How many customers does your company actually have? It depends on who you ask.

Business Insider

4 days ago

How many customers does your company have?
Sounds like a simple question. But check your CRM, CDP, or ERP systems, and the answer is anything but clear. There's a very good chance they each give a different response.
You could try asking your enterprise LLM. But your LLM probably doesn't know the answer either.
Four systems, four responses, but no definitive answer. It's not just about mismatched numbers. Many systems operate on different definitions of data sets. Sales, marketing, operations, and finance could all define and track customer data differently, leading to conflicting and unactionable information. What are you going to tell the CEO when they ask the question?
This is the enterprise data mess in a nutshell. It's exactly the type of garbage being pumped into enterprise LLMs today — and the top reason why organizations don't trust results and find their pilots stuck in first gear. AI doesn't magically resolve bad data. It papers over the gaps with confident-sounding, but often completely false, information.
AI's sluggish, disappointing start
Enterprise leaders rushed into AI expecting immediate results and rapid transformation. Instead, many find themselves stuck with disappointing pilot projects, unreliable insights, and growing impatience. Why? Because AI can't fix broken data. AI amplifies the weaknesses in enterprise data that most organizations have suspected but ignored for decades.
Reltio
Research conducted by TrendCandy and commissioned by Reltio, a real-time data intelligence company, found that most enterprise AI initiatives fail or stall because of poor data quality, lack of trust and explainability, and poor integration with existing systems.
Fixing data is tedious, and very few with high career aspirations want to own it. It is the graveyard of enterprise IT, according to Mihir Shah, former CIO at a Fortune 500 financial services firm. Half-baked, abandoned, and lost in the shuffle of acquisitions, reorgs, budget cuts, and ever-evolving priorities. Why? Because short attention spans drive short-term thinking. You can't build a lasting foundation that way. Instead of investing in what's foundational, teams get pulled toward whatever use case promises the quickest ROI.
Quality, trusted data comes first, AI second
The inconvenient truth is this: Your AI will only be as good as your data. Yet, countless businesses jumped straight into AI projects without addressing foundational data issues left unresolved from earlier technology waves like ERP, CRM, and the cloud. Organizations became experts at collecting data, but putting it to use for operations and analytics has been extremely challenging for a long time.
This isn't a failure of AI technology or a lack of data itself. It's a failure of sequence.
Most enterprises still have significant gaps in their data maturity —gaps that can't simply be patched over by throwing AI tools at the problem. Companies that recognized this early, like McDonald's, have quietly invested heavily for decades in meticulous data practices, positioning themselves to fully exploit the power of AI today.
McDonald's data transformation has improved customer experience, streamlined operations, and enabled smarter decisions. Personalized offers, mobile ordering, and digital kiosks boost convenience and loyalty. AI-driven supply chains and real-time performance tracking reduce waste and wait times. Data analytics helps McDonald's anticipate customer needs and market trends, while cloud, AI, and machine learning power the agility and innovation needed to stay competitive.
Others who skipped this tedious but essential step of data readiness are now forced to shift into reverse in a frustrating retreat.
We are in a new age, we have left the industrial age behind, and have entered the age of intelligence. Organizations that are not prepared for the new age will be left behind.
AI starts with intelligent data
Today, CEOs, CIOs, and everyone in the C-Suite are under enormous pressure. Boards expect immediate AI-driven returns, and Wall Street analysts reward companies that can demonstrate tangible AI outcomes. However, transforming decades of siloed, fragmented enterprise data into AI-ready information in a matter of months is simply not feasible. Real transformation takes patience, dedicated resources, and a cultural shift to treat data excellence with the seriousness it deserves.
The bottom line? AI success doesn't start with algorithms or cloud computing — it starts with clean, trusted data. New rules are being written today, specifically, new data rules for enterprise AI.
Enterprises must slow down to speed up, investing first in data accuracy, governance, speed, and quality. Those who grasp this fundamental truth will leave their competitors behind. Those who don't risk staying stuck in neutral, spinning their wheels.