Time to Market: Why Leadership, Execution, and Operational Alignment Matter More Than Technology

Sriram Upadhyayula, Chief Information & Technology Officer at Propelis and President of 5Flow, explains why speed failures are rarely technical and how CIOs can accelerate delivery.

Over the last few years, many organizations have invested heavily in AI, automation, and digital platforms, expecting these technologies to reduce time to market drastically. Yet speed hasn't improved the way they expected.

The issue isn't the technology itself: most systems today are sophisticated enough to deliver on their promises. The problem is that investment in technology has outpaced investment in organizational alignment—getting people, processes, and systems working in sync toward shared goals. If speed is the goal, we need to examine the structures, handoffs, and decisions that actually determine how work moves through a business. And that starts with IT leadership. CIOs need to move beyond simply delivering technology and focus on orchestrating how work flows across the organization—aligning people, processes, and systems so decisions can happen faster and closer to where the work gets done.

Speedbumps: The Usual Suspects

The slowdowns in time to market happen in predictable places. In my experience across CPG and other complex, compliance-heavy industries, the initial work—whether it's product development, campaign creation, or solution design—often moves quickly and is rarely the issue. What slows everything down is what comes after: handovers, approval cycles, compliance reviews, and the back-and-forth before anything goes live. That's the first pinch point.

The second comes down to teams working in silos. One team manages work in a project platform, another tracks progress in a separate system, while approvals happen in spreadsheets 
or email. A single change can touch multiple disconnected tools before it moves forward. Even when organizations talk about having an end-to-end process—one that can take a product from concept through compliance to market launch without manual handoffs—the reality is often that different teams use different tools that don't communicate. This isn't just anecdotal: MuleSoft's 2025 Connectivity Benchmark Report found that organizations use an average of 897 apps, but only 29% are integrated. That fragmentation means people are constantly context-switching, re-entering information, or translating between platforms. It's not that any of these systems are failing; it's that they're not connected in a way that supports the efficient flow of work.

Another common pattern is teams introducing automation or AI into one part of a multi-step process without thinking through what happens downstream. If one team can now produce ten times the output but the next step is still a three-day manual review, you haven't accelerated time to market—you've just moved the bottleneck. Worse, you may have created a new one: the downstream team now has ten times the work with the same capacity.

What High-Velocity Organizations Do Differently

Companies with strong data cultures—where data flows freely across teams and decisions are grounded in shared, accessible information—make decisions five times faster than their peers, according to Hydrogen BI's 2025 research. But data integration alone isn't the differentiator; it's how the organization is wired to act on it. The patterns that separate fast movers from the rest have less to do with tech stacks than with how workflows are designed and who's empowered to keep things moving.

A handful of practices show up consistently:

• They take an end-to-end view. When a step is automated, the downstream impact is considered and the process adjusted accordingly. Teams operate under a single, shared timeline rather than separate, disconnected ones.

• They reject the speed-versus-governance tradeoff. Compliance and technology teams often operate in silos. When leadership brings them together—when compliance understands why automation can reduce risk and technology understands what compliance is protecting—they can partner on solutions that are both fast and safe.

• They decentralize decision-making. Speed breaks down when every step requires multiple approvals and moves up a hierarchy. High-velocity organizations design oversight differently, so approvals don't get stuck with a single gatekeeper.

• They're further along with AI. Most companies today are still in a human-led, AI-assisted model. The faster ones are moving toward AI-led, human-governed. That's a fundamental shift, and the organizations that get there sooner will have a significant advantage.

• They fix problems instead of endlessly analyzing them. I see companies invest heavily in dashboards and reports—dozens of ways to visualize the same issue. But action doesn't follow. Too much time is spent understanding the problem, and not enough on solving it.

In my experience with large global organizations, applying these principles makes a measurable difference. In one case, redesigning approval flows and connecting previously siloed teams reduced the time to move a product from final design to market launch from several weeks to just a few days. The technology already existed—the real change came from aligning the process and decision structure around it.

The CIO's Evolving Mandate

If speed is primarily a leadership issue rather than a technology one, the CIO's role has to evolve accordingly.

One of the biggest responsibilities we have now is managing expectations. Everyone has a point of view on what AI can do, what should be automated, and what's possible. Part of our role is grounding those conversations—making clear what's secure, what can actually be done, and what will help in the short term versus the long term.

Another key responsibility is enablement. If an organization is going to move from human-led, AI-assisted work to AI-led, human-governed work, that doesn't happen on its own. It's a journey. I think about it in four steps:

1. Familiarity: Getting people comfortable with AI—perhaps by giving them access to tools like Copilot—to build a baseline understanding.

2. Integration: Embedding AI into workflows, so it becomes part of how people work rather than a separate tool.

3. Transition: AI begins driving processes, with humans providing oversight and handling exceptions.

4. Full automation: AI handles nearly everything, with humans intervening only when necessary.

Most organizations are still somewhere between steps one and two. Moving through that progression requires the CIO to own the journey and help the organization progress along the right path. In practice, that means working on both fronts at once: introducing AI capabilities while also redesigning how work moves across the organization so those capabilities can actually deliver value.

Starting small with tools like Copilot may seem incremental, but it often reveals the deeper operational issues that slow organizations down. When teams begin using AI in their daily work, they quickly see where processes are fragmented, where approvals stall, or where different teams rely on disconnected systems. That visibility becomes the catalyst for larger organizational changes—connecting end-to-end workflows, clarifying decision ownership, and breaking down silos between teams.

In one large global organization I worked with, early AI pilots started with simple productivity tools but quickly exposed how different teams were operating in separate systems with little visibility into each other's work. By aligning those teams around a shared workflow and integrating the tools that support that process, the organization moved work through the pipeline far more quickly—significantly reducing delays that had previously stretched over weeks.

Automation and AI will keep getting better, but they won't deliver speed unless the processes, handoffs, and decision-making around them change too. The CIOs who figure this out won't just be deploying technology faster. They'll be the ones who finally close the gap between what the tech can do and what the organization can deliver.