Nat Natarajan, Chief Operations Officer, G-P.
Look around the corporate world right now, and you’ll see a massive disconnect. We have some of the most advanced technology in human history at our fingertips, yet the average enterprise is practically tearing its hair out trying to get value out of it.
My company recently surveyed 2,850 executives with a minimum seniority of vice president to understand why so many organizations are hitting this friction point and to give business leaders a clear roadmap forward. The findings reveal a crucial turning point: Organizations are shifting from blind adoption and experimentation to intentional pressure-testing, demanding technology that drives modern, scalable growth.
The takeaway isn’t that AI is falling short; rather, it’s a transformative technology that is exposing where our business models are overdue for an upgrade.
Think about standard global HR operations: layering an AI assistant onto a legacy hiring process or onboarding playbook doesn’t magically build a dynamic global team; it automates yesterday’s compliance bottlenecks at 10 times speed.
But when you modernize the underlying model first, AI can become the ultimate accelerator for borderless growth and human potential.
The Innovation Deficit
This operational friction explains a key trend in our report: The share of global executives who say their organizations are aggressively using AI to innovate has dropped nearly 20 points since the previous year.
I don’t think this signals a loss of faith in AI’s transformative power. However, in the rush to adopt AI, a lot of companies only bolted it onto existing, messy workflows instead of rethinking how the work should actually be done.
This disconnected approach that concentrated on individual tasks rather than broader workplace systems might be one of the main reasons that only about one in 10 employees in AI-adopting organizations strongly agree that artificial intelligence has transformed how work gets done.
Consider this: According to the survey my company conducted, nearly 70% of global executives say the time their employees spend reviewing and fixing AI-generated work has gone up this year.
Again, this is a structural problem. When AI tools are fed misaligned data or context, your highest-paid, most strategic minds are acting as spell-checkers for a chatbot. You hired them for their judgment, empathy and vision, but right now, they are stuck in a frustrating loop of prompting and fixing.
Every hour an expert spends correcting an AI hallucination is an hour they aren’t using to solve a real business problem. I call this the “monitoring tax,” and it’s a silent killer of ROI. AI is built to liberate human potential, not burden it.
The Budgetary Ultimatum
This is why the boardroom conversation has changed. For a while, you could put AI in a budget request, and it would get approved almost instantly. Not anymore.
Boards are done with vague promises of increased efficiency; they want to see proof on the P&L. Our research found that nearly seven in 10 executives say they’re ready to scale back their AI budgets if they don’t see clear profit goals met this year.
Retreating isn’t the solution. You don’t need to slash the budget, but you do need to up the ante. I don’t mean throwing more money at ad hoc tools or just using AI for AI’s sake. I mean doing the hard, grinding operational work of completely rethinking how your business runs so the technology can actually do its job and perform at its full potential.
We have to transition from simply experimenting with AI to truly executing with it.
Moving Beyond The ‘Broken Model’
But how?
To start, we need to move away from isolated conversational tools that require constant hand-holding. The future of AI isn’t about giving everyone a digital assistant to help write emails 10% faster. It’s about agentic architecture and designing system-level workflows where AI can actually execute complex, end-to-end business processes on their own, safely and within clear corporate guardrails.
But you can’t deploy that kind of advanced capability onto a messy foundation. You have to re-architect the work itself first. That comes down to three things:
• Stop automating your current mess. If you automate a broken process, you just get a faster version of a mistake. Before you apply AI, you have to clean up the underlying workflow. Frankly, the goal should be to remove some of the work entirely. If AI isn’t deleting unnecessary tasks from your plate, you’re using it wrong.
• Demand outcomes, not activity. We need to completely change how we buy technology. Stop buying software licenses just to show an adoption metric or a high usage chart. Look for solution-driven tools that target specific, high-value business bottlenecks.
• Insist on enterprise-grade guardrails. Because we are moving toward systems that can execute tasks more autonomously, you have to design for compliance, legal indemnification and verified data sources from day one. You can’t have true innovation without absolute trust in the architecture.
The Path Forward
It’s easy to get caught up in tech hype cycles and the constant friction of the quickly accelerating market. Major research institutes are warning that responsible AI practices aren’t keeping up with technological advancements, while enterprise leaders across the country are speaking out about frontier labs overselling model capabilities.
Slowing down isn’t a viable strategy, but neither is blindly charging ahead without a plan. This moment is forcing us to grow up. It’s pressuring us to stop viewing AI as a magic plug-and-play trick and start viewing it as a blueprint for a modern, resilient business model.
Having the biggest budget isn’t the solution, but neither is waiting around for the world to hit a pause button. Instead, leaders should have the operational courage to look at their own broken legacy structures and finally tear them down.
AI can optimize a workflow, it can write code and it can execute complex tasks at lightning speed. But it cannot fix an outdated organizational design. That part is entirely on us. This is the ultimate test of leadership execution.
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