10 hours ago
The CIO's Guide To Leading The AI Productivity Shift
Ashwin Ballal is the Chief Information Officer at Freshworks.
The AI productivity boom is here. It's moving fast, leaving the unprepared behind. The chance to transform work is huge, as is the responsibility.
Business technology is undergoing a historic reset. AI is no longer a futuristic promise—it's reshaping how we work today. For CIOs, this means transitioning from operational managers to transformation drivers. According to recent McKinsey research, generative AI could add up to $4.4 trillion annually to the global economy by 2030 through productivity enhancements.
That scale of the potential is exciting—but only if it's grounded in reality. I didn't start in tech. I've worked in sales, marketing, engineering and general management. That journey taught me that technology only matters if it drives outcomes and empowers people. Without alignment with business goals, new tech often creates more headaches than solutions. Today, that's clearer than ever, as businesses struggle under the weight of costly, complex "solutions" that promise growth and then deliver the opposite.
Here's how to break free from that cycle and lead with impact:
AI is often seen as a threat to employment, but the reality is far more optimistic. Rather than replacing roles, CIOs should focus on evolving them by taking the following actions:
• Identify tasks that can be enhanced by AI across departments, not jobs to be replaced.
• Start with repetitive, time-consuming processes.
• Set clear productivity metrics before and after implementation.
At my organization, we've seen a meaningful impact across teams by integrating AI into our workflows. Engineers are delivering more efficiently with AI-assisted coding. Our marketing team is creating more content without compromising quality. And our customer success team is resolving issues faster thanks to smarter knowledge retrieval.
AI is also creating demand for new skills, especially for people who understand both business and AI applications. According to the U.S. Bureau of Labor Statistics' Occupational Outlook Handbook, data scientist and AI specialist roles are projected to grow at 15% through 2032, much faster than average.
This marks a shift from automation for cost savings to automation for capacity expansion. When teams are freed from routine tasks, they focus on strategy, innovation and customer impact.
Empathy is a strategic differentiator. CIOs must understand how people interact with tools, not just whether the tools work. Implement these practices to build more human-centered solutions:
• Shadow employees to spot friction points.
• Measure experience metrics alongside technical ones.
• Pilot new tools with real users before rolling them out.
For example, when my team redesigned our internal knowledge management system, we focused on user experience, not just functionality. The result? A reduction of nearly half the time spent searching for information, leading to faster customer resolutions and better decisions.
Empathy helps prioritize AI investments that reduce complexity and drive real value. Tools should simplify work, not add new layers of frustration.
The biggest threat to AI success isn't a lack of technology—it's too much of it. The following three actions should assist you in identifying where you need new tools and where you need to streamline:
• Audit tools enterprise-wide to identify redundancies.
• Adopt a "one in, one out" policy for tech adoption.
• Choose platforms that integrate and scale seamlessly.
Many solutions are just a force-fit of features you'll never use, adding complexity that slows you down. AI thrives in streamlined environments. Simplify CRM, billing and ERP—core workflows where impact multiplies. Don't confuse more features with more value.
Software is a choice that can make or break a business. The challenge is choosing platforms that solve real problems without creating new ones.
AI's value depends on adoption—and adoption depends on confidence. Consider these empowering steps to ensure you're implementing value in alignment with business goals:
• Deliver role-specific training for every function.
• Empower AI champions across departments.
• Create clear career paths that reward AI fluency.
Most employees know AI matters but don't yet feel equipped. Research by PwC found that 77% of executives cite skill gaps as the top challenge to AI adoption. Upskilling isn't just technical—it's about mindset. When people understand how AI enhances their role, they stop resisting change and start driving it.
Constraints aren't barriers—they're catalysts. To make the most of limited resources, consider these steps:
• Create an AI value scorecard to prioritize use cases.
• Sunset the lowest-performing 20% of your tech stack.
• Reinvest savings in high-ROI, organization-wide initiatives.
Transformation at scale doesn't come from spreading resources thin. It comes from doubling down where AI adds measurable value. For example, consolidating analytics platforms into one AI-powered solution can cut costs while expanding insights across teams.
The hard truth? Many organizations are still stuck in AI pilot mode. A recent Accenture study found that two-thirds of companies haven't scaled past the experimentation phase. The ones who will succeed are those who simplify, empower and lead with purpose.
The future of CIO leadership isn't defined by how much AI we implement—it's defined by how well we enable people to thrive with it.
Real transformation happens when systems elevate human potential, drive cross-functional impact and turn IT from a cost center into a growth catalyst.
The productivity shift is here. The question is: Are you building for speed or building for scale?
The time for strategy is now—the time for implementation was yesterday.
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