2026 Is the Year of the Customer

Why leading enterprises treat data quality as a prerequisite for agentic AI

2026 is the year data quality graduated from an IT ticket to a boardroom strategy. In Deloitte’s most recent Chief Data and Analytics Officer survey, 61% of CDAOs said improving data quality and access was key to the success of their AI and agentic AI initiatives. As enterprises rush to adopt agentic AI and manage sprawling hybrid clouds, the need for high-quality data has never been higher. The world’s most customer-centric brands, including ADP, Equifax, and Nationwide, are treating data quality as a product to earn trust, scale AI, and deliver the customer experiences their businesses depend on.

The agentic enterprise runs on data it can’t yet trust

For years, a bad data point meant a wrong number on a dashboard that a human eventually caught, questioned, and corrected. That safety net is disappearing. When an AI agent acts on bad data, the failure isn’t caught before it does damage. It’s silent, and it happens at machine speed. A pricing agent fed stale data prices a product wrong, instantly, across every customer who sees it. A churn model trained on incomplete data misreads which customers are actually at risk. A supply chain agent acting on a distribution shift it doesn’t recognize makes a decision that compounds before anyone notices.

This isn’t a hypothetical for some future date on the calendar. It’s a 2026 problem, and the numbers back that up:

• 62% of enterprises are actively experimenting with AI agents (McKinsey)
• 23% expect to achieve full deployment of agentic AI within the next 12 months (IDC)
• 50% of business workflows are projected to be augmented or automated by AI agents by 2027 (Gartner)

Every one of those agents will be making decisions on top of a company’s existing data infrastructure. For most enterprises, that infrastructure was never built with the assumption that a machine would be the one reading it.

Three enterprises, three proof points

The companies furthest along in the agentic AI transition aren’t skipping data quality to get there faster. They’re treating it as the gate they have to pass through first.

Nationwide manages over 13,000 databases, roughly 5,000 of them in production, carrying data that ultimately feeds regulatory reporting. Before Anomalo, the marketing team alone maintained more than 3,000 custom business rules just to catch known issues. And those rules only ever caught what someone had already thought to look for. Mike Randall, Director of Enterprise Data Governance at Nationwide, put the gap plainly:

“All data programs have checks that help us find known issues. What Anomalo brings is that it finds issues we’re not looking for.”

Mike Randall has spent more than 30 years in data quality at Nationwide, watching the discipline move from terminals and printed policy books to AI. He described the pace of the Anomalo rollout as some of the fastest movement he’d seen at the company: once the team stood up monitoring on their most important analytics tables, they were up and running within months.

Equifax sits at the center of the U.S. credit system, maintaining information on more than 3.6 billion tradelines with over 1.6 billion updates a month, all governed by the Fair Credit Reporting Act and a stack of state and federal consumer protection laws. When your data determines whether someone qualifies for a mortgage or a job, “mostly accurate” isn’t a tier of service, it’s the whole point. Nick Oldham, USIS Chief Operations Officer at Equifax, frames it as a matter of responsibility, not just infrastructure:

“At Equifax, we play an important role in the financial lives of consumers and we take that responsibility very seriously. When our data informs decisions that affect people’s financial lives, accuracy and trust are not optional. They are foundational.”

Equifax moved from initial discovery to a live, alerting production environment for its USIS consumer credit ecosystem in roughly 12 months, well inside the two-plus-year timelines that typically define enterprise deployments in financial services. Every Tier 1 data asset that supports an AI model at Equifax is now subject to automated quality checks before that model ever gets to make a decision.

ADP processes payroll for 1 in 6 American workers and $3.1 trillion in U.S. payroll and taxes annually. There is no more literal version of “the customer experience holds up or breaks based on this data” than whether someone’s paycheck is correct. ADP’s data team started where most enterprises do: 700 individual data quality checks, built one by one, reviewed through a slow, manual cycle between data stewards and a centralized programming team. Kristin Hlavinka, Director of Enterprise Data Governance at ADP, described why that model stopped working:

“We needed something smarter, more powerful, and more automated. That’s where Databricks and Anomalo come into play.”

Within months of integrating Anomalo into their Databricks environment, ADP scaled from those 700 manual checks to more than 16,000 daily machine-learning-powered validations. This was a jump in coverage that would have been operationally impossible to sustain by hand, and one Hlavinka has pointed to as the foundation that let ADP’s data science and Gen AI initiatives move forward with confidence.

