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NetApp CEO George Kurian said at NetApp Insight 2026 that AI adoption succeeds when executives treat it as a leadership and business transformation rather than a technology project. He argued vendors must provide the storage and data-management foundations that allow organisations to move pilots into production. Those remarks come against persistent adoption problems: an MIT study in August 2025 found 95% of AI projects were failing, and other research shows many pilot-stage initiatives stall. This account was first reported by IT Pro and summarises Kurian’s framing, NetApp’s product moves at Insight and the adoption gaps vendors are being asked to fill.
Like all transformations, AI transformation is a leadership and business transformation.
George Kurian, NetApp CEO
Key takeaways
- Kurian’s framing: George Kurian, NetApp CEO, said that “AI transformation is a leadership and business transformation.”
- Project failure rate: An MIT study in August 2025 found 95% of AI projects were failing.
- Data quality and silos: IDC research identified data quality as the most important factor for success for more than half (52%) of companies, and seven-in-ten leaders cited data silos as a top challenge.
- NetApp product moves: At NetApp Insight 2026 the company announced AI-powered storage management, predictive maintenance and the ability to integrate customer LLMs via the NetApp Console and ONTAP.
Table of contents
- Key takeaways
- Why Kurian calls AI an organisational, not just a technical, change
- The data and infrastructure gaps that stop pilots becoming production
- What NetApp announced at Insight 2026 and how it ties to Kurian’s point
- Where organisations need to act to turn vendor capabilities into ROI
- How this could play out
- What to be careful about
- Frequently asked questions
Why Kurian calls AI an organisational, not just a technical, change
George Kurian framed AI adoption as needing executive leadership and organisational change rather than merely new tools. He argued that, like the desktop computer or the internet before it, AI must be driven by business strategy and governance to deliver measurable outcomes.
Kurian said that “AI transformation is a leadership and business transformation.” That phrasing places accountability for adoption, resourcing and outcomes at the top of the company rather than solely with IT teams or data scientists.
Those figures echo broader evidence of stalled projects: an MIT study from August 2025 reported a 95% failure rate for AI projects, and PwC analysis shows only one-in-eight CEOs reporting cost or revenue improvements. Kurian cited those industry indicators to stress that technology alone does not convert pilots into production.
The data and infrastructure gaps that stop pilots becoming production
Adoption hurdles reported by IDC surface repeatedly in Kurian’s remarks: poor data quality and fragmented storage are practical blockers for models in production. IDC’s research identified data quality as the single most important factor for success for more than half (52%) of companies, and it found seven-in-ten business leaders see data silos as the biggest challenge.
Those gaps force engineering teams to spend months on data prep, lineage and access controls instead of on model improvements. Kurian pointed to these operational choke points when he described vendors’ role: to provide storage, visibility and management tools that reduce the manual labour of readying data for AI.
The implication for CIOs is clear: unless data pipelines and storage are reworked to support model retraining, observability and production latency requirements, pilot projects will remain experiments. Kurian framed vendor platforms as part of the solution but reiterated that organisational processes must change to use them effectively.
What NetApp announced at Insight 2026 and how it ties to Kurian’s point
NetApp presented specific product moves aimed at the infrastructure problems Kurian described: enhancements to ONTAP, expanded capabilities in the NetApp Console, and new AI-powered automation for storage management. The announced features include predictive maintenance insights and automated remediation, plus the option for customers to integrate their own large language models into the console.
ONTAP was presented as the data-management foundation intended to break down silos and improve visibility across on-premises and cloud storage. NetApp says these tools are meant to reduce the operational overhead that stalls AI initiatives, enabling teams to shift effort from plumbing data to delivering business use cases.
Kurian and NetApp positioned those capabilities as enablers rather than cures: he noted that LLMs and vendor tooling can help solve data-prep challenges, but “it doesn’t mean that because the technologies are there that it magically gets solved.” The company’s demonstrations are built around use-cases and best-practice playbooks aimed at customers struggling to move to production.
Where organisations need to act to turn vendor capabilities into ROI
NetApp’s message at Insight 2026 narrows the problem set a CIO must address: align leadership metrics to production outcomes, invest in data-quality programs, and adopt platforms that centralise management. Kurian said NetApp works with clients to show best practices and successful use-cases from high-performing organisations.
Practically, that means executives must set measurable KPIs for pilot-to-production conversion, fund sustained data engineering work, and require vendors to demonstrate how new features reduce recurring operational tasks. The reporting cited shows executives are impatient: PwC analysis found only one-in-eight CEOs reporting cost or revenue improvements from AI investments.
The balance Kurian proposes is operational plus organisational: adopt vendor tools such as ONTAP and the NetApp Console to remove technical drag, while changing governance and incentives so teams finish projects that deliver measurable business value.
How this could play out
The case for
- Vendors that deliver integrated data-management platforms can materially reduce the operational time spent on data prep and storage operations, lowering the barrier to production.
- Demonstrations and playbooks from companies like NetApp may accelerate adoption among enterprises that lack in-house data engineering scale by providing tested blueprints.
The case against
- Even with improved tooling, persistent data-quality and governance issues could keep a large share of projects in pilot without sustained executive sponsorship.
- If vendors promise easy wins without demanding organisational change, adoption rates and ROI statistics cited by MIT and PwC may remain weak.
What to be careful about
- Over-emphasising vendor capabilities may let leadership defer internal governance, leaving core organisational blockers unaddressed.
- Tooling that integrates customer LLMs raises operational and security complexity that can create new production risks if not governed correctly.
- Enterprises that treat NetApp-like features as a turnkey fix risk under-investing in the data engineering and process changes that drive production outcomes.
The bottom line
George Kurian’s message at NetApp Insight 2026 reframes a familiar technical debate in organisational terms: vendors can and should build the data and storage foundations that make AI feasible, but leadership must set strategy, KPIs and governance to realise value. The industry metrics cited — notably an MIT finding that 95% of projects fail and IDC’s emphasis on data quality and silos — show why Kurian stresses playbooks and customer collaboration. For CIOs the takeaway is operational and behavioural: adopt platforms that reduce manual data work, but pair them with executive commitment to move projects from pilot to production.
What to watch
- Watch for NetApp to publish general availability dates and detailed release notes for the new AI-powered storage features; no date has been set.
- Watch for customer case studies and playbooks showing pilots moved into production using ONTAP and the NetApp Console; no date has been set.
Frequently asked questions
What did George Kurian say about AI transformation?
George Kurian said that AI transformation must be led by business and leadership, not treated as purely a technology change; he framed it as a leadership and business transformation and warned that tools alone do not make projects succeed.
What are the main barriers to AI adoption cited in the coverage?
Industry research cited includes an MIT study from August 2025 reporting a 95% AI-project failure rate and IDC findings that more than half (52%) of companies name data quality as the most important success factor while seven-in-ten leaders point to data silos as the biggest challenge.
Which NetApp products or features were highlighted at Insight 2026?
NetApp emphasised ONTAP for data management and the NetApp Console for centralised infrastructure control, and announced AI-powered storage management features including predictive maintenance, automated remediation and the ability for customers to integrate their own large language models.
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