate execution risk. Partnering isn't about outsourcing accountability; it's about leveraging expertise to move faster and smarter. It's a strategic choice, not a cop-out. Remember, the goal isn't to have the most AI. It's to have AI that works for you. Focus on your foundation, be brutally honest about your maturity stage, and let clear outcomes guide every dollar you spend. That's how you make 2026 the year your investment finally pays off.