How To Prioritize Digital Initiatives For Impact

Too many data and AI teams aren’t tied to a critical business function or revenue-generating product. In the rush to cash in on going AI-first, businesses forgot to select impactful projects for teams to work on. Data scientists and engineers can spend years working in one of these teams, but have little to show for it aside from a few “special projects” with no connection to business impacts. It’s like having a black hole on your resume, and it makes finding a new job more difficult. When I talk to people who aren’t having success on the job market, they’re typically escaping a zero-impact team or role. The solution starts with technical leadership. If your team isn’t directly connected with business impacts or you can’t quantify those impacts, do this ASAP: Evaluate the projects that are in progress and on the roadmap. Do any of them touch revenue-generating products or core business functions? Implement a value-based prioritization model. Shelve anything that doesn’t have a quantifiable impact. Promote projects that touch KPIs that C-level leaders are paying attention to. Put projects that could generate revenue at the front of the line. Advertise what you’re doing and why to the C-Suite. Don’t be bashful because this could save your team from the chopping block. No one will be upset at you for reprioritizing based on value and impact. Reskill team members with domain expertise by embedding them with product teams and high-profile business units. Create shifts of one week embedded and three weeks with the data and AI team. Rotate team members so everyone gets exposure. If you can, bring in a product manager to help work with external teams, rebuild the roadmap, and implement value-based prioritization. If you can’t, take on as much of that as you’re capable of. When you run into gaps, advertise the impact of not having someone in the value management role so you can get help. Doing two jobs isn’t sustainable for very long. Finally, don’t wait for the right time or for executive leaders to be more receptive. Every team that doesn’t contribute to the top and bottom line is under scrutiny and at risk.

Most enterprises waste millions on tech without seeing real impact. I learned this the hard way. Early in my career, I saw companies invest in cutting edge tools only to struggle with adoption, integration, and ROI. That’s when I developed a smarter, outcome-driven approach. Here’s the exact method I use to maximize ROI from technology investments:  Start with Business Outcomes, Not Features ↳ Define the measurable impact before picking the tech. What problem are you solving? What KPIs will prove success?  Ensure Alignment Across Teams ↳ IT, finance, and business leaders must be on the same page. Misalignment leads to wasted budgets and underutilized tools.  Adopt in Phases, Not All at Once ↳ Test, refine, and scale. A phased rollout prevents disruptions and maximizes adoption.  Measure, Optimize, Repeat ↳ Regularly assess ROI. What’s working? What needs adjustment? Continuous refinement drives long-term value. Tech alone doesn’t drive transformation—strategy does. How do you ensure your technology investments deliver real business impact? Let’s discuss. 👇 🔹 Follow me for more insights on digital transformation. 🔹 Connect with me to explore strategies that drive real impact. ♻️ Repost this to help your network. P.S.: Thinking about how to maximize your tech investments? Let’s chat. I’m happy to share insights on what works (and what to avoid).

Where do I start? This is arguably the question I’ve been asked the most by data leaders tasked with a large scale transformation initiative. The transformation could be a cloud migration, an ERP consolidation, or any large data-centric replatforming that involves a complex web of people, process, and technology. Quite often, many leaders have convinced themselves, or have been guided by a consultant, that taking a ‘bottoms up’ approach that starts with with an inventory of the data, often along with some form of a maturity assessment, is the right way to go. It’s not. The right way to go is to take an outcome-driven approach where you are rabidly focused on solving a very limited number of business problems. Each problem would have a well defined and limited scope, and would be accompanied by a business case where the financial benefits of that initiative are quantified, and aligned upon by your customers. Instead of focusing on all data, you’ll instead inventory, observe, govern, steward, master and integrate only the data needed to solve your immediate problem. Yes, some idea of the ‘future state’ must be defined and you need to ensure you’re building out an architecture that is scalable and flexible, but complete clarity on all aspects of every individual deliverable between now and that future state do not need to be defined. If you focus each of your phases around solving specifc problems, you will build the momentum and business support you need to get more funding, and slowly grow the program over time. Instead of taking a ‘framework driven’ approach that ensures your customers will have to wait 18+ months to see any value, your customers will get benefits now. Don’t be foooled into thinking that you need to catalog and govern everything in order to transform your data estate. You don’t. Focus on solving business problems and in time, you’ll catalog and govern what matters the most. What do you think? If you have different ideas on where to start, I would love to hear them? #cdo #datagovernance #datamanagement