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Harnessing AI responsibly for business transformation at scale

The critical role of digital infrastructure

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Ritu Jyoti
Ritu Jyoti
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In the last few decades, digitally transforming businesses have raced past the era of mainframe and client-server computing. Our current IT landscape, which IDC refers to as the age of multiplied innovation, was primarily shaped by cloud- and mobility-related investments. Low-cost manufacturing processes made computing technologies elastic and plentiful; mobility made computing ubiquitous. The internet made the cost of distribution of apps and data almost zero.

Digital transformation continues to bring about organizational, process, and technological changes. All of these changes are leading to unprecedented shifts in how the business operates, creates value, and positions itself as a differentiated entity to its customers, employees, and stakeholders. And while businesses will continue to innovate, the time has come for many firms to shift to the next phase, which IDC refers to as the age of Intelligent Automation. This new period of accelerated innovation will infuse artificial intelligence into every aspect of business, driving global spending on AI hardware, software, and services from $166 billion in 2023 to $424 billion by 2027 (Source: IDC Worldwide Semiannual Artificial Intelligence Systems Spending Guide, August 2023).

The age of Intelligent Automation is now

A key element of this innovation is generative AI, which will permanently raise standards of competition in nearly all industries as organizations leverage AI to not only predict analytic outcomes but automate content generation to drive even greater efficiencies and competitive advantage. Mainstream companies are now augmenting Large Language Models (LLMs) with private data to explore use cases that will expand labor productivity, personalize customer experiences, accelerate R&D, and even develop entirely new business models. Companies that fail to keep pace with this extremely fast-moving adoption cycle will fall behind or risk significant disruption.

This new era will be one of AI everywhere, in which AI underpins practically every aspect of business strategy. It builds upon decades of progress in enhancing our relationship with data and the degree to which actionable insights can be extracted from this data. It takes us to a place where we gain deep, real-time insights at scale to improve virtually every aspect of business. This transformation is hugely consequential to IT strategy and especially to data strategy. Without data, none of the favorable outcomes tied to AI use cases are possible.

Leaders will scale AI responsibly and efficiently

Digital IT infrastructure will continue to be the foundation for this next business transformation. The challenge will be dialing in the right IT strategy that navigates scale, governance, and social and environmental responsibilities to make our data AI-ready. Businesses that want to make their data AI-ready and lead in AI transformation must implement:

  • A scalable AI Architecture capable of securely streaming data in real-time to these new AI processes designed for a new generation of data consumers delivering highly strategic use cases.
  • Governance policies such as proactive data classification, and careful management of data access and distribution to ensure that data remains secure, and outcomes are trustworthy.
  • Auditable copies of AI models and associated datasets for traceability and diagnosis of model behavior over time.
  • Data security policies that eliminate the risk of personal information leaks, which can bring both legal and business reputation damages, secure confidential commercial data, and guard against malicious data theft.
  • Sustainability and efficiency practices to ensure that the infrastructure enabling AI initiatives is fully supportive of corporate carbon emissions and/or energy use goals.

For businesses to scale their AI initiatives responsibly and efficiently, they must ensure that each AI project is implemented on a fit-for-purpose, AI-ready data infrastructure: a flexible and fully integrated data pipeline and infrastructure that can compose the right datasets for any AI use case, wherever it is needed.

Like digital transformation, AI transformation is a technology-led business strategy that harnesses the power of AI to differentiate the business through new and innovative products and services, increased productivity, and agility. With an AI-ready data infrastructure:

  • Data scientists are more productive. With an efficient, automated, free-flowing, and scalable data pipeline, data scientists are able to access the right datasets at the right stage of their workflow. The data pipeline is integrated with many open-source and commercial tools that enable data scientists to automate their jobs. IT can deliver simplified deployment, portability, and a cloud-anywhere experience for any AI initiative.
  • Businesses can create and enforce governance policies. CIOs and CDOs can protect corporate data against exposure and risk—no matter where their data resides—by protecting sensitive information (like personally identifiable information, or PII) so that it isn’t inadvertently exposed in an AI workflow. IT can automatically create auditable copies of AI models and associated datasets for traceability and diagnosis of model behavior. Security teams can identify cyberthreats, including malicious insiders, in real time with AI- and ML-driven anomaly detection, protecting your most important, confidential, and commercially critical datasets.
  • The organization delivers AI efficiently and sustainably. The business can make full use of their infrastructure in concurrent processing environments by bursting out to cloud during peak utilization periods, and generally reducing idle times of GPU computing tiers. IT can also use AI to recommend the most efficient storage for a given workload, and to remove data that the organization no longer needs. Both outcomes result in sustainability and efficiency gains.

To increase their returns on investments in transformative AI projects, it is imperative that businesses foster a strong collaboration between data science teams, chief data officers, line-of-business stakeholders, and IT. Without coordinated contributions from all these stakeholders, AI initiatives may become sporadic, slow, expensive, out-of-reach, or the cause of unintended business risk.

AI-ready data and the right digital infrastructure are key

Unlocking the game-changing power of AI is almost completely dependent on the data that fuels it. Success will require a lifecycle approach to data with careful attention to data organization, governance, and security that builds more value as data is analyzed and used in progressively higher value AI use cases. 

But this process is easier said than done. Not only is the volume of data massive and unrelenting, it is also scattered, often unstructured, and always a security risk. Disparate data silos and technology complexity are major hurdles to getting AI projects into production. 

However, with thoughtful planning, organizational commitment, team collaboration, and the right data environment in place - businesses can thrive as never before in this new age of Intelligent Automation.

Attend NetApp INSIGHT to learn more about how NetApp can help your organization scale your strategic AI initiatives responsibly and efficiently.

This blog was co-authored by Ashish Nadkarni, GVP/GM, Worldwide Infrastructure and BuyerView Research, IDC.

Ritu Jyoti

Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice Global AI Research Lead

Ritu Jyoti is group vice president of Worldwide Artificial Intelligence (AI) and Automation Research with IDC's software market research and advisory practice. Ms. Jyoti is responsible for leading the development of IDC's thought leadership for AI research and management of the Worldwide AI and Automation Software research team. Her research focuses on the state of enterprise AI efforts and global market trends for the rapidly evolving AI and machine learning (ML) markets, including generative AI innovations and ecosystem.

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