2025 could prove to be a pivotal year in AI data readiness if a newly released survey from data infrastructure provider NetApp is any indication. According to the survey, most enterprise executives believe there must be an “unprecedented” amount of investment in AI and data management for companies next year.
That high level of investment remains necessary despite two-thirds of the 1,300 global tech and data executives surveyed stating that their data is fully, or mostly, already optimized for AI. That finding is much more optimistic toward enterprise AI data readiness when compared to several recent surveys that asked similar questions.
Krish Vitaldevara, senior vice president of product platforms at NetApp, told TechStrong AI that the NetApp survey reflects the current state of enterprise IT, where the focus over the past year has been experimentation and foundational preparation for AI. “However, this is still early days, and businesses face a pivotal moment as we approach 2025—a year many view as critical for realizing returns on AI investments. This urgency is particularly evident in regulated industries such as health care and financial services, leading the adoption curve,” Vitaldevara said.
Vitaldevara added that such industries benefit from superior data hygiene, allowing them to leverage accelerated computing. “Optimizing data for AI is no small feat—it requires a robust, intelligent data infrastructure capable of managing complex workflows, unifying data silos, and enabling seamless integration across hybrid environments, Vitaldevara said.
“For businesses to truly unlock AI’s potential, their data must be managed in ways that prioritize performance, governance and accessibility,” Vitaldevara added.
Successful AI Requires Data Harmonization
The NetApp survey found that data unification, or the integration and harmonization of data from multiple sources, is essential for 79% of respondents to deliver good AI outcomes. The technology executives surveyed are also confident in their AI capabilities: Only 24% of those surveyed believe that they will not reach their AI goals for 2025 due to data siloes.
The challenges enterprises face in harmonizing their data are quite high. Bentzi Aviv, global head of fintech solutions at technology services provider Amdocs, said the companies he’s observed in their AI implementations have a long way to go to get their data readied for AI success. He cited challenges in the banking sector. “Getting data right is priority one,” Aviv told Techtrong AI.
“It’s not all because of AI. Regulatory requirements are coming from around the world, whether that’s the US, European, or Chinese authorities and all the associated data security and privacy challenges,” he explained.
Citing the challenges a typical bank faces today, Aviv detailed how data is stored across different siloes, especially customer data. “There are many agreements associated with banking customers. There’s their deposit agreement, mortgage agreement, loan agreement, insurance agreement. Every service with a bank becomes an agreement, and each customer can be viewed as an association of agreements,” he explained.
The data harmonization challenge for banks is that each of the systems associated with each of these agreements store their data much differently, he explained. The way that a typical mortgage system represents a customer is entirely different than how you will see a customer in a deposit system, a lending system or an insurance system. The attributes are all different,” Aviv explained.
“Managing and getting this data ready for AI is not just about storing it. It’s not just about accessing it. It’s also about standardizing it by creating a common language within the bank’s systems that can combine all these various pieces of information from different silos in a way that it’s readable and represents a 360-degree view of a customer,” Aviv said.
AI Data Readiness Surveys Show Mixed Results
Recent surveys attempting to explore AI readiness within enterprises have mixed conclusions. Consider a recent survey of 362 professionals (who participate in AI decision-making) from Harvard Business Review Analytic Services, conducted on behalf of Microsoft and Profisee. That survey found that 54% of respondents do not agree their organization has the data foundation required to succeed in the new AI era. Another survey from the Center for Applied AI and Business Analytics at Drexel University’s LeBow College of Business found that only 12% of organizations reported that their data was of sufficient quality and accessibility to implement AI effectively.
Most expect considerable business investment into data readiness tools, regardless of current data readiness. Precedence Research predicts that the global data preparation tools market size will grow to $31 billion by 2034, up from $6 billion in 2023.
Data and AI Consumption Expected to Challenge Sustainability Efforts
Survey respondents believe that increased demand for AI fuels rising energy consumption, which could create a tipping point for sustainability efforts. According to the NetApp survey, 34% of global tech executives reported AI adoption will drive significant shifts in corporate sustainability processes. Globally, 35% of companies rank sustainability as very important when choosing a flash storage vendor, yet 50% of global tech execs believe AI-driven data use significantly contributes to their companies’ carbon footprints.
“The rapid pace of AI adoption is outstripping the capacity of traditional data center infrastructure, creating significant challenges around energy efficiency, rack density and sustainability. Modernizing infrastructure is a critical first step for organizations,” NetApp’s Vitaldevara said.
Vitaldevara cited flash storage technology as offering currently high-capacity appliances that require far less power and space compared to legacy disk-based solutions. “Consolidating older systems with flash appliances not only reduces energy consumption but also alleviates cooling demands—both key factors in improving overall data center sustainability,” Vitaldevara said.
Finally, 33% of global tech executives say AI adoption will drive new government energy policies and investment efforts. In these aims, Vitaldevara said governments can advocate for and incentivize data center modernization efforts that include energy-efficient infrastructures. “Policies encouraging renewable energy adoption and investment in green data centers can also help mitigate the environmental impact of AI-driven workloads,” he said.
Finally, Vitaldevara advised IT teams to continuously monitor and optimize their compute and storage resources to maximize efficiency. “Beyond hardware, intelligent data infrastructure plays a pivotal role by enabling enterprises to manage and process data more effectively, which reduces unnecessary resource usage while supporting the demands of modern AI workflows.”
