How Better Data Engineering Unlocks Enterprise AI

Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.

As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.

According to McKinsey’s latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.

Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.

Data Quality Is the Competitive Advantage

For Hastimal Jangid, Co-Founder of RankRabbit AI, organizations frequently make the mistake of pursuing sophisticated AI technologies before establishing confidence in the underlying data.

“The highest-value capability isn’t a platform—it’s data quality and discipline in lineage,” Jangid says. “Teams rush to buy new AI tools while ignoring that their underlying data is inconsistent, undocumented or untrustworthy. No model architecture compensates for that.”

Jangid also argues that metadata deserves far more executive attention than it typically receives. Strong cataloging enables employees to quickly locate, understand and trust enterprise information, reducing duplicated work while improving decision-making speed.

Equally important, he says, are dependable operational pipelines rather than fashionable technologies.

“A boring, dependable ETL process that runs on time, every time, quietly saves organizations more money than any AI initiative built on top of shaky data.”

Ultimately, Jangid says organizations consistently overestimate the value of cutting-edge AI while underestimating the value of disciplined engineering.

“The pattern I keep seeing is that companies that master these fundamentals get more value from a modest AI investment than those with cutting-edge tools sitting on fragile foundations. Get the plumbing right first.”

Move Beyond Lineage to Decision Traceability

Many organizations have improved their ability to track where data originates and how it moves through enterprise systems. Mani Padisetti of Almost Magic Tech Lab believes that is only the beginning.

“The highest-value capability is decision traceability, not simply cleaner pipelines or faster data movement,” he says. “A business should be able to answer: What data informed this decision? Which definition and version were used? What assumptions changed? Who was accountable for acting on it? What happened afterward?”

Those questions become increasingly important as organizations deploy autonomous and agentic AI systems capable of making thousands of operational recommendations every day. Padisetti illustrates the distinction with a practical example.

“An AI risk alert is not valuable because it is generated quickly; it is valuable when the right person can verify the evidence, override it when necessary and learn from the result.”

Padisetti believes the distinction is critical.

“Lineage tells us where data travelled. Decision traceability tells us whether it produced an outcome the organization can explain, correct and improve.”

Ultimately, he argues that this capability transforms data engineering into something much more valuable than back-office infrastructure.

“That is where data engineering becomes business infrastructure rather than platform maintenance.”

“Most organizations collect more data than they govern and invest in platforms before they have the skills to connect data to actual business processes.”

– Yogesh Malik, CEO of Way2Direct B.V.

SHARE IT

Creating AI Organizations Can Trust

For Yogesh Malik, CEO of Way2Direct B.V., successful AI adoption depends less on collecting additional data than on governing the information organizations already possess.

“The three capabilities that consistently deliver the most value are data governance, data lineage and the skill of creating data-product and data-process connections.”

Malik says organizations frequently reverse the proper order of investment, purchasing sophisticated platforms before building necessary organizational capabilities.

“Most organizations collect more data than they govern and invest in platforms before they have the skills to connect data to actual business processes.”

He describes governance as the discipline that establishes common understanding across the enterprise.

“Governance defines what your data means and who owns it. Lineage shows how it moves and transforms,” he says. “The connective skill, turning raw data flows into products and decisions, is where value is actually created.”

Malik says that trust ultimately determines AI success.

“Organizations that build these three capabilities first don’t just get better AI outcomes. They get outcomes they can trust and repeat.”

Build Data Discipline Before Agentic AI

As enterprises move beyond predictive analytics into autonomous AI agents, foundational data engineering becomes more than an operational concern—it becomes a strategic necessity. Sabarinath Yada, Business Architect Associate Manager at Accenture, argues that autonomous systems magnify existing data weaknesses rather than masking them.

“Agentic AI systems increasingly act autonomously across enterprise workflows,” he says. “The cost of foundational data weaknesses will compound rather than average out.”

Traditional analytics often tolerate occasional inconsistencies because humans remain in the decision loop. Autonomous agents, however, can execute thousands of actions daily based on the same flawed assumptions.

“An agent making thousands of micro-decisions daily amplifies semantic ambiguity and lineage gaps at a scale traditional analytics never did.”

Yada believes organizations that invest early in semantic consistency, governance and lineage will gain a durable competitive advantage.

“Organizations that treat data engineering fundamentals as strategic infrastructure, rather than a preliminary step to ‘the real AI work,’ will be the ones positioned to deploy autonomous systems with confidence rather than hope.”

Ultimately, Yada sees tomorrow’s winners differentiating themselves through disciplined execution rather than ever-larger models.

