How much do you know about AI functionality? While AI has made incredible strides, many do not understand its capabilities, limitations, and potential impact. A prevailing myth is that AI “thinks” and “understands” just like people do. Here’s the truth:
AI doesn’t truly understand anything—it recognizes patterns in data based on training. It can mimic human conversation or decision-making but has no consciousness, awareness, or emotional depth. It’s powerful but not sentient.
Here’s another truth: AI is only as powerful as the data that fuels it.
“No AI tool will deliver if your data is a mess or simply not available,” Olga Traskova, VP of RevOps at Birdeye. “Garbage in, garbage out–especially when you’re asking AI to make decisions or automate processes.”
Eighty-three percent of companies claim that AI is a top priority in their business plans. However, once implemented, only one-third of executives reported seeing a significant return on investment.
Meanwhile, two out of three executives surveyed said generative AI adoption has led to division between teams, while almost half (42%) reported that adopting AI “is tearing their company apart.”
A surface-level approach to data won’t cut it for companies looking to scale intelligent systems and drive real value from AI investments. That’s why leading AI companies are rethinking how they manage company data—from governance to infrastructure—to unlock the full potential of machine learning and automation.
Top Challenges in AI Data Management
Adopting AI can feel like unlocking a superpower—but only if your data is up to the task. Many companies run into roadblocks not because the AI tech isn’t advanced enough, but because their data foundation is shaky. Disconnected systems (aka data silos) make it difficult to provide the full picture AI models need to learn and perform well. Add to that the chaos of inconsistent data formats and unstructured inputs, and you’re left with serious friction during model training. It’s like trying to train a world-class athlete with broken equipment.
And when it comes to speed, AI doesn’t wait. Applications in industries like finance, healthcare, and logistics demand real-time processing, not yesterday’s batch reports. On top of all that, weak governance leaves companies vulnerable to privacy risks and compliance headaches. These challenges make one thing clear: companies must rethink and modernize their data management strategies. Aligning data operations with AI functionality isn’t optional—it’s essential for unlocking real value and staying competitive.
“So the real question is: Can AI actually solve foundational problems like data quality and availability?” Olga asks. “We spend so much time chasing the next shiny AI feature. But maybe the real game-changer is AI that collects, prepares, and cleans your data — not just acts on it.”
How Leading Companies Are Shifting Their Data Strategies
To begin the process of modernizing and aligning data management AI functionality, organizations can start by shifting their focus to four key pillars:
- Prioritizing data quality over quantity
- Adopting real-time data pipelines
- Implementing unified platforms that break down silos
- Embedding ethical governance
These changes aren’t just about keeping up—they’re about unlocking AI’s full potential to drive smarter, faster, and more responsible decisions across the business. Here’s how.
Prioritizing Data Quality Over Quantity
The future of AI optimization doesn’t belong to the company with the most data—it belongs to the company with the smartest strategy to manage it. For forward-thinking leaders, that means shifting from big data to better data.
Consider the financial advantages of better data. For instance, following up on a bad email address or phone number wastes valuable time for SDRs. Sources estimate that SDRs waste an average of 27% of potential selling time following bad data.
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Managing large volumes of bad data costs organizations around $15 million annually. Some companies watch 25% of their potential revenue vanish due to bad data. Here’s the real kicker: Over half (60%) of these companies don’t even notice how much waste they are generating with their bad data.
Adopting Real-Time Data Pipelines
AI systems powered by live, clean data streams anticipate, adapt, and act within dynamic go-to-market. If your data can’t move at the speed of business, your AI can’t either.
Whether it’s AI for capacity planning or territory management, only real-time insights can fuel instant decision-making. Forrester’s research found that 73 percent of surveyed companies agree that access to real-time data analytics is crucial to securing a competitive advantage.
One study found that organizations using real-time data analytics are five times more likely to make decisions at a pace that keeps them competitive in go-to-market.
However, less than half (42%) have implemented these capabilities. If your data isn’t live, your AI is flying blind—and your competition is already two steps ahead.
Implementing Unified Data Platforms
Clean data is the backbone of any successful AI strategy—especially for companies shifting to a Revenue Operations (RevOps) model, where collaboration across sales, marketing, and customer success is key.
In a RevOps environment, AI is often used to unify forecasts, personalize customer journeys, and optimize performance across the funnel. But if your data is outdated, incomplete, or inconsistent, those AI insights become unreliable—or worse, misleading.
Seventy-six percent of CFOs recognized that their organization will struggle to meet objectives if they lack a single source of truth across departments, according to Accenture.
As companies embrace AI to streamline their go-to-market processes, clean data becomes a strategic differentiator. AI models need structured, high-quality data to accurately predict outcomes, identify trends, and deliver actionable recommendations. Without it, automation efforts stall, customer experiences suffer, and trust in shared dashboards erodes. For RevOps to thrive, companies must prioritize data hygiene. When the data is clean, the collaboration flows, and AI can actually deliver on its promise.
Emphasizing Ethical AI Through Better Governance
Companies are finally waking up to the urgent need for ethical AI. Studies show over half (54%) of business leaders are concerned about AI bias. Among surveyed companies that track the AI impact on company data, 36 percent experienced AI algorithm bias that impacted lost revenue, customers, and employees.
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Organizations are now adopting ethics-by-design frameworks, building algorithmic transparency directly into their pipelines to ensure that AI decisions are explainable, trustworthy, and aligned with real-world values.
This isn’t just about compliance—it’s about credibility. Customers and regulators are demanding responsible AI that reflects integrity, not bias. That’s why leaders are shifting from vague AI ambition to clear data ethics frameworks that guide everything from data sourcing to model output.
To optimize AI, companies must go beyond just training models and reimagine how they manage their data. Organizations can turn their data from a liability into a strategic advantage by investing in better data quality, real-time processing, moving data to a centralized location, and ethical governance.