Why “customer-centric” now runs through AI

Nationwide’s promise to policyholders, Equifax’s promise to consumers, and ADP’s promise to workers all used to be delivered primarily through people: an underwriter reviewing a claim, an analyst pulling a credit file, a payroll specialist double-checking a run. Increasingly, those promises are delivered through AI agents. The agents summarize claims, flag credit anomalies, and validate payroll data before a human ever sees it.

That shift changes what “customer-centric” actually means operationally. It used to mean good service design and responsive support. Now it also means the data quality layer sitting underneath every agent that touches a customer’s claim, credit file, or paycheck. Data quality isn’t a backend metric anymore, quietly tracked by a data team no one outside of IT ever hears from. It’s the mechanism that determines whether the customer experience holds up or silently breaks in a way nobody notices until the customer does.

The market agrees: data quality has become AI infrastructure

This isn’t just an internal conviction at Anomalo. It’s increasingly the market’s conclusion too. Anomalo was recently named a Contender in the Forrester Wave for Data Quality Solutions, and Forrester’s own research backs up exactly what these three companies discovered the hard way: the biggest blocker to scaling AI isn’t model performance. It’s data readiness and trust. Being named in Forrester’s Wave for Data Quality Solutions is external confirmation that this is the right problem to be solving right now, not a legacy concern enterprises should be moving past.

That validation extends beyond analyst research into the customers actually using the platform day to day. Anomalo customers rated the platform 4.7 out of 5 in Gartner Peer Insights’ 2025 Voice of the Customer report for Augmented Data Quality Solutions, one of the highest willingness-to-recommend scores of any vendor evaluated. 71% of Gartner Peer Insights reviews rated Anomalo 5 stars, and more than half came from companies with over $10 billion in annual revenue. That breadth matters as much as the score itself: the reviewers span banking, insurance, media, retail, and IT services, which means this isn’t validation from one industry with unusually forgiving requirements. It’s validation that holds up across the kinds of complex, large-scale data environments where “garbage in, garbage out” carries the highest stakes.

What “AI-ready” data quality actually requires

Nationwide, Equifax, and ADP arrived at the same underlying approach from three different industries and three different starting points. That convergence is a useful signal for any data leader trying to figure out what “AI-ready” actually requires:

• No rules or thresholds to maintain. AI learns what “normal” looks like for your data directly from the data itself, rather than requiring a team to write and maintain thousands of manual rules that only catch what someone already thought to check for.
• Content-level depth, not just metadata checks. Knowing that a table arrived on time isn’t the same as knowing whether the values inside it are correct. Real AI-readiness requires monitoring the actual content of the data, not just its arrival schedule.
• Agentic root cause investigation, not just alerting. An alert that tells you something changed is a start. What a data team actually needs is a system that investigates why, and delivers that answer before someone has to go digging.
• Coverage across every cloud and warehouse, not a single platform. Large enterprises don’t run on one system. A monitoring approach that only sees part of the data estate can only build trust in part of it.
• Compounding organizational memory. Every correction a team makes should make the system smarter, not force them to re-teach it the same lesson next quarter. The system should get more accurate over time, not just more familiar.

If a data quality approach can’t check all five of these boxes, it’s already behind what the AI transition requires today. It’s called Self-Driving Data.

The window is shorter than most realize

Anomalo customers such as Nationwide, Equifax, and ADP didn’t wait for agentic AI to mature before building the data foundation underneath it. They built the foundation first, which is precisely why they’re positioned to move confidently now. Organizations doing the same work today are building a compounding advantage: every correction, every well-monitored table, every automated check makes the next AI initiative faster and safer to deploy. Organizations waiting for AI initiatives to force the issue are instead accumulating AI risk at scale, one ungoverned data source at a time, without realizing it until an agent acts on bad data in production.

The gap between those two positions is where 2026 will actually get decided for most data leaders. If you want to see why the market is reaching the same conclusion, read Anomalo’s recognition in the Forrester Wave for Data Quality Solutions and Anomalo’s 2025 Gartner Peer Insights Voice of the Customer results for yourself. And if you’re ready to see what AI-ready data quality looks like on your own data, request a demo.