“The next competitive differentiator won’t be model sophistication,” he says. “It will be data discipline maturity.”

“If AI is only as good as the data, and the data has anomalies because you let AI work autonomously too quickly, the entire system seems shaky.”

– Lynn Comp, Head of AI Center of Excellence at Intel

SHARE IT

Design an Architecture That Evolves With AI

While many executives focus on selecting today’s best platform, Lynn Comp, Head of AI Center of Excellence at Intel, believes organizations should instead prioritize creating an architectural framework capable of adapting as AI technologies inevitably evolve.

“A solid data architecture framework from the start gives you something to ‘snap into’ over time even as the infrastructure evolves with AI-driven changes.”

In other words, organizations should think less about today’s tooling and more about building a flexible foundation that accommodates tomorrow’s innovations without constant redesign.

Comp is equally cautious about relying on AI itself to repair poor-quality data.

“In my opinion it’s risky to use a non-deterministic technology to fill in gaps and/or correct missing or incomplete fields.”

As generative AI becomes more capable, it can be tempting to automate data cleansing. However, Comp warns that allowing AI to infer missing values too early introduces new uncertainty into downstream systems.

“If AI is only as good as the data, and the data has anomalies because you let AI work autonomously too quickly, the entire system seems shaky,” she says. “I would be suspicious of the provenance and veracity of the AI output as a result.”

Organizations that establish trustworthy architectural standards early are better positioned to incorporate future AI capabilities without sacrificing reliability.

Reliable Pipelines Matter More Than Flashy Platforms

Despite the rapid pace of AI innovation, Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft, believes organizations continue to overlook the capabilities that consistently generate business value.

“Shiny new data platforms don’t create AI value. Reliable pipelines and unified business logic do,” he says. “When pipelines fail or data drifts, AI models generate confident, costly mistakes.”

To reduce those risks, organizations should automate quality controls rather than relying on manual validation after problems emerge.

“High pipeline availability and automated quality checks ensure your business operates on truth, not hallucinations.”

Beyond reliability, Muthukamatchi highlights another frequently overlooked capability: semantic consistency across the enterprise.

“If ‘churn’ or ‘revenue’ means different things to different teams, no advanced AI tool will fix the resulting chaos.”

As organizations deploy AI across finance, marketing, operations and customer service simultaneously, shared business definitions enable AI systems to deliver consistent recommendations regardless of where data originates.

For Muthukamatchi, these capabilities ultimately create a foundation that scales.

“When you master clean lineage, automated quality and unified definitions, you build an unshakeable data foundation.”

That foundation, he says, produces the outcome executives care about most.

“In AI, elite execution on basic data engineering will always outperform expensive, complex architectures built on shaky ground.”

“Without this foundation, even advanced platforms produce unreliable insights and low adoption.”

– Paul Freeman, Vice President of AI and Strategic Intelligence at Test Rite Products

SHARE IT

Context Is the Missing Layer in Enterprise AI

As organizations race to deploy large language models and AI agents, many underestimate how much institutional knowledge remains trapped in disconnected systems. Paul Freeman, Vice President of AI and Strategic Intelligence at Test Rite Products, has spent more than 25 years designing intelligent systems across global supply chain, warehouse management and retail sourcing operations. His experience modernizing enterprise data frameworks has reinforced a simple lesson: AI performs only as well as the context surrounding its data.

“The foundational data engineering capabilities delivering the greatest business value are comprehensive data discovery—knowing exactly what data is captured and where it resides across every process—and building a robust context and semantic layer on top of it.”

Freeman argues that many organizations believe they have sufficient data when, in reality, they lack a clear inventory of what exists and how it relates across business functions. Without that visibility, even sophisticated AI applications struggle to understand business intent.

“Maximized, context-enriched data lets LLMs and agentic tools accurately interpret and act on information,” he says. “Without this foundation, even advanced platforms produce unreliable insights and low adoption.”

For organizations pursuing enterprise AI at scale, discovering, organizing and contextualizing existing information may deliver greater returns than collecting more data.

Fix the Data That Drives Decisions First

While enterprise data strategies often aim to improve everything simultaneously, Divya Parekh, Founder of executive coaching brand DivyaParekh.com, believes organizations generate more value by identifying the small number of data elements that directly influence critical business decisions.

“For me, the capability that pays is the least glamorous one: knowing which data actually touches a decision, and fixing that first.”

Her philosophy stems from experience working in regulated biopharmaceutical environments where “you couldn’t release anything you couldn’t reconstruct months later.”

Rather than attempting enterprise-wide perfection, Parekh encourages leaders to prioritize the information with the greatest operational impact.

