Generative AI: How to Innovate with Less Risk

By: Narasimha Reddy Goli, Chief Technology Officer and Chief Product Officer, Iron Mountain

Enterprises worldwide are embracing generative artificial intelligence (GenAI) with excitement, fuelled by the promise seen in early use cases to enhance innovation and unlock unprecedented efficiencies. Research shows that 93 percent of organisations use GenAI in some capacity. For the first time, AI is within reach of anyone with an internet connection and intelligent device.

While excitement reigns, ripples of unease are beginning to permeate the corridors of the C-suite due to increased awareness of the risks and challenges posed by the rapid adoption of GenAI. Specifically, via the proliferation of free GenAI tools and “shadow AI,” the unsanctioned and hidden use of AI in organisations.  

For IT and data decision-makers, addressing these challenges are urgent priorities they must address responsibly. The question is: how can they ensure they maintain a balance between innovation and risk to compete in the new, AI-driven world? 

The growth of GenAI and challenges it poses

Research commissioned by Iron Mountain surveyed IT and data-decision makers around the world to understand how their organisations use GenAI and the challenges they face adopting this new technology. Half of the respondents say that their organisations use AI to create content such as marketing or design-based input. Interacting with customers, such as via chat or voice responses, increasing team collaboration, and adding value to services and products are other significant ways in which AI is currently being used.

Amid this surge of potential, leaders also identified challenges and risks when implementing AI. The two most prominent challenges are planning for IT resources to train and implement GenAI models (38 percent) and sourcing, protecting, and preparing data from physical and digital assets for use in GenAI model training (38 percent). Other challenges facing organisations include ensuring the accuracy and transparency of AI models, alongside creating and enforcing GenAI policies.

Some of these concerns may feel worryingly familiar for C-suite leaders who remember the early days of the public cloud. Back then, the requirement to pay for the technology was an impediment to enthusiasts. But with ubiquitous, free GenAI tools, citizen “data scientists” propagate shadow AI without the training, discipline, and organisational support needed to implement responsible GenAI.

Without training and expertise in multiple disciplines, employees using GenAI can expose sensitive and protected data, introduce bias, and harm innovation, rather than enable it. The ready availability of GenAI forces businesses to re-evaluate their corporate policies and ensure protections are in place to keep data safe.  

Optimising innovation with GenAI via a unified asset strategy 

Our research points to a potential solution to turn these challenges into opportunities, with almost all respondents (96 percent) saying that implementing a unified asset strategy is critical to GenAI success. This strategy enables organisations to manage, protect, and optimise digital and physical assets used in and produced by GenAI applications. With such an approach, organisations can fill gaps and solve challenges in strategy, ethics and risk management, and practice. 

Strategically, a unified asset strategy harmonises AI initiatives and asset management while providing for secure and environmentally sustainable retirement of digital and physical assets in keeping with enterprise objectives. It also can help maximise the return on investment (ROI) by managing digital and physical assets involved in AI, enhancing data quality, streamlining operations, mitigating risks, and enabling flexibility as per the organisational needs. 

When it comes to ethics and risk management, a unified asset strategy can ensure audit-ready compliance with GenAI-related regulations and guidelines and help create and enforce GenAI policies. Information governance is critical to the strategy, as it contributes to policies that address ethical use, data privacy, and security. Aligning these policies with the organisation’s goals and the nature of its assets enables more effective policy creation and enforcement.

Practically, a unified asset strategy can help in a variety of ways. Through effective full lifecycle asset stewardship and a scalable operating model, a unified asset strategy facilitates efficient resource planning, allocation, and management so IT teams can prepare for training and deploying GenAI models. Second, it encompasses comprehensive lifecycle management of physical and digital assets. It involves digitising physical assets and enriching them with metadata for improved discoverability and accessibility, extracting valuable information from unstructured data, and protecting the source and generated data against unauthorised access. Finally, it ensures that models are accurate, unbiased, and transparent, enabling organisations to protect and manage data and other assets created by GenAI.

These combined outcomes are only possible with a unified asset strategy, as its framework encompasses every stage of the physical and digital asset lifecycle management and protection, intelligent document processing, content services, compliance, ROI optimisation, and more. Altogether, it provides a foundation for accelerating and amplifying the impact of AI while reducing risk for enterprises. 

The need for experienced AI leaders

While data and IT leaders agree that a unified asset strategy is essential for capitalising on GenAI opportunities, 98 percent of survey respondents agreed that a dedicated AI leader like a Chief AI Officer (CAIO) could also accelerate the effective adoption of GenAI. While only 32 percent say their organisations have onboarded someone in this capacity, 94 percent expect the role to be filled in the future. When asked what a dedicated AI leader should achieve, the top response was ensuring that a unified asset strategy was in place. Other key benefits include orchestrating resource needs, following ethical practices, managing data input and output appropriately, and addressing ownership risk. The research suggests a strong connection between the challenges that GenAI presents and the power of a dedicated AI leader driving a unified asset strategy to address them. By implementing a unified asset strategy, organisations can evolve outdated asset lifecycle management approaches, optimise physical and digital asset protection and management at scale, and catalyse value creation. Taking these steps will help these leaders remove roadblocks that impede innovation. 

A call to action

The unstoppable march of GenAI demands new strategies and experienced AI leaders to address challenges and harness benefits. Decision-makers must consider the gaps within their organisations and how a unified asset strategy and a dedicated AI leader can help. 

It is time to create new value from physical and digital assets in the age of GenAI. Organisations need all the pieces of the puzzle to balance opportunities and risks and capitalise on the technology’s potential before they get left behind.

data privacyData scientistsData SecurityGenAIGenerative AIIron Mountain
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