Ashish Bhutiani

Before organizations rethink their structure around AI, leaders often need to rethink how they operate themselves. The biggest changes usually start with decision making, priorities and personal habits before they show up across the company. forbes.com/sites/johnwins…

Most organizations are no longer debating whether to modernize. The real question is how to do it without creating new problems along the way. Cloud migration is often treated as a technical milestone. In practice, it’s a business decision with real cost, risk, and operational implications. When migrations are rushed, especially during data center exits, the default tends to be lift-and-shift. The issue is that it often carries forward the same inefficiencies, just in a new environment. A more deliberate approach tends to play out differently. Phased migration and sequencing allow teams to consolidate workloads, reduce redundant infrastructure, and uncover gaps in resilience or recovery that would otherwise go unnoticed. As environments simplify, the day-to-day load on teams changes. Less time goes into maintenance, patching, and reactive work. More time opens up for initiatives that actually move the business forward. Hybrid models are also becoming a practical choice. Keeping critical systems stable while moving the right workloads to the cloud creates a balance between performance, compliance, and flexibility instead of forcing a uniform approach. Modernization works when it’s anchored in how the business operates and creates value. Otherwise, it’s just infrastructure moving from one place to another.

The pace of threats feels different right now. What used to take time can happen almost instantly. Identity becomes central as systems start acting across workflows. This takes ongoing discipline, not a one time solution. Full conversation on UNCAGED: open.spotify.com/episode/0aJLuR…

Organizations often invest in technology before aligning how they operate around it. Issues tend to show up later, when the cost is higher. Real progress comes from maturing alongside the technology. Watch the full conversation on UNCAGED: open.spotify.com/episode/0aJLuR…

Organizations learn the most from AI when they use it inside their own operations first. Internal experience changes how teams think about adoption, value and customer success.

Security tends to become a priority after something goes wrong. Buying tools or checking compliance boxes is only the starting point. The real work is how organizations mature alongside the technology and anticipate risk before it becomes a problem. My full conversation on UNCAGED: open.spotify.com/episode/0aJLuR…

Cloud modernization is advancing, but utilization patterns are barely changing. New platforms create more capability, yet old assumptions around provisioning and risk tolerance often stay in place. The organizations getting real value are revisiting how decisions are made, not only the technology stack. cio.com/article/416665…

Our boba shop near UNLV is getting close. Final inspections are in progress and aiming for a soft opening later this month. Building from the ground up takes patience, but it is coming together.

Leadership feels less about polish and more about clarity and being real. People respond to leaders who communicate with intention and show up as themselves.

Security can't stay a checkpoint as systems move faster and become more dynamic. It has to be built into how teams design and operate from the start so it becomes part of the workflow, not a separate step. The real change is moving from approval-based security to systems that guide decisions in real time without slowing teams down. Full conversation on UNCAGED: open.spotify.com/episode/0aJLuR…

The idea of a future-proof job is changing. More people are choosing to build something of their own and develop skills that compound over time. Ownership and adaptability are becoming the advantage.

Franchise support sounds great in a pitch, but the real test is how it holds up under pressure. Consistency, structure and accountability make the difference once the business is live. entrepreneur.com/franchises/how…

88% of organizations utilize AI in at least one business function, but only one-third have scaled it across the enterprise. While many struggle to see ROI, AI-enabled ERP systems are emerging as a key area for deriving value. Unlike traditional ERP systems, which treated all users the same, modern AI-enabled ERP adapts to individual needs by understanding context and behavior. To maximize value, organizations must maintain a clean data foundation across systems, ensuring ERP serves as a reliable source of truth. diginomica.com/next-evolution…

Sometimes leaders make an important decision, only to later receive new information that complicates it. Even when leaders know they've made a mistake, they still hesitate to change their position, fearing it will make them look indecisive or lacking authority. Yet when a leader refuses to change course in the face of new information, it can cost them credibility in their team's eyes. The important thing to remember is that there's a difference between indecision and confident humility. An indecisive leader lacks conviction, while a confident and humble leader simply remains open to new evidence, particularly when their current model of the situation is incomplete. So long as you're transparent about what you believed, what changed, and what the direction forward looks like, you'll maintain your sense of authority and your team's trust.

