The Need for Data Freedom in the AI Era

AI has quickly evolved from a buzzword to the backbone of businesses across industries. According to McKinsey, 78% of businesses are using AI in at least one business function, marking a 23% increase from 2023 and further proof that adoption is no longer the main hurdle in AI success. Yet, as demand skyrockets, so does frustration – siloed systems, inaccessible data, and limited visibility are hindering many from making the jump from using the technology to deriving meaningful value from it. The companies breaking through all share one thing: data freedom.

Even the most advanced AI models are all but useless without access to high-quality data. Yet too often, business leaders are realizing that their most valuable data sources are held by vendors under lock and key. Data “lock-in” practices, in which contracts and punitive fees for access and extraction restrict a company from leveraging its own data are increasingly commonplace. Not only does lack of access to high-quality training data create real compliance risk and erode the trustworthiness of outputs, it also limits AI’s potential – leaving the bulk of its business value on the cutting room floor.

Successful enterprise AI adoption ultimately comes down to data freedom, more so than even model selection or budget, and the battle over data access is already well underway. Companies like AT&T and Atlassian are making headlines for data throttling and limited functionality. It’s time for a new industry standard, where firms can access and use their own data freely, without penalty or restrictions.

What Is Data Freedom?

Data freedom starts with data accessibility. Organizations should be able to access their data on their own terms. Real freedom goes deeper than that, to full transparency around how, when, and where data is moved and stored. At the technical level, this means cost-efficient and fast data export so that organizations can move their data when they need to – from mandatory purposes like audits and legal discovery to streamlining operations when changing vendors.

Data freedom isn’t negotiable. It’s a foundational principle and without it, AI will remain stuck in the toddler phase – curious but limited, and never able to learn, adapt, and thrive with maturity to drive meaningful business outcomes.

The consequences of limited data access, restrictive data migration fees, and the current industry norm of vendor lock-in are more than just an inconvenience – they present very real compliance, operational, and innovation risk. This rings particularly true in highly regulated industries like financial services firms. For banks, investment firms, or insurers, data is more than just a record of activity; it’s protection from disputes and concrete proof regulatory compliance.

Gatekeeping data by forcing firms to depend on a single vendor’s proprietary technology or systems hinders organizations in their ability to act on data-driven insights when it matters most. As a result, compliance risk skyrockets and business decisions suffer. Everything from audits to regulatory reviews slows down without the free flow of data, leaving firms less prepared and less agile should a compliance threat arise. Operational inefficiencies that result from manual extraction and data management processes also drain staff resources and increase the likelihood of human error or inaccuracies.

The effects of limited data access are felt both in day-to-day challenges, as well as long-term planning and innovation. As technology is constantly changing, it’s crucial that data keeps pace with modern tools, especially AI. When organizations cannot easily integrate their data with more advanced technologies, adoption slows to a halt – and the barriers to continued transformation grow.

Additionally, when the cost to access data is high, those funds are often taken away from other valuable business functions, such as AI R&D. If organizations are forced to re-prioritize projects, or even abandon cutting-edge AI deployments, they lose out on a significant competitive edge – not to mention the potential productivity, cost savings, and modernization benefits that come with AI.

Data Determines AI’s Continued Development

AI has moved beyond the hype cycle into more practical use cases, and adoption is only continuing to soar. So too is the pressure for business leaders to deliver tangible ROI and demonstrate the strategic value of those same AI projects. In today’s landscape, however, that’s often easier said than done.

Just this year, the share of companies abandoning AI initiatives hit 42%, up from 17% the year before. At the same time, data availability and quality have consistently emerged among the top challenges in AI implementation – a finding that holds true across AI maturity level, according to 34% of leaders from low-maturity and 29% from high-maturity organizations.

AI lives and dies by data. If the input is strong, the output will be as well, making seamless access to high-quality data one of, if not the most, important factors in whether an AI project is successful. That’s not to say that model selection, proper funding, or leadership buy-in don’t matter, but when it comes to driving value across compliance, discovery, risk mitigation, and operational workflows, AI is dependent on data.

And yet, as long as firms are stuck operating in an ecosystem that perpetuates limited functionality, costly data migrations, and unnecessary disruptions, the advancement of AI technology stands little chance. Accurate, accessible, and modernized data powers quicker development, better training, and more secure outputs while also encouraging innovation, making data freedom the non-negotiable solution to driving more advanced AI capabilities.

Compliance Risks and Why Data Is the Solution

For 45% of companies, concerns about data accuracy or bias sit top of mind, while 40% cite privacy as a top issue and barrier to AI implementation. Organizations can’t fix what they can’t see, so when visibility is limited, acting quickly in response to an issue or inaccurate output becomes a steep task.

Transparency also builds trust, which in turn drives compliance. By prioritizing their data, business leaders are better equipped to overcome these concerns. An advanced tech stack offers little value alone. It is strong data and visibility that power technological growth, both of which begin with clear, comprehensive data governance policies and free-flowing access.

Data freedom empowers enterprises in future-proofing compliance strategies by removing cost and access hurdles and driving greater flexibility, adaptability, scalability, and innovation. The trickle-down effect of a strong data foundation is clear. When a firm starts strong with high-quality inputs under their own control, audits, investigation into security flaws, and the ability to identify areas of improvement all become exponentially easier.

Beyond the technology itself, cross-functional collaboration and external partnerships are also a key consideration. Data, compliance, and IT teams must work together, sharing expertise to foster a culture of continuous learning and knowledge sharing. This ensures business leaders stay atop the latest shifts in regulation and data control requirements, an essential, but difficult task given the uncertainty of the AI regulatory landscape in recent years.

Data is everything in business, making its full access and control the real strategic advantage, regardless of industry, enterprise maturity, and beyond. With vendor lock-in, costly fees, and uncontrolled data posing significant, often overlooked, risk, the focus must shift to forward-looking solutions that restore control to firms and open the door to accurate, trustworthy AI. By empowering teams in mitigating risk, optimizing operations, and encouraging confidence in navigating an evolving regulatory landscape, data freedom stands as the next step in bringing peace of mind to the organization and user long-term.