Latest news with #MaggieLaird
Forbes
28-07-2025
- Business
- Forbes
Why Data Fitness Is The Foundation For AI Success
Maggie Laird is the President of Pentaho, Hitachi Vantara's data software business unit. We're entering a new era of enterprise automation, one where intelligent agents can analyze, decide and act independently. But while the industry's imagination races forward, infrastructure still lags behind. Ninety-three percent of enterprise IT leaders have implemented or plan to implement AI agents in the next two years, according to a recent survey by MuleSoft and Deloitte Digital cited by ZDNET. However, the survey also found that 95% of the IT leaders said they are struggling to integrate data across systems. Meanwhile, Gartner recently noted that, through 2025, 'poor data quality will persist as one of the most frequently mentioned challenges prohibiting advanced analytics (e.g., AI) deployment.' AI agents can only be as good as the data that feeds them. And most enterprise data environments are far from ready. They're fragmented. Opaque. Siloed by system, department, geography or format. Putting AI to work in those environments won't be transformative. That's automation built on sand. Enterprises are at a crossroads. Many are in the initial stages of deploying next-gen agents to boost productivity, streamline decision making and lower costs. But without a trusted, accessible and well-governed data foundation, those ambitions rest on shaky ground. AI agents don't just need data. They need data that is structured, contextualized, traceable and aligned to the organization's goals. Organizations must overcome a false sense of readiness. Think about what AI agents do: They generate signals, draw inferences and act. If they're trained on or use inconsistent or incomplete inputs, the decisions they make will reflect those flaws. While agents may move faster than humans, if they're working off half-truths or hidden assumptions, the consequences can quickly multiply. Thankfully, this isn't a technology gap. It's a data-readiness gap. And it needs executive-level attention. Over the past 20 years, my company has worked with some of the most data-intensive organizations in the world, from global banks and national defense systems to airlines, telecoms and critical infrastructure providers. Across these environments, four themes consistently separate those who scale well from those who stall and will be essential to an agentic world: 1. Build a unified, queryable data catalog. Most enterprises don't have a clear inventory of what data they have, where it lives or how it relates. A living, searchable catalog makes it possible for both humans and machines to understand and access what is available and use it responsibly. Organizations need to catalog both structured data and the rapidly growing pool of unstructured data, which makes up 80% to 90% of data today. Unstructured data is everything from videos to sales presentations to emails to social media posts that provide context for decisions. While this data will still likely live in different silos, a catalog is foundational to creating the data products that AI agents need to get a full picture of the business challenge they are addressing. 2. Operationalize data governance. Governance isn't about limiting access; it's about enabling trusted use. Yes, AI agents must know what data is relevant. But agents also must know how it was processed and what rules apply to its use. Think lineage, version control and explainability as baseline requirements. Every agent needs to know where the data came from, when it was changed, if it was changed, by whom and for what reason. For example, data may be scrubbed so voraciously that a person's middle initial gets removed in one dataset but not others. That can confuse an AI agent when, for example, gathering intelligence on whether a certain loan product would be good for a particular person. 3. Apply intelligent access controls. The data democratization that will drive agents doesn't mean letting every AI workload touch every dataset. It means giving the right agents access to the right data, under the right guardrails, for the right purposes. This requires policies that adapt to roles, risk levels and regulations. 4. Design pipelines for business relevance and scale. A smart pipeline isn't just fast; it's aligned. Different agents will need different datasets, formats, and levels of latency depending on the task. For instance, AI agents working on procurement will want and need access to different datasets than AI agents working on marketing. Once agents are working, monitor their success—or lack thereof—and make necessary changes in the data, the data pipelines and the access and control policies. Build data flows that can evolve with business needs and AI capabilities. Readiness is an ongoing practice, not a one-time project. Being 'ready for AI' isn't a one-time certification. It's a commitment to continuously refining how your organization collects, manages and mobilizes information. The companies that win with AI won't be the ones who adopt the most tools. They'll be the ones who do the most with what they already know and ensure their intelligence systems are built not just to automate, but to align. Make space for the agents of change, but first, build the conditions they need to succeed. AI is moving. Fast. The quality of your data will determine whether you're leading or watching from behind. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