“Most teams try to raise quality everywhere at once and stall. The ones that get value pick the handful of fields their real decisions run on and put a name against each.”

Ownership, she argues, is just as important as technology. Organizations frequently invest in sophisticated governance capabilities without assigning people responsible for using them.

“Lineage no one reads is shelfware.”

Parekh offers executives one practical question before approving another technology purchase.

“When this number is wrong on Monday, who finds out, and how fast?”

The answer often reveals whether an organization truly understands its own data.

Treat Data Engineering as a Business Capability

Throughout his career leading cloud engineering, data platforms and enterprise modernization initiatives, Venkata Kondepati, Manager of Data Architecture and Engineering at Ascentt, has consistently seen organizations mistake data engineering for a purely technical discipline.

“The data engineering capabilities that deliver the greatest business value are not always the newest tools,” he says. “They are trusted data foundations: clear data ownership, high-quality pipelines, reusable data products, governed access, common metrics and strong metadata.”

Those capabilities produce value because they simplify collaboration across technical and business teams alike.

“These capabilities reduce friction between business, analytics and AI teams. Without them, organizations spend too much time reconciling numbers, moving data manually and rebuilding the same logic for every use case.”

As AI adoption expands, the return on disciplined engineering continues to grow.

“AI increases the value of strong data engineering because models need context, lineage, security and reliable inputs.”

Kondepati believes the organizations that outperform their competitors will embrace a broader perspective.

“The winners will be companies that treat data engineering as a business capability, not just a technical function.”

AI Doesn’t Change the Fundamentals—It Exposes Them

After decades of helping organizations operationalize AI and machine learning, Blake Crawford, Partner and CTO at Fusion Collective, sees a familiar pattern emerging: AI has not rewritten the rules of data engineering—it has simply made longstanding weaknesses impossible to ignore.

“Nothing has fundamentally changed in the data engineering space. The rules today are the same as they were pre-AI.”

He lists the essential building blocks without hesitation.

“Reliable ingestion, agreed ontology, quality checks, lineage, idempotency and versionable pipelines,” he says. “These items were struggles and oftentimes short cut before AI. Now, they can’t be. We’re playing the same game, just on a new field.”

Ultimately, Crawford believes AI acts less as a disruptive force than as a mirror reflecting organizational maturity.

“Once again, we’re seeing AI surface issues that always come down to the same root cause: the fundamentals.”

His conclusion perfectly sums up the consensus among Think Tank members.

“If you’re not good at the fundamentals, AI will just expose that a whole lot faster.”

Data Engineering Priorities for AI Leaders

• Prioritize data quality before investing in more AI tools. Establish disciplined data quality, lineage and metadata practices before expanding AI initiatives.

• Measure decisions, not just data movement. Build decision traceability that connects data, business actions and measurable outcomes.

• Create governance that the business owns. Define clear ownership, common definitions and repeatable governance processes so AI outputs are trusted across the organization.

• Treat data engineering as strategic infrastructure. Prepare for agentic AI by investing in semantic consistency and lineage today, ensuring autonomous systems operate with confidence rather than uncertainty.

• Design for long-term flexibility. Build a scalable architecture that evolves alongside AI technologies instead of repeatedly replacing platforms.

• Automate quality and standardize business definitions. Reliable pipelines, automated validation and consistent business terminology reduce AI errors and improve enterprise decision-making.

• Know where your data lives and what it means. Develop comprehensive data discovery and semantic context so AI systems can accurately interpret enterprise information.

• Improve the data that matters most first. Identify the handful of data elements driving your most important business decisions and assign clear ownership before expanding governance programs.

• View data engineering as a business capability. Invest in reusable data products, common metrics and governed access that reduce organizational friction while accelerating AI adoption.

• Master the fundamentals before pursuing advanced AI. Reliable ingestion, ontology, quality checks, lineage and version-controlled pipelines remain the foundation of successful AI.

The Foundation Beneath the AI Revolution

The AI conversation often begins with what is visible: the models, the interfaces and the moments when machines appear to reason. But the real competitive advantage is being built somewhere less visible: in the systems that determine what information AI can access, trust and act upon. Data engineering may not generate the headlines, but it quietly decides whether an organization’s AI ambitions become business capabilities or expensive experiments.

As enterprises move toward more autonomous AI, the margin for uncertainty will continue to shrink. The next generation of AI leaders will understand that data is not just fuel for intelligent systems—it is the architecture behind them. What once happened behind the scenes is now becoming a defining factor in which organizations can move faster, make smarter decisions and scale AI with confidence.