Table of Contents
Table of Contents
Table Of Contents
Table Of Contents
Read top CDO and data leader data strategies to scale AI successfully.
Nearly 9 out of 10 organizations report deploying and regularly using AI in at least one function. As adoption grows, executives are focused on turning early pilots into measurable business value.
Yet scaling remains a challenge. Kyndryl’s research finds that nearly 60% of innovation efforts stall after the proof-of-concept phase, increasing pressure to demonstrate returns on AI investments.
For CDOs and data leaders, overcoming these barriers means examining whether the underlying data is reliable enough to support production AI.
91% of data leaders report that data reliability is a barrier to moving GenAI initiatives from pilot to production.
Source: Informatica from Salesforce, CDO Insights 2026
Unsurprisingly, initiatives to scale AI operations and maximize returns have the undivided attention of executives and technology leaders. The role a strong data foundation plays in driving trust and enterprise-scale AI is now clear.
Companies now prioritize investing in data and AI management to build a reliable, safe, and compliant data foundation that supports AI as it scales into production and across the enterprise.
This article helps CDOs and data leaders understand the core challenges of scaling AI and the five data strategies to navigate them.
Key Takeaways
- Break AI pilot stagnation: Nearly 60% of enterprise AI projects stall at proof-of-concept due to fragmented architectures, integration bottlenecks, and unharmonized data sources.
- Unify integration and observability: Modernizing to cloud-native pipelines and automated observability eliminates manual data prep while maintaining real-time data health and preventing model drift.
- Embed automated AI governance: Scaling autonomous AI requires moving past manual oversight by embedding lineage tracking, privacy controls, and compliance rules directly into data workflows.
- Foster organizational literacy: Technology alone can’t drive success. Upskilling workforce AI fluency through targeted role-based training is essential to foster adoption, trust, and responsible deployment.
Why scaling AI continues to be a challenge
Moving AI from pilot to production means transitioning AI initiatives from experimental, small projects to fully operational, enterprise-wide solutions. Unlike technical proof-of-concepts (POCs), scaling AI is non-linear. It requires organizational trust, transparency, clear ownership, and adaptability. Companies must align decisions across architecture, workflows, governance, compliance, and team capabilities.
Robust data and AI management practices are crucial for companies to handle increasing data volumes, complexity, and user demands without compromising AI reliability, performance, or accuracy.
Building a data and AI foundation for consistent enterprise AI performance is challenging. Common reasons AI initiatives fail to move beyond experiments include the following:
Data integration and connectivity bottlenecks
Successful AI deployment relies on seamless connectivity among data, applications, and systems. Integrating AI with existing processes is complex due to compatibility issues, interoperability limitations, and the need for smooth integration that doesn't disrupt operations. AI systems also require massive amounts of data for training and inference. Integrating data from diverse, siloed sources is complex due to diverse data types, incompatible formats, and fragmented tools, all of which slow or limit AI's expansion.
Integration Issues Checklist
- Are there frequent gaps or delays in accessing harmonized and synchronized data needed for AI initiatives?
- Do data quality problems, such as duplicates, missing values, or conflicting records, occur frequently?
- Is there an increasing demand for resources for manual data cleansing before AI model training?
- Are you experiencing reduced data availability and frequent failures in data and AI pipeline performance?
- Is there noticeable performance degradation or inability to support new data sources as the data load increases?
Lack of AI model monitoring and data observability
Continuous monitoring and maintenance are vital for a resilient, high-performing AI system at scale. However, monitoring data and AI pipelines remains difficult in most enterprises. Implementing standardized, unified monitoring across diverse, siloed systems can be technically complex and time-consuming.
Organizations must monitor data and model accuracy with key metrics to effectively measure performance and take timely actions before accuracy drops below set thresholds, which can lead to incorrect insights. Insufficient monitoring creates blind spots that hinder enterprises from maintaining AI quality, trust, and agility needed for safe deployment at scale.
Monitoring Issues Checklist
- Are you observing a noticeable decline in model accuracy, precision, or other key performance metrics?
- Is your organization's response to data quality issues slow, relying mainly on manual or reactive resolution methods?
- Do you face difficulty correlating data quality and model performance metrics due to the use of multiple disconnected tools?
- Are increasing costs from data reprocessing, model retraining, and operational inefficiencies a growing concern?
- Are critical indicators such as data drift, concept drift, or fairness metrics missing or not monitored effectively?
