Managing complex enterprise and government IT projects is a constant battle against shifting requirements and growing stakeholder expectations. To stay ahead, project managers need predictive, real-time visibility. While the market is flooded with hype about automation, what actually works are specific AI applications that enhance risk forecasting, automate resource tracking, and streamline data analysis. AI in project management successfully delivers the precise visibility needed to keep massive IT initiatives on time and on budget.
In this interview, Tonko Borovina talks about how artificial intelligence is changing the way teams approach scope management, risk detection, and stakeholder communication and why human judgment still matters most.
Background & experience:
With over 10 years of experience delivering complex IT and digital transformation projects, Tonko leads large-scale implementations for government institutions in international environments.
1. From your experience, where does AI bring the most immediate value in complex IT projects?
Tonko: From my experience, the biggest and fastest value comes right at the start of the project, during requirements analysis.
On large implementations like government HRMS or ERP systems I've worked on, you're usually dealing with a large amount of documentation, RFPs, annexes, and different versions of requirements, often written by multiple stakeholders over time. Inevitably, that leads to inconsistencies, overlaps, and sometimes even contradictions.
What AI does really well is helping you go through that content quickly and highlight what does not align. It can point out conflicting requirements, identify unclear statements, and group similar needs that are just written differently.
In practice, that saves a lot of time in early workshops, but more importantly, it helps avoid issues later on. Instead of discovering problems during UAT or deployment, you are addressing them upfront when they are much easier to handle. For me, that is where the most immediate and practical value is.
2. How can AI help project managers detect scope creep before it turns into missed deadlines or budget overruns?
Tonko: In practice, scope creep rarely comes through a formal change request. It accumulates slowly, one conversation at a time.
In the projects I manage, especially with government solutions and government stakeholders, you will see new expectations appear in meetings, emails, backlog items, or even casual comments during workshops. Individually, they do not always look like scope changes, but over time, they add up.
What AI helps with is connecting those pieces. It can track how something mentioned in a meeting later appears in a Jira ticket and is referenced in an email, even if it was never part of the original scope.
That is very difficult to track manually when you are managing multiple communication streams simultaneously. And timing is critical. If you recognize it early, you can address it as a scope discussion. If you notice it too late, it will already be affecting delivery and the budget. So, for me, AI really helps by increasing visibility and allowing you to stay in control of the scope.
3. How is AI helping project managers get ahead of risks before they escalate?
Tonko: Previously, risk management was largely reactive. You would identify risks during status reports or when issues are escalated. With AI, you can detect trends much earlier.
In complex IT programs like the ones I’ve handled, with integrations, data migration, and many dependencies, there are usually warning signs before a problem gets serious. For example, you might notice extra rework, delays in dependencies, or frequent changes in requirements.
AI can monitor those signals continuously and recognise patterns that have historically led to issues. So instead of waiting for a milestone to slip, you can already see that things are heading in the wrong direction. That gives you time to act, whether that’s reallocating resources, adjusting priorities, or tightening governance.
So the shift is really from reacting to problems to anticipating them, which is a big advantage in complex and fixed-price environments.
4. Can you share examples of how AI analyses historical project data to identify risks that human teams might overlook?
Tonko: There are a few patterns where AI can add real value based on historical data.
One example is integration complexity. Projects that involve multiple legacy systems, custom integrations, and data migration usually carry a higher risk around testing and timelines. AI can recognise that pattern across projects and highlight it early.
Another example is stakeholder-driven scope expansion. In projects with a lot of workshops and ongoing requirement discussions, something I’ve seen quite often in government environments, scope can easily grow if governance isn’t strict. AI can pick up on that trend much earlier.
There’s also the human side, like delivery fatigue. When teams are under pressure, working overtime, and switching between tasks, quality issues don’t show up immediately; they tend to surface later. AI can detect those early signs even when they’re not obvious.
As project managers, we often feel these things based on experience, but AI provides a broader, data-driven perspective and helps confirm when something needs attention.
5. How can AI support PMs in understanding stakeholder sentiment and expectations?
Tonko: Stakeholder management will always be personal, but AI can help you spot early signs of misalignment.
In my experience, especially with large and sensitive programmes, misalignment doesn’t happen overnight. You usually start seeing small changes, differences in tone, more urgency in communication, or expectations slowly moving outside the agreed scope.
AI can analyse those communication patterns across emails, meetings, and collaboration tools and highlight when something is changing. For example, if stakeholders start referring to things that were never agreed upon, or communication becomes more escalatory, you want to address it early. It gives you a chance to step in, clarify expectations, and realign before it turns into a bigger issue.
That said, AI only provides the insight. Building trust, managing relationships, and handling those conversations is still very much the responsibility of the project manager.
6. AI is becoming more capable. Should project managers be worried about being replaced?
Tonko: For me, it’s very important to keep AI in the role of support, not decision-making. In the types of projects I manage, fixed-price, complex environments with multiple stakeholders, decisions are rarely purely data-driven. They involve commercial considerations, client relationships, and sometimes organisational or political context.
AI can give you very useful insights and help you see things earlier or more clearly, but it doesn't fully understand that context. AI consulting helps organisations define exactly where AI should and shouldn't have a say. I see it as a tool that improves visibility and supports better decisions, but the responsibility for those decisions stays with the project manager.
At the end of the day, AI can make you faster and better informed, but judgment, accountability, and leadership still need to come from experience.
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