Traditional project management is a liability, says VP of Strategy & Project Management
Fola Alabi, VP of Strategy and Project Management at Strategic Project Leader Consulting, shares how AI is fundamentally reshaping project delivery — exposing weak strategies and accelerating decision-making.
Expert Insights: Fola Alabi leverages AI to transform project strategy and delivery for measurable executive outcomes.
AI Advances: AI reshapes project delivery roles by shifting focus from task coordination to decision-making architecture.
Strategic Intelligence: Strategic Project Intelligence™ reframes project success from timelines to delivering right outcomes through AI.
Role Evolution: Project managers evolve into strategic translators, enhancing value through intelligent AI-driven insights.
Integration Focus: Maximizing existing tools with AI integration optimizes decision-making and enhances project management efficacy.
Fola Alabi is an expert in project delivery. She's the VP of Strategy and Project Management at Strategic Project Leader Consulting, where she's closing the gap between strategy and real value. She has supported multi-billion-dollar portfolios and helped organizations shift from managing projects to delivering measurable outcomes.
We sat down with Fola to learn more about how she's evolving her approach to project delivery with AI. Here's what she had to say.
How strategic project intelligence enhances delivery success
I operate at the intersection of strategy, leadership, and project delivery. I am the VP of Strategy and Project Management at Strategic Project Leader Consulting, and my work focuses on one core problem: closing the gap between strategy and real value. Across industries, I have supported multi-billion-dollar portfolios and helped organizations shift from managing projects to delivering measurable outcomes. Today, my role is less about overseeing timelines and more about shaping how we make decisions, structure portfolios, and realize value through execution.
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I also develop frameworks like Strategic Project Intelligence™ and advise executives on how to turn project delivery into a competitive advantage, not just an operational function. I spend most of my time upstream, where strategy is shaped, because that is where most delivery failures begin.
At a practical level, my work includes:
- Translating executive strategy into executable portfolios
- Designing governance systems that drive decisions, not just reporting
- Coaching leaders and PMOs to operate as strategic partners
- Embedding AI and data into delivery models for better foresight and speed
- Tracking benefits realization, not assuming it
Why AI reshapes project delivery roles and priorities
AI is not just changing delivery; it is exposing existing weaknesses within it. The project professional's role shifts from task coordination to decision architecture. I now spend significantly less time on status reporting, manual updates, and consolidating information. AI increasingly handles these activities.
AI is not just changing delivery; it is exposing existing weaknesses within it.
I now focus on decision logic, trade-offs, scenario modeling, and ensuring alignment across portfolios. AI has accelerated access to information, but it has also raised the standard for thinking. Leaders must now interpret signals, anticipate risks, and guide direction with far greater precision. Day to day, AI enables faster synthesis of complex data, real-time insights into delivery risks, and more dynamic planning. But the real shift is this: we are no longer managing projects; we are managing decision velocity and value flow.
How AI-driven tools enhance project decision-making power
In one transformation portfolio I led, the organization experienced significant delays in multiple initiatives. On the surface, it looked like an execution problem, but deeper, we found it was a prioritization and decision problem. We integrated financial data, delivery timelines, and risk indicators into a centralized dashboard using tools like Power BI and Excel. And we added AI-driven analysis to identify patterns in projects.
We found that a substantial portion of initiatives consumed resources without delivering meaningful value. We reframed governance meetings entirely, shifting from status updates to decision-making sessions. Instead of presenting updates, we are structuring every governance session around three questions: What should we stop, what should we accelerate, and what needs to change? This required redesigning dashboards to highlight value, risk exposure, and resource allocation in real time, rather than just timelines and milestones.
Within weeks, leadership had visibility that they'd never had before. We reallocated resources to high-impact areas, significantly reduced decision latency, and improved confidence in delivery. The key insight: the issue was never effort; it was clarity. AI helped surface the patterns, but leadership unlocked the value.
The key insight: the issue was never effort; it was clarity. AI helped surface the patterns, but leadership unlocked the value.
How AI exposes weak strategies in project delivery
AI delivers immediate value in clear areas, especially in repetitive, data-heavy, and time-consuming tasks. AI can significantly reduce effort and improve accuracy in reporting, risk identification through pattern recognition, documentation, and forecasting. When implemented correctly, AI enhances visibility and speeds up insight generation.
But AI does not replace thinking; it exposes weak thinking.
When information becomes instant and abundant, it immediately reveals any lack of clarity around priorities, outcomes, or decision-making. Many organizations believed their challenge was execution, but AI revealed the real issue was ambiguity in strategy and misalignment in decision-making. AI does not fix poor strategy. It accelerates its consequences. That has been both the biggest opportunity and the biggest wake-up call.
