Examining the Causes of AI Project Failure at Scale and the Importance of Delivery Maturity: Insights from Viacheslav Latypov
Even with major investments and fast progress in artificial intelligence, many AI projects never move past the pilot stage. Studies show that organizational barriers, not technology, are usually the main reason.
McKinsey’s recent research shows that many AI programs do not deliver real business value because of weak execution, unclear ownership, and poor operational readiness. Gartner also finds that most AI failures happen when trying to fit models into business processes, not during development. While these are immediate problems, the root causes often come from deep-seated structures that resist change and make accountability hard. Solving these core issues helps organizations improve delivery maturity, which is key for successful AI projects.
For example, when rolling out an AI credit engine, it’s important to handle uncertainty, scale carefully, and keep operations under control. These skills help AI projects launch well and keep delivering results over time.
When AI Exposes Delivery Weaknesses
AI development is different from traditional software projects because uncertainty continues even in the later stages. This is often caused by things like changes in customer data each month or new regulations. As data and assumptions change, models need regular updates. Organizations with strict delivery systems often can’t adapt, and those without good governance struggle to grow.
Viacheslav Latypov, a Senior Project and Program Manager, told the editorial team that AI itself rarely causes delivery problems.
“AI doesn’t introduce chaos,” he explains. “It reveals where decision-making, ownership, and accountability were never clearly defined in the first place.”
This view matches what the World Economic Forum and others have found: AI maturity depends more on the organization’s systems than on the algorithms themselves.
From Engineering Logic to Delivery Architecture
Latypov began his career as an engineer, working on complex systems with explicit reliability requirements and constraints. As project scopes expanded around 2014, his responsibilities grew to include coordination, prioritization, and cross-functional decision-making. This transition led to a key professional insight: while technical systems often perform as designed, projects fail when organizations cannot make consistent decisions under uncertainty. This realization prompted a shift toward program and portfolio leadership, with a greater focus on delivery architecture, challenging assumptions, and evaluating risk at both project and portfolio levels.
Delivery Systems Must Fit Context, Not Ideology
Throughout his career, Latypov has worked in Canada and Russia, managing international programs across North America, Europe, and Asia. Exposure to diverse regulatory environments and organizational cultures reinforced the principle that delivery systems should be tailored to specific contexts, challenging the applicability of many popular frameworks. According to Latypov, Agile, Waterfall, and hybrid models are tools rather than comprehensive solutions. “The biggest mistake in AI delivery is copying what worked somewhere else,” he says. “What matters is whether your delivery system matches the uncertainty, constraints, and maturity of your organization.”
This practical approach is especially important in AI projects, where you need to balance trying new things with keeping operations disciplined.
Latypov has put this philosophy into practice in both startups and large companies. At Hercules Dynamics, he set up PMO capabilities in a growing company where projects had already started, but there was little visibility at the portfolio level.
Instead of adding heavy processes, the focus was on clear decisions, standard reporting, and open risk management. This helped leaders set priorities and keep five programs on track, leading to early delivery of a key project even with limited resources.
Earlier, at Imagine Communications’ EPMO, Latypov worked to improve delivery across several projects. Change management was key, leading to a 27% boost in productivity and less risk at the portfolio level. Compliance needs like SOC 1 and SOC 2 were built into daily work instead of being treated as outside requirements.
What Successful AI Delivery Has in Common
Latypov has seen the same pattern in his work with companies like SkyHive, Bicom Systems, Dahua Technology, and Elics Group.
AI projects that make it to production usually have three things in common:
- Clear ownership at the program level
- Delivery structures built to handle uncertainty
- Governance is integrated within execution processes rather than being added as a separate layer.
This approach aligns with industry-wide research, which suggests that AI success depends more on operational maturity than on technical sophistication.
Limits, Trade-offs, and Reality
Even with professional delivery leadership, AI projects remain inherently complex. Latypov emphasizes that no governance model can fully eliminate risk. Organizations must balance flexibility to adapt to evolving challenges with enough control to ensure consistency and reliability. The goal is to make trade-offs explicit and manageable. “You’re always balancing flexibility against control,” he says. “The problem starts when organizations pretend they can have unlimited experimentation and perfect predictability at the same time.” Recognizing these limitations, rather than obscuring them with process, often distinguishes sustainable AI programs from stalled initiatives.
Delivery as a Strategic Discipline
As more industries adopt AI, delivery is now seen as strategic infrastructure, not just an administrative task. Being able to turn uncertainty into action, without oversimplifying, is now a key skill for organizations working with AI.
Latypov’s experience shows this shift in action. His work highlights how delivery maturity is becoming a top factor for success in AI transformation.
Why This Matters Now
As organizations keep investing in AI, the main question is moving from whether the technology works to whether the organization is ready. Without mature delivery systems, even the best AI projects may stay as isolated experiments.
The growing focus on delivery maturity shows that AI’s future will depend not just on innovation, but also on the systems that help it grow.
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