Enterprises worldwide are embracing generative artificial intelligence (AI) with excitement, fueled by the promise seen in early use cases to enhance innovation and unlock unprecedented efficiencies. Research shows that 93% of organizations use generative AI in some capacity. For the first time, AI is within reach of anyone with an internet connection and intelligent device.
While early use cases show great promise, ripples of unease permeate the corridors of decision-makers, including C-suite executives, due to increased awareness of the risks and challenges posed by the rapid adoption of free generative AI tools. For IT and data decision-makers, the question is: how can they maintain a balance between innovation and risk to compete in an increasingly AI-driven world?
The growth of Generative AI and the challenges it poses in enterprises
Research commissioned by Iron Mountain surveyed IT and data-decision makers around the world to understand how their organizations use generative AI and the challenges they face adopting this new technology. Half the respondents say their organizations 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 their organizations use generative AI.
Leaders also identified challenges and risks when implementing AI. The two most prominent challenges are planning for IT resources to train and implement generative AI models (38%) and sourcing, protecting, and preparing data from physical and digital assets for use in generative AI model training (38%). Other challenges facing organizations include ensuring the accuracy and transparency of AI models (37%) and creating and enforcing generative AI policies (35%).
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 impeded enthusiasts. But with ubiquitous, free generative AI tools, citizen “data scientists” propagate shadow AI without the training, discipline, and organizational support needed to implement responsible generative AI. Without expertise in multiple disciplines, employees using generative AI can expose sensitive and protected data, introduce bias, and harm innovation rather than enable it. The ready availability of generative AI forces enterprises to re-evaluate their corporate policies and ensure protections are in place to keep data and reputations safe.
Chief Technology Officer and Chief Product Officer, Iron Mountain.
Optimizing innovation with Generative AI via a unified asset strategy
Our research points to a potential solution to turn these challenges into opportunities, with most respondents (96%) saying that implementing a unified asset strategy is critical to generative AI success. This strategy enables organizations to manage, protect, and optimize digital and physical assets used in and produced by generative AI applications. With this approach, organizations can fill gaps and solve challenges in strategy, ethics and risk management, and practice.
Strategically, a unified asset strategy harmonizes 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 maximize the return on investment by managing digital and physical assets involved in AI, enhancing data quality, streamlining operations, mitigating risks, and enabling flexible scale responsive to the changing needs of the organization.
When it comes to ethics and risk management, elements such as information governance contribute to policies that address ethical use, data privacy, and security concerns. Aligning these policies with the organization’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 IT resource planning, allocation, and management so IT teams can prepare for training and deploying generative AI models. Second, it encompasses comprehensive lifecycle management of physical and digital assets. It involves digitizing physical assets and enriching them with metadata for improved discoverability and accessibility, extracting valuable information from unstructured data, and protecting source and generated data against unauthorized access. Finally, it enables organizations to protect and manage data and other assets created by generative AI.
These outcomes are possible through implementing a unified asset strategy that encompasses physical and digital asset lifecycle management and protection, intelligent document processing, content services, compliance, return-on-investment optimization, and more. Altogether, this strategy 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 capitalizing on generative AI opportunities, 98% of survey respondents say that focused AI leadership, such as the emerging role of a chief AI officer (CAIO), can also accelerate the effective adoption of generative AI. While only 32% say their organizations have onboarded someone in this capacity, 94% expect the role to be filled in the future. When asked what AI leadership should achieve, the top response is ensuring that a unified asset strategy is in place. Other key benefits include orchestrating resource needs, following ethical practices, managing data input and output, and addressing ownership risk.
A call to action
The research suggests a strong connection between the challenges that generative AI presents and the power of focused AI leadership in driving a unified asset strategy to address them. By implementing a unified asset strategy, organizations can evolve outdated asset lifecycle management approaches, optimize physical and digital asset protection and management at scale, and catalyze value creation. Taking these steps will help these leaders remove roadblocks that impede innovation.
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