The core of delivery still requires human judgment. Strategic prioritization, stakeholder alignment, navigating trade-offs, and leading change cannot be automated. These deeply human activities require context, emotional intelligence, and accountability. The future is not about replacing people with AI; it is about elevating people to focus on the decisions that truly matter.
Why integration over fragmentation optimizes tech stacks
Our current stack is built around integration rather than fragmentation. We use Power BI for executive-level visibility, advanced Excel models for scenario planning and value tracking, and tools like Azure DevOps or Jira for execution. We use AI tools such as ChatGPT for synthesis, analysis, and accelerating thinking, while platforms like Miro support alignment and strategy design.
One of our most underrated tools is Excel, but not in its basic form. When designed strategically, Excel becomes a powerful decision engine that can model scenarios, track value, and support portfolio optimization. When combined with AI, it becomes even more impactful. AI can generate insights and scenarios, while Excel provides the structure and discipline needed to make decisions. Many teams overlook this and chase new platforms, but the value often lies in maximizing the tools they already have.
Many teams chase new platforms. The value often lies in maximizing the tools you already have.
Why traditional project management methods are irrelevant (and what to do instead)
I am not just moving away from traditional project management methods. I am actively challenging their relevance in today’s environment and rethinking the path forward to ensure robustness in a world defined by volatility, uncertainty, complexity, AI acceleration, and persistently high project failure rates. We are not seeing incremental change; a paradigm shift is clearly required.
Traditional project management was designed for a more predictable era, one that assumed stability and slower change. It emphasizes control, reporting, and adherence to predefined plans. In today’s environment, that approach is not just outdated, it is a liability. It creates the illusion of progress while value quietly erodes beneath the surface, often unnoticed until it is too late.
I have shifted toward what I call Strategic Project Intelligence™ (SPI). SPI represents the evolution from managing projects to orchestrating value. It reframes success from being on time and on budget to delivering the right outcomes through timely, informed decisions. Planning becomes dynamic, governance becomes decision-driven, and delivery becomes intelligence-led through the integration of AI, data, and human judgment.
Most importantly, the project professional's role is now elevated. They move from task coordinators to strategic translators who connect executive intent to execution and influence value creation in real time. The result is measurable. We see greater precision, stronger alignment, and clear visibility into value, not just progress.
The reality is simple: project management fails not due to execution, but because its design never incorporated strategic thinking. SPI fills that gap. Once adopted, organizations do not revert because improvements in ROI and success rates make the old model unacceptable.
The project professional’s role is now elevated. They move from task coordinators to strategic translators who connect executive intent to execution and influence value creation in real time.
How to redesign decision-making and leave traditional project management behind
We transitioned away from traditional project management not by introducing more tools, but by redesigning decision-making and connecting tools to value. At its core, we implemented an integrated decision-making system connecting strategy, delivery, and financial outcomes in one view.
We transitioned away from traditional project management not by introducing more tools, but by redesigning decision-making and connecting tools to value.
Practically, this meant combining tools like Power BI for executive dashboards, advanced Excel models for scenario planning and value tracking, and delivery platforms such as Jira or Azure DevOps for execution visibility. On top of this, we layered AI tools to synthesize data, identify patterns, and accelerate insight generation.
We also implemented dynamic prioritization frameworks within Excel and Power BI, where we continuously assess projects against strategic alignment, expected value, risk, and resource constraints. This allows leadership to make informed trade-offs quickly, rather than waiting for quarterly reviews. We embedded AI-assisted analysis into delivery workflows. For example, we used AI to scan risk logs, identify recurring patterns across projects, and surface early warning signals typically missed in manual reviews. This enabled teams to move from reactive issue management to proactive risk mitigation.
These changes significantly impacted outcomes. In one case, an organization identified over twenty percent of initiatives consuming resources without delivering meaningful value and reallocated those resources to higher-impact areas within weeks. We shortened decision-making cycles, improved alignment across leadership, and increased overall delivery confidence.
How AI influences scope definition and team alignment
AI's real value emerges when we embed it into decision-making moments, not just task tracking. In scope definition, AI fundamentally changed how we establish clarity up front.
AI’s real value emerges when we embed it into decision-making moments, not just task tracking.
Stakeholder workshops and static requirements: Instead of relying solely on stakeholder workshops and static requirements documents, we use AI to synthesize inputs from multiple sources such as past project data, stakeholder feedback, risk logs, and even market signals. In one case, AI identified hidden dependencies and conflicting expectations early in the scoping phase that would have otherwise surfaced much later as costly change requests. This allowed us to define scope based on what would deliver value, not just what was requested, significantly reducing rework.