Overlooking enterprise data governance and trust
Trust affects how organizations, users, and stakeholders perceive and rely on AI systems as they expand within the enterprise. It’s built when AI consistently delivers accurate, unbiased, and explainable results. Treating governance as an afterthought raises AI operation risks. A technically advanced model that performs well in pilots can still fail if biases and inconsistencies emerge or if the model operates without safeguards that promote transparency and responsible use.
Governance-Related Issues Checklist
- Is there frequent confusion or uncertainty regarding accountability, ownership, or processes related to AI initiatives?
- Are there occurrences of unexpected behavior, bias, or errors in AI models after deployment?
- Has there been a loss of trust or confidence in AI systems due to insufficient explanation of model outcomes?
- Have there been any non-compliance incidents related to AI governance or usage?
- Do audits experience delays because of insufficient or missing records documenting AI model development and deployment?
Struggling to balance innovation with control
Being too conservative with AI limits innovation and delays benefits, while being too risk-tolerant can introduce compliance, privacy, or security risks.
Companies often fail by enforcing overly strict data privacy policies that hinder experimentation or lax controls that expose sensitive data and pose compliance risks. Limited expertise in deploying data protection and access management further complicates scaling, hampering a company’s ability to balance control and innovation and managing risks without avoiding them.
Access Management-Related Issues
- Are AI deployments experiencing excessive delays from prolonged risk assessments and compliance reviews?
- Have there been incidents of data being accessed by unauthorized users or systems within the organization?
- Are the adoption and usage of data and AI products below expectations or industry benchmarks?
- Are there high volumes of access denials or rejections for data requests related to AI initiatives?
- Do teams face long wait times to obtain necessary data because of complex approval processes?
Gaps in organizational AI readiness and workforce literacy
Rushing into AI initiatives without proper readiness often stalls pilots. Scaling AI requires technology, people, and process changes. Ignoring these can hinder progress.
Without building AI literacy across teams, AI capabilities may be misunderstood, misaligned, and even misused, eroding trust in AI systems and increasing resistance to change.
Companies must adopt a holistic approach to improve AI readiness that extends beyond technology. This includes building AI fluency through shared language, a collaboration platform, ongoing learning, and a culture based on responsible AI frameworks.
Readiness Issues Checklist
- Is there a noticeable gap between the AI models' performance and the actual business impact achieved?
- Do team members across the organization have a low understanding of AI concepts, capabilities, and limitations?
- Are there frequent pushbacks or reluctance from users to adopt AI tools due to a lack of understanding?
- Are communication and cross-collaboration between teams working with AI limited or ineffective?
- Are there significant skill gaps within the organization and is there a demand for new roles to effectively drive AI initiatives?
86% of CDOs expect increased investment in data management by 2026, mainly to enhance data privacy, governance, and AI operations.
Source: Informatica from Salesforce, CDO Insights 2026
As companies implement initiatives to accelerate their AI roadmaps, we share the five most essential strategies data leaders need to prioritize.
Strategy 1: Reinforce data integration and engineering for AI
As AI programs scale, integrating data across layers becomes vital to supply models with diverse inputs. Legacy on-premises tools often cause bottlenecks, hindering scalable, cost-effective operations.
Today's integration tools support smarter decision-making and efficiency by creating trusted data and AI pipelines, connecting cloud and legacy apps, breaking silos, and providing a unified data view for AI models.
No-code, no-build tools enable real-time data sync, orchestrate disconnected systems, cut costs, and reduce development effort. Taking a strategic approach to data and application integration helps transition from manual coding and proof-of-concept exercises to building production-ready AI applications.
Strategy 2: Enhance model monitoring and observability
The path to trusted, scalable AI begins with rethinking data quality, which now includes consistency, context, continuity, and completeness beyond accuracy. Traditional manual profiling and cleansing, along with reactive monitoring, are inadequate for handling model drift, feedback loops, or cross-functional AI pipelines.
Point solutions and bolt-on fixes increase tool sprawl and vendor complexity. Hand-coding for data quality is complex, resource-intensive, and not enterprise-ready, often resulting in brittle, non-scalable solutions that can’t meet AI demands. This creates gaps in governance, security, and trust.
What's needed is a unified, cloud-native solution that integrates data quality at every stage of the data and AI lifecycle. Companies must monitor, trace, and understand data health and behavior across systems and pipelines.
Strategy 3: Build trust with data and AI governance
AI depends on trust, rooted in governance. As AI projects grow to involve larger, diverse teams, ensuring data reliability, explainability, and regulatory compliance becomes crucial. A structured approach to AI governance is vital for transparency and oversight, but manual governance struggles to keep pace with the increasing complexity of data and models.
