AI investment is accelerating. So is AI project abandonment.
According to S&P Global Market Intelligence, the share of companies abandoning most AI initiatives rose from 17% to 42% year over year. And across industries, more than half of executives cite data quality and availability as the single biggest barrier to AI adoption.
The pattern is consistent: organizations are deploying AI tools before the underlying data infrastructure can support them. The result is models that produce unreliable outputs, insights that cannot be trusted, and business stakeholders who lose confidence before value is ever demonstrated.
Getting AI right at scale is not primarily a technology problem. It is a data problem, and specifically, a governance, quality, and readiness problem.
Most AI pilots fail to reach production for the same reasons. The Informatica CDO Insights 2025 survey identified the top obstacles to AI success as data quality and readiness (43%), lack of technical maturity (43%), and skills shortage (35%). These are foundational issues that no model, platform, or vendor can solve after the fact.
There are three places where AI initiatives most commonly break down.
AI models are only as reliable as the data they are trained and run on. In most enterprise environments, data sits across multiple systems, platforms, and business units, each with its own definitions, formats, and quality standards. When those systems are not integrated, models draw from inconsistent inputs and produce inconsistent outputs.
A 2025 DATAVERSITY survey found that 62% of data professionals report incomplete data, 58% cite capture inconsistencies, and 57% flag data integration issues as persistent obstacles. In industries like financial services and healthcare, where AI informs high-stakes decisions around fraud detection, risk modeling, or clinical care pathways, fragmented data is not just an inefficiency. It is a liability.
A 2025 AuditBoard study found that only one in four organizations have fully operational AI governance, despite widespread awareness of the need for it. Most have drafted policies but struggle to operationalize them due to unclear ownership, limited expertise, and resource constraints.
The gap between policy and practice matters because AI without governance creates compounding risk. Models drift over time. Data inputs change. Regulatory requirements evolve. Without continuous oversight, an AI system that performed well at deployment can quietly degrade in accuracy and compliance without anyone noticing until something goes wrong.
Many AI initiatives stall because they were defined around technical capability rather than a specific business problem. Teams ask which models to deploy before they have quantified the process gap those models are meant to close.
The most successful deployments start from the opposite direction: identify the operational pain point, measure what it costs, and then design the AI capability to address it. When the use case is grounded in a real business outcome, it is far easier to secure stakeholder support, measure progress, and sustain investment through the full path to production.
AI readiness is a set of practices that need to be in place and maintained continuously as models, use cases, and data environments evolve. Four areas define whether data is truly ready to support scalable, trusted AI.
For AI, data must be representative of the full range of conditions the model will encounter, consistent across sources, current enough to reflect the operating environment, and documented well enough that anyone working with it understands its limitations.
Key practices include:
Governance defines who owns data, how it is managed, how it can be used, and how accountability is maintained. Without it, AI systems have no reliable mechanism for ensuring the data feeding them remains accurate, compliant, and fit for purpose as conditions change.
Effective AI governance includes:
Metadata is what allows teams to assess whether a dataset is appropriate for a given use case, understand why a model produces a particular output, and trace any accuracy or compliance issue back to its source. A 2025 DATAVERSITY survey found that only 11% of organizations have high metadata management maturity, meaning the vast majority of enterprises lack the visibility required to govern their AI systems with real confidence.
AI workloads require scalable compute, low-latency data access, real-time processing support, and integration with the systems where AI outputs will be applied. Many organizations begin AI initiatives before their cloud and data platform environments are ready to support them at scale.
This is especially common in industries with hybrid architectures, where data is distributed across on-premises systems, cloud environments, and operational technology platforms. The AI use case is often valid. The platform cannot support it yet. Closing that gap requires deliberate sequencing of infrastructure modernization alongside AI development, not treating them as separate workstreams that will converge later.
The path from a working AI pilot to a production system that delivers sustained business value requires three organizational capabilities that most pilot programs do not build.
Cross-functional alignment on data ownership.
AI systems draw on data from across the organization. When no one is clearly accountable for the quality and fitness of that data, quality issues persist and governance gaps widen. Moving to production requires establishing clear ownership across both business and technology stakeholders, not just within the data team.
Adoption and workflow integration.
AI delivers value when it is embedded into how decisions are made and work gets done. Role-based enablement and change management are not post-launch activities. They determine whether the business ever realizes the value the model was built to deliver.
Continuous monitoring and model management.
AI models degrade over time as data distributions shift and business conditions change. Production AI requires an ongoing operating model that includes performance monitoring, drift detection, retraining schedules, and governance checkpoints. Organizations that treat model deployment as a finish line routinely see performance erode within months.
Every industry faces data readiness challenges, but the stakes and specific blockers vary.
In financial services, AI applied to fraud detection, credit risk modeling, and regulatory reporting requires model accuracy and explainability that are directly tied to compliance and financial exposure. Data lineage and governance are prerequisites, not enhancements.
In healthcare, fragmented EHR data, interoperability gaps, and HIPAA requirements make data readiness foundational to both accuracy and regulatory compliance before any clinical AI application can be trusted.
In utilities and energy, OT and IT data environments are often siloed, and integrating operational data with enterprise systems is a prerequisite for load forecasting, predictive maintenance, and grid optimization to function reliably.
In communications, network optimization and churn prediction depend on real-time data pipelines that most legacy OSS/BSS environments were not built to support, making platform modernization a precondition for AI deployment.
The organizations making real progress with AI share a common foundation: they invested in data before they invested in models. That means treating data quality, governance, and platform readiness as prerequisites, defining AI use cases around specific business outcomes, and integrating adoption and change enablement into the delivery model so AI actually changes how decisions are made.
AI trust is built incrementally, through consistent data practices, accountable governance, and a delivery model that treats model management as an ongoing capability rather than a launch event.
TSG helps organizations build the data foundation, governance model, and delivery infrastructure required to move AI from pilot to sustained operational capability. Our integrated approach spans data strategy, platform modernization, AI enablement, and change adoption so AI investments deliver measurable outcomes, not just working prototypes.