Team alignment: AI became a powerful tool for translating strategy into actionable plans for teams. We use it to break down high-level strategic objectives into clear, role-specific actions and to highlight misalignment across teams. In practice, leaders no longer assume alignment. They can see where interpretations differ and address them immediately. In one transformation initiative, this reduced cross-functional friction and improved delivery speed because teams no longer worked from slightly different versions of the same goal.
Validation processes: AI enables continuous validation instead of relying on periodic reviews. By integrating delivery data, performance metrics, and feedback loops, AI assesses whether what we build tracks toward intended outcomes. In one example, we used AI to compare expected benefits against real-time delivery signals, which revealed early that a solution, while technically on track, would not achieve the intended business impact. That insight allowed us to pivot before committing fully.
By integrating delivery data, performance metrics, and feedback loops, AI assesses whether what we build tracks toward intended outcomes.
Execution management: AI shifted teams from reactive to proactive. Instead of waiting for reported issues, AI scans patterns across timelines, risks, and dependencies to surface early warnings. This changes the delivery leader's role from managing problems to anticipating them. In one portfolio, this reduced escalation cycles and improved predictability because we addressed risks before they became visible disruptions.
Across all these areas, the common thread is this: AI does not replace leadership; it sharpens it. It brings visibility to previously hidden aspects and accelerates the feedback loop between intent, action, and outcome. The result is not just faster delivery, but smarter, more aligned, and more value-driven execution.
Why agentic workflows focus on high-value delivery tasks
We are exploring agentic workflows selectively, particularly in areas like data aggregation, reporting, and risk scanning. These are areas where automation can significantly reduce manual effort and improve speed. Early results show faster turnaround times, improved visibility, and more capacity for teams to focus on higher-value work.
We are deliberate in how we adopt these approaches. Full automation without governance introduces risk. The goal is not to automate everything, but to automate intelligently while maintaining control over critical decisions.
Why strategic project intelligence will redefine delivery
Project delivery, as we know it today, will no longer exist as a standalone discipline. The primary focus will shift from managing projects to orchestrating value across the enterprise.
In this future, traditional markers of success such as timelines, budgets, and task completion will be secondary. Organizations will primarily ask if they are making the right decisions at the right time to maximize value. AI will take over coordination, reporting, and much operational complexity, but it will also raise the stakes. Effort or activity will no longer hide poor strategy, weak prioritization, and unclear outcomes; AI will expose them in real time.
The project manager will evolve into a strategic translator and value orchestrator—someone who understands business strategy, leverages AI and data intelligence, and actively shapes decisions rather than simply executing them. PMOs will no longer function as reporting centers. They will evolve into Strategy Realization Offices, directly accountable for value delivery, clear capital allocation, and portfolio-level decision-making.
The project manager will evolve into a strategic translator and value orchestrator.
Organizations will distinguish themselves not by how well they manage projects, but by how well they integrate intelligence into their delivery systems. Those who adopt this new framework will continuously see value creation and leakage. They will reallocate resources faster, adapt to change with precision, and outperform competitors not because they work harder, but because they make better decisions consistently.
The uncomfortable truth is that many organizations will not make this transition in time. They will continue to optimize execution in systems never designed for today’s complexity, and they will fall behind.
The uncomfortable truth is that many organizations will not make this transition in time.
How delivery leaders can navigate AI-driven change successfully
This moment requires a shift in identity. You can no longer coordinate tasks or manage timelines. You must become a strategic translator who understands the business, connects strategy to execution, and actively shapes decisions. That means developing the ability to challenge priorities, quantify value, and make trade-offs visible to leadership.
Stop focusing on tools and upgrade your thinking. Build your adaptability muscle and expand your psychological capacity to understand how leaders make decisions.
AI is not the differentiator. Strategic clarity, decision-making capability, and value orientation are. Without clear intent, AI simply accelerates confusion. With clarity, it becomes a powerful amplifier.
Many leaders rush to adopt new tools, but without a clear understanding of what value to create, these tools simply accelerate noise. If you do not define success properly, AI will help you get to the wrong destination faster. At a practical level, start by redesigning your delivery environment. Move away from status reporting and toward decision-based governance. Ensure every meeting clarifies what should stop, what should accelerate, and where to reallocate resources. Build visibility not just into progress, but into value creation and value leakage.
If you do not define success properly, AI will help you get to the wrong destination faster.
Equally important, lead people through this shift. AI will change workflows, but it will also create uncertainty. Leaders who can bring clarity, build trust, and align teams around outcomes will stand out. This is where human leadership becomes even more critical.
Finally, do not wait for permission or perfect conditions. Leaders who define the next era of delivery start now, experiment, learn quickly, and refine their approach with intention.
Follow along
You can keep up with Fola Alibi's work on LinkedIn. And check out Strategic Project Leader.
More expert interviews to come on The Digital Project Manager!