05-06-2025
- Business
- Forbes
Getting AI-Ready In A Hybrid Data World
Maggie Laird is the President of Pentaho, Hitachi Vantara's data software business unit. If AI is the future, then data is the terrain. And most businesses are hiking in flip-flops. We all feel the urgency. Executives know that AI has the potential to drive exponential gains in productivity, insight and market leadership. But inside most organizations, the reality looks a lot less like ChatGPT magic and more like stalled pilots, costly proof-of-concepts and growing technical debt. Gartner predicts that through 2025, 60% of AI projects will be dropped because they are 'unsupported by AI-ready data.' What's more, 63% of organizations either lack proper data management practices for AI or aren't sure whether they have them, according to Gartner. And it's not just about volume or speed. It's about trust, structure and the ability to bridge hybrid environments without losing fidelity or context. Adopting AI today is a lot like installing a high-performance kitchen in a house with faulty wiring and sagging floors: flashy, expensive and ultimately unsafe. That's because AI doesn't magically fix data problems—it amplifies them. Many of the core data challenges that AI surfaces are issues companies have faced for years. Most businesses have data scattered across multiple environments: in the cloud, on-premises or a mix of both. Take customer retention. A company may have six streams of data tied to improving it. Some of the data is structured in columns and rows. But other critical data is unstructured—buried in email, PDFs or training videos. Most companies have mountains of unstructured data that hasn't been labeled, making it inaccessible to AI or incompatible with structured data. If a company wants AI to detect customer retention issues or trends, it needs all the relevant data to build an accurate picture. Without it, insights are skewed and incomplete—and the resulting decisions may be wrong. Imagine setting an AI agent loose to fix customer retention problems without reliable data. Who knows what could happen? Air Canada, for example, was recently found liable for a chatbot that gave a passenger incorrect information. The airline argued that it shouldn't be held responsible for what the chatbot said, but the court disagreed. The bar has been raised. 'Good enough' is no longer acceptable. AI is often designed to operate without humans in the loop, which means errors can go undetected. To get the right and best results from AI, organizations need a strong data foundation—what I call 'data fitness.' Here are four key indicators that your organization is data-ready for AI: Most organizations don't. Data lives in the cloud, on-prem servers, Slack threads, old Excel files and more. Being data-fit means you've cataloged what matters, labeled what's useful and can locate the most current version of any given asset—structured or unstructured. Your platform should connect across hybrid environments without needing to copy or move the data. To streamline this, start by clearly defining the AI use case. That makes it easier to identify what data to inventory. AI shifts decision-making to more people who don't have 'data' in their job titles. An analyst might know to exclude 'test region X' or adjust for seasonal bias in a report. Your AI agent won't. Neither will the product manager using a low-code interface to generate pricing suggestions. If your data isn't clean, governed and context-aware, you risk making high-speed, AI-driven decisions based on flawed inputs. That's not just bad insights—it's a serious risk. Different problems require different speeds. Historical data might be enough to plan next quarter's staffing, but real-time data could be essential for adjusting a flash sale or spotting inventory shortfalls. AI-ready platforms must operate across batch, real time and streaming—sometimes all within the same use case. Getting data-fit isn't just about cleaning up for the sake of it. It's about knowing which AI use cases matter, which data is needed and how much effort it will take to make that data usable. Sometimes, the return isn't worth it. That's okay—clarity saves time. But in many cases, once the investment is made, follow-on projects accelerate. Readiness compounds. Future efforts don't start from zero. AI isn't a tech project any longer, it's a business imperative. But without a solid data foundation, the tools don't matter. A home kitchen remodel inevitably involves hard, messy work—so does good data management. AI just makes it more urgent than ever. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
