I had a recent conversation with an executive at one of the world's largest semiconductor companies. He expressed genuine frustration and confusion about how to approach AI in his organization.
Like many leaders, he felt overwhelmed by the sheer volume of AI solutions available. But upon closer discussion, the real issue wasn't about selecting the right technology, it stemmed from not knowing precisely what problems they needed AI to solve.
The root issue? Visibility.
Without clear, accurate data about daily operations, leaders end up guessing, attempting to force trendy AI tools into unknown gaps.
Drawing from my experience leading operational excellence initiatives in corporate finance and accounting for nearly two decades, I've found that gaining full operational visibility is foundational. It transforms guesswork into informed decisions and makes AI strategy practical rather than theoretical.
My practical recommendation for leaders feeling lost with AI is straightforward:
Gain Visibility: Clearly understand your team's tasks and resource allocation.
Start simply: Have your team manually track tasks in an Excel sheet for a defined period (2-4 weeks) to quickly identify resource-intensive tasks.
Categorize tasks clearly (e.g., Recurring, Ad hoc, Growth-focused) to distinguish business-critical activities from disruptions.
Consider automated workforce visibility platforms (like CuroWork) for ongoing, real-time insights, removing the burden of manual data entry.
Analyze the Data: Identify and address inefficiencies clearly and systematically.
Prioritize quick wins by eliminating unnecessary reports, redundant tasks, and unproductive meetings.
Use real-time dashboards to visualize workload distribution and productivity trends, enabling quick decision-making.
Match Solutions to Problems: Select AI or other technologies specifically tailored to identified business problems.
Create a targeted shortlist of AI solutions based on specific, data-validated inefficiencies (e.g., automating repetitive month-end close activities).
Pilot potential AI solutions in a controlled, measurable environment to confirm effectiveness before full deployment.
Drawing from my nearly two decades of experience in corporate finance and operational excellence, I've consistently seen how achieving operational visibility first transforms guesswork into strategic action, and in current times, making AI adoption genuinely impactful.
For example, our Workflow Visibility Report (attached screenshot) clearly highlighted 'Month-End Close' consuming 47.2% of the finance team's time, and 'General Communications' accounting for another 26%, pinpointing prime areas for efficiency gains.
AI isn't about adopting technology for technology's sake, it's about solving clearly understood business challenges. Visibility first, then technology.
I'd love to hear from you: How have you approached your AI strategy? What's working or not for your teams?