Generative AI: how to innovate with less risk
Companies around the world are embracing generative artificial intelligence (AI) with enthusiasm, 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 the reach of anyone with an internet connection and an intelligent device.
While early use cases are promising, waves of unease are permeating 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 balance innovation and risk to compete in an increasingly AI-driven world?
The growth of generative AI and the challenges it poses for enterprises
Research commissioned by Iron Mountain surveyed IT and data decision makers around the world to understand how their organizations are using generative AI and the challenges they face in adopting this new technology. Half of respondents say their organizations use AI to create content, such as marketing or design-based input. Interacting with customers, for example through chat or voice responses, increasing team collaboration and adding value to services and products are other key ways their organizations are using generative AI.
Leaders also identified challenges and risks in implementing AI. The two most prominent challenges are planning IT resources for training and deploying generative AI models (38%) and collecting, protecting, and preparing data from physical and digital assets for use in generative AI model training (38% ). Other challenges organizations face include ensuring the accuracy and transparency of AI models (37%) and creating and enforcing generative AI policies (35%).
Some of these concerns may sound worryingly familiar to C-suite executives who remember the early days of public cloud. At the time, the requirement to pay for the technology hindered enthusiasts. But with ubiquitous, free generative AI tools, “data scientists” are promoting shadow AI without the training, discipline, and organizational support needed to implement responsible generative AI. Without expertise in multiple disciplines, workers using generative AI can expose sensitive and proprietary data, introduce bias, and harm rather than enable innovation. The ready availability of generative AI is forcing companies to reevaluate their corporate policies and ensure protections are in place to keep data and reputations safe.
Chief Technology Officer and Chief Product Officer, Iron Mountain.
Optimize innovation with generative AI through 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 the success of generative AI. This strategy enables organizations to manage, protect and optimize digital and physical assets used in and produced by generative AI applications. This approach allows organizations to close gaps and solve challenges in strategy, ethics, risk management and practice.
Strategically, a unified asset strategy harmonizes AI initiatives and asset management while ensuring the safe and environmentally sustainable retirement of digital and physical assets in line with business objectives. It can also help maximize return on investment by managing digital and physical assets involved in AI, improving data quality, streamlining operations, mitigating risk and enabling flexible scale that responds 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 objectives and the nature of its resources enables more effective policymaking and enforcement.
In practice, a unified asset strategy can help in several ways. Through effective asset lifecycle management and a scalable business model, a unified asset strategy facilitates efficient planning, allocation, and management of IT resources so IT teams can prepare for training and deploying generative AI models. Second, it includes comprehensive lifecycle management of physical and digital assets. It involves digitizing physical assets and enriching them with metadata for better findability and accessibility, extracting valuable information from unstructured data and protecting source and generated data against unauthorized access. Finally, it allows organizations to protect and manage data and other assets created by generative AI.
These results are possible by implementing a unified asset strategy that includes managing and protecting the lifecycle of physical and digital assets, intelligent document processing, content services, compliance, optimizing return on investment and more. Overall, this strategy provides a foundation for accelerating and increasing the impact of AI while reducing risks to enterprises.
The need for experienced AI leaders
While data and IT leaders agree that a unified asset strategy is essential to capitalizing on generative AI opportunities, 98% of survey respondents say focused AI leadership, such as the emerging role of a Chief AI Officer (CAIO), can also accelerate effective development. adoption of generative AI. While only 32% say their organization has hired someone in this capacity, 94% expect this role to be filled in the future. When asked what AI leadership should achieve, the key answer is to ensure there is a unified asset strategy. Other key benefits include orchestrating resource needs, following ethical practices, managing data input and output, and addressing proprietary risk.
A call to action
The research suggests a strong link between the challenges posed by generative AI 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 legacy approaches to asset lifecycle management, optimize the protection and management of physical and digital assets at scale, and catalyze value creation. Taking these steps will help these leaders remove the roadblocks that hinder innovation.
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