76% of data leaders say their governance hasn't entirely kept pace with AI adoption.
Source: Informatica from Salesforce, CDO Insights 2026
To roll out AI organization-wide, governance tools must manage project stages across systems and enable stakeholder collaboration. Embedding AI automation into governance helps monitor quality, detect bias, track lineage, and ensure compliance, providing a competitive edge.
Strategy 4: Leapfrog innovation with agentic data management
Agentic AI is a natural evolution of scaling AI into autonomous, intelligent, adaptable systems. As agents shift from experiments to deployment, ensuring they operate on accurate, relevant, and governed data from the start is crucial.
Companies must move beyond legacy data management and embrace an agile, responsive, and AI-driven approach to combat the amplified data risks associated with AI agents.
91% of organizations are using AI agents, yet only 10% have a well-developed AI strategy to manage these non-human identities.
Source: Okta, AI at Work 2025: Securing the AI-Powered Workforce
Agent-driven data management enables AI systems to be more adaptive and responsive by using AI-powered agents that handle complex data tasks over time with minimal human oversight. These specialized agents focus on specific data lifecycle components, managing different aspects of data and AI with precision.
Investing in capabilities that build, deploy, and manage enterprise agents trained with clean data and immersed in the business context can transform AI scaling.
Strategy 5: Elevate data and AI literacy
AI programs and strategies fail when there’s poor or limited data and AI fluency in an organization.
91% of organizations are using AI agents, yet only 10% have a well-developed AI strategy to manage these non-human identities.
Source: Okta, AI at Work 2025: Securing the AI-Powered Workforce
Only when teams understand and trust AI and make informed decisions can companies scale AI. Although many AI POCs validate technology, the people and skills are often tested only at runtime. Finding skilled individuals to design, train, and deploy AI/ML models is challenging and costly.
Successful organizations foster AI fluency among data professionals across roles. Building an AI-fluent organization is faster by expanding the current staff's skills rather than hiring externally. Companies should boost AI literacy beyond IT to business and operations teams. Role-based training ensures targeted upskilling.
Operationalizing AI needs cross-disciplinary skills, change management, and executive support to foster a data- and AI-focused learning culture. Choosing user-friendly, scalable, and transparent platforms across data and AI operations builds user trust.
5 best practices framework to fuel enterprise AI
Taking AI from pilots to enterprise use is complex and varies by organization, influenced by goals, data, culture, and tech. Below, we outline a framework of key stages, activities, and best practices designed to guide data leaders and CDOs through their journey.
| Stage | Key activities | Best practices |
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| 1. Pilot and proof of concept (PoC) |
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| 2. Foundation building |
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| 3. Model operationalization |
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| 4. Scaling across use cases and business units |
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| 5. Continuous improvement and innovation |
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Break the AI pilot loop with IDMC
When companies scale up AI initiatives, they need a platform for AI-ready data. Informatica is a strategic partner helping CDOs and data leaders scale AI responsibly with the right capabilities, governance, and organizational readiness. Informatica's Intelligent Data Management CloudTM (IDMC) provides AI-driven automation, unified data management, and modular design, enabling organizations to quickly advance their AI initiatives on a shared, trusted data platform.
The platform's scalability and flexibility support modern, hybrid, and cloud-native architectures, enabling enterprises to adapt and expand their AI initiatives without limits. As a leader in data management, Informatica offers trusted technology and expertise gained from helping hundreds of clients across various industries and regions.
Organizations must now move beyond AI experiments and act quickly to gain a competitive edge and harness AI's transformative value.
Ready to break out of the AI pilot loop? Get the full guide to the five data and AI management strategies that take enterprises from pilot to production.
Data Management Strategies FAQs
AI projects stall at the proof of concept stage primarily due to data integration bottlenecks, lack of continuous model monitoring, unharmonized data sources, and a failure to embed automated governance directly into data pipelines.
Modernizing data integration transitions AI to production by replacing legacy on premises tools with cloud native, no code data pipelines that connect siloed systems and deliver synchronized, reliable data in real time.
Automated AI governance provides a structured framework that tracks data lineage, monitors model quality, detects bias, and enforces compliance rules across workflows without relying on slow, manual oversight.
Agentic data management uses specialized, AI driven agents that automatically handle complex components of the data lifecycle, such as cleaning and contextualizing data, with minimal human intervention to build adaptive, autonomous systems.
Workforce literacy is critical because technology alone can’t drive adoption. Upskilling employees in AI concepts ensures teams understand, trust, and responsibly use AI outputs in their day-to-day operations.
