Understanding the Common Service Data Model (CSDM) in ServiceNow
The Common Service Data Model (CSDM) serves as a unified framework that helps organizations manage and illustrate their services and related data…
AI continues to dominate conversations across IT and digital transformation teams. Leaders see the potential to improve decision-making, reduce manual effort, and help teams work more effectively. But for many organizations, turning AI from an idea into something operational proves harder than expected.
The biggest obstacles are rarely about AI itself. They’re rooted in the realities of existing systems, data, and processes. Technical debt, fragmented architectures, and unclear ownership often stand between ambition and execution.
Understanding those constraints — and working within them deliberately — is what separates stalled initiatives from steady progress.
AI initiatives tend to surface problems that already exist beneath the surface. In that sense, AI doesn’t introduce complexity so much as it reveals it.
As soon as teams try to integrate AI into real workflows, questions emerge:
Many organizations discover that the answers aren’t as clear as they assumed. Over time, systems evolve, teams adapt, and workarounds become normalized. AI shines a light on those inconsistencies.
This can be uncomfortable, especially when expectations are set around speed. AI projects are often positioned as innovation efforts, but in practice, they quickly turn into discovery exercises.
Key takeaway: When AI progress slows, it’s often because it’s exposing how work actually gets done — not because the technology isn’t ready.
Most organizations don’t suffer from a lack of data. They struggle with confidence in that data.
For AI to be useful, data must be more than accessible. It needs to be consistent, understandable, and trusted by the people relying on it. Without that trust, even technically sound AI outputs are questioned or ignored.
Common data challenges include:
AI tends to amplify these issues rather than smoothing them over. When outputs vary or feel unreliable, users lose confidence quickly.
A practical way forward is to narrow the focus. Instead of trying to fix everything, start with a single data domain tied to a clear use case. Standardize key fields, clarify definitions, and establish light governance around how that data is maintained.
Key takeaway: AI readiness improves fastest when teams focus on making one data set trustworthy rather than attempting broad, unfocused cleanup.
Technical debt isn’t one thing. It shows up in different forms, and not all of it needs to be addressed immediately. The challenge is knowing which types of debt directly affect AI initiatives.
Some of the most common categories include:
Architecture decisions that made sense years ago may now limit flexibility. Rigid integrations or siloed systems can make it difficult for AI to access data or trigger actions across workflows.
Manual approvals, informal handoffs, and undocumented exceptions introduce variability. AI struggles when processes aren’t consistently followed or understood.
Inconsistent data models, unused fields, and unclear ownership reduce confidence in AI-driven insights.
Point-to-point integrations often work until they don’t. As AI initiatives expand, these brittle connections become harder to maintain and extend.
Not all debt needs to be eliminated. Some can be worked around, while other issues may need to be addressed before AI can move forward safely.
Key takeaway: The goal isn’t to remove all technical debt, but to understand which debt actively blocks progress and address that first.
Large, sweeping AI initiatives tend to stall under their own weight. A more effective approach is to start with narrowly defined use cases that fit naturally into existing workflows.
Strong early use cases share a few characteristics:
Examples might include summarizing historical records, assisting with categorization, or highlighting patterns that are difficult to see manually. These efforts help teams learn how AI behaves within their environment without introducing unnecessary risk.
Just as important, they build confidence. Early wins show stakeholders that AI can deliver value without disruption.
Key takeaway: Momentum matters. Small, well-chosen use cases create learning and trust that support larger efforts later.
AI works best in environments where systems are understandable and well-connected. Overly complex architectures make scaling AI more difficult over time.
Simplification doesn’t require dramatic change all at once. It often starts with questions like:
Reducing unnecessary complexity creates space for AI to operate more reliably. It also lowers the cost of experimentation and change.
Key takeaway: A simpler system landscape gives AI room to grow without increasing operational risk.
Governance is frequently seen as a barrier to speed, especially when AI initiatives are involved. In reality, the absence of governance often creates more friction over time.
Effective governance focuses on clarity rather than control. It establishes:
When governance is right-sized, teams move faster because expectations are clear. Users trust outputs, leaders feel confident in decisions, and AI initiatives avoid unnecessary rework.
Key takeaway: Governance done well doesn’t slow AI adoption — it makes progress sustainable.
Even well-designed AI solutions can struggle if users don’t trust or understand them. Resistance often has less to do with fear of change and more to do with uncertainty.
Common concerns include:
Adoption improves when AI is positioned as an assistant, not an authority. Providing transparency, inviting feedback, and allowing users to refine outcomes over time builds confidence.
Key takeaway: AI succeeds when it supports human judgment, not when it attempts to replace it.
Early AI success doesn’t always show up as dramatic cost savings. More often, it appears in incremental improvements that compound over time.
Useful indicators include:
These signals help teams demonstrate progress while setting realistic expectations with leadership.
Key takeaway: Early AI value is often measured in momentum and consistency, not just financial return.
AI delivers the most value when it operates within a cohesive platform rather than as a standalone solution. Platform-based approaches provide shared context, consistent experiences, and stronger governance.
This alignment reduces duplication, improves scalability, and ensures AI capabilities evolve alongside the organization’s broader goals.
Key takeaway: AI works best when it’s integrated into how the organization already operates.
Progress with AI doesn’t require perfect systems or complete transformation. It starts with clear priorities and steady improvement.
A practical starting point includes:
The objective isn’t to solve everything at once. It’s to create momentum that builds confidence and capability over time.
