AI agents are moving from hype to reality. With that, more learnings are shared and myths are busted. As we’ve seen in previous hypes, AI success requires clarity and culture, not slogans. Successful AI agent adoption is less about protocols and more about safe experimentation, and ultimately, AI readiness creates a real competitive edge before getting to an “AI first” mindset.
Building on our previous recap of the year’s first-half vendor conferences, Jon Reed, Industry Analyst and Co-Founder of diginomica, and I recently discussed the most common myths surrounding Agentic AI implementation, and what leaders need to do right now.
Beating “AI First” Mantras with Clarity and Vision
One of the biggest traps organizations fall into is declaring themselves “AI first.” It sounds bold. It looks great in a board deck. But it often confuses employees and creates pressure without direction. People don’t know what “AI first” means for their day-to-day work. Worse, it can fuel fear—does “AI first” mean fewer jobs? Am I being replaced? What leaders should be doing instead is focusing on clarity: what kind of AI company do we want to be? Do we want to be known for the best customer service, the fastest supply chain, or the most personalized products? Once that is defined, AI becomes a tool to enable that vision. But it’s not the vision itself.
That’s where AI readiness comes in. Readiness isn’t about mastering every model. It’s about being clear on your business strategy, knowing what role AI can play, and building a culture that feels empowered to experiment. Without that clarity, the AI-first mantra becomes noise. With it, AI becomes a way to scale what you already do best.
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Employees are already using AI. Studies from both Slack and Duke University showed that nearly half of workers don’t tell their managers because they’re afraid of being seen as lazy or incompetent. Welcome to the era of “shadow AI.” The solution is providing safe experimentation. Employees need sanctioned, secure sandboxes where they can try tools, access company data responsibly, and share wins without fear. Without this, people will keep using consumer tools outside of IT’s control, creating risks around privacy, IP, and compliance.
» Provide a sandbox environment that creates a culture of innovation where employees can propose new uses of the technology that are validated and secure within your enterprise structure. «
— Jon Reed
Sandboxes also shift the narrative from intimidation (“We’re AI first, use these tools or else”) to inspiration (“Here’s how you can use AI to make your work easier and more impactful”). Recognition programs can reward teams that discover creative workflows, sparking momentum across the company. Adoption doesn’t come from mandates. It comes from trust, freedom to experiment, and proof that leadership values responsible use. When companies build that foundation, they not only reduce shadow AI but also unlock real enterprise value.
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Vendors are touting protocols like MCP or A2A. These are important developments, but most companies aren’t there yet. Before you worry about multi-agent orchestration across vendors, focus on being AI-ready within your company first.
AI readiness has two pillars: data and people. On the data side, start small. Identify areas where you already have clean, accessible data and build pilots around them. Don’t waste time on “throwaway POCs.” Go live with contained pilots that deliver real results, then expand. On the people side, focus on confidence and culture. Give employees tools, guidance, and freedom to experiment without fear of replacement.
For smaller organizations, the advantage comes from choosing partners wisely. Don’t try to build everything from scratch. Lean on vendors with deep pockets to manage the shifting architectures while you focus on business outcomes. For larger enterprises, readiness means aligning pilots with high-value goals and measuring success continuously. This goes beyond mere productivity gains and results in increased employee engagement and customer satisfaction. Although AI readiness is less flashy than “AI first,” it is far more sustainable. Companies that invest in people, data, and safe experimentation now will be the ones ready to scale responsibly when protocols and architectures mature.
Summary
So, where are we with AI agents? Clarity matters more than slogans. Define the company you want to be, then use AI to get there. Safe sandboxes reduce shadow AI and unlock adoption by making experimentation secure and rewarding. Approach AI readiness as the real advantage.
As you head into your own AI journey, ask yourself: Do our people feel empowered? Do we have the data to back our ambitions? Are we treating AI as a tool to serve our strategy (not the other way around)? If you develop answers to those questions, and you’ll be far ahead of those chasing mantras without meaning.
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