Agentic AI is transforming how businesses operate, so IT leaders need to see hiring, skill building, and vendor choices as connected decisions. These levers are working together now: - Hiring decides where human judgment is in the company. - Building skills helps AI enhance that judgment. - Partnering with vendors makes sure the productivity boosts benefit the whole organization. The choices we make influence the tech economy in an AI-first organization. CIOs need to take charge, adjusting their teams to drive value. mckinsey.com/capabilities/m…

A lot of organizations judge leadership potential by obvious traits like confidence or charm. But they often mess up by promoting the people who seem like leaders instead of those who actually have the skills to lead. Some of this stems from a lack of information. Bosses will not always have the right information to predict a candidate’s leadership ability, so they fall back on what's more readily apparent. Yet just because someone shows self-confidence and authority while communicating doesn't make them a good leader. Confidence and visibility matter, but more important are traits like emotional intelligence, sound judgement, and the ability to develop others. theconversation.com/why-organisati…

We're launching an ice cream truck business in Las Vegas and spent weeks on route optimization, pricing models, demand patterns by neighborhood and time of day. Solid planning and we felt prepared. Then we got out there and learned that the route I'd modeled as our best performer was dead because there's no shade and nobody wants to stand in 105 degrees waiting for an ice cream sandwich. Meanwhile a parking lot near a soccer complex I hadn't considered, turned into our highest volume stop within a week. I've watched the same thing happen with enterprise programs I've been a part of. The plan looks airtight until it hits real customers, real usage patterns, real objections that nobody in the planning room anticipated. The teams I've seen move fastest aren't the ones with the most thorough plans. They're the ones that got a version in front of people early enough to learn something they couldn't have predicted. This isn't an argument against planning. I still think the route modeling mattered because it gave us a starting point worth testing, but some things about your business only become visible once you're operating, and treating the plan as a hypothesis rather than a blueprint changes how quickly you adapt when the data doesn't match.

#AI is starting to change something more fundamental than productivity. It’s changing how much work can be done without adding more people. And once that happens, the way organizations are structured starts to come into question. For years, scale meant headcount. More growth meant more layers, more coordination, more process. That relationship is beginning to shift. We’re already seeing individuals and small teams handle work that used to require larger groups. The work hasn’t gone away. It’s being compressed. Which makes this less about tools and more about how companies are designed. The harder question for leaders isn’t how to make the current model more efficient. It’s whether we would build it the same way today. The organizations that take that question seriously will likely operate very differently from what we’re used to. #Leadership #ChangeManagement

As AI agents start participating in customer journeys, some of our assumptions around identity are starting to break. For a long time, identity systems were designed around a simple model: a human interacting with a system. That model is changing. AI agents are beginning to act on behalf of customers across payments, service interactions, and account access. And with that, the line between human and machine activity is becoming harder to distinguish. There’s also a broader question emerging. If an #AI agent initiates a transaction, how do we think about authorization? What does trust look like in that interaction? And where does accountability sit? This is less about fraud tools and more about how we define identity in a system where humans are no longer the only actors. And that shift will require a more deliberate approach to trust, governance, and how customer interactions are designed going forward. cxtoday.com/security-priva…

#Security used to be about protecting systems. More and more, it’s about protecting identities. Recent research from PwC and Cloudflare highlights a shift I’ve been seeing as well. Instead of breaking in, attackers are logging in, using stolen credentials, impersonation, or social engineering to get access without raising obvious flags. That changes the nature of the problem. These aren’t always loud attacks. They rely on trust, and they’re getting harder to spot. #AI is making that easier on the attacker side. Messages look more convincing. Interactions feel more real. The margin for error is smaller. There’s another layer to this. Identities today aren’t just people. Service accounts, bots, and agents are growing quickly, but most organizations still manage access as if humans are the primary users. In my view, how access is granted, how it’s monitored, and how quickly it can be contained when something goes wrong: those are leadership decisions now, not just technical ones. cybersecuritydive.com/news/identity-…