Eight Questions to CIOs: Prepare enterprises for generative AI

Businesses now realize they are faced with the task of preparing their data, people and processes to harness the full potential of generative AI. In fact, a recent Accenture survey found that nearly all (99%) executives said they planned to increase investment in this technology. As a result, leaders need to fundamentally rethink how work gets done. CIOs have a cross-functional business process perspective, coupled with a deep understanding of how technology can be used to reshape operations and deliver value, making them uniquely positioned to help organizations prepare for generative AI

However , but leaders are working hard to take the necessary follow-up steps to promote the implementation of this technology. For example, a recent Accenture report found that 67% of senior technology leaders believe that a lack of technical acumen among peers is a major obstacle to integrating technology into strategy development. The key to overcoming this obstacle is to understand generative AI and innovation and linking it to business success.

Effectively integrating AI into the business starts with setting clear goals to define business value and aligning the AI strategy with these overall business goals. Many CIOs who are responsible for promoting the digital agenda of enterprises have begun to regard AI as the core and use AI solutions to realize the most critical elements of the strategy. They recognize that building a robust infrastructure is an important first step in their organization's journey to enterprise readiness, which will enable enterprises to scale generative AI with maximum efficiency and effectiveness and promote success with this technology across the enterprise. use. In fact, 98% of global executives believe that AI-based models will play an important role in their organizational strategies within the next three to five years.

In designing the new AI Navigator for Enterprise, we identified these eight questions CIOs should ask themselves to determine if their enterprise is ready to stress test generative AI

• Which base model should we use? In other words, which architecture best ensures that the model’s output is relevant, reliable, and usable. The number of models and vendors for generative AI continues to increase, and you need to carefully consider your choice to ensure it is aligned with your organization's needs and requirements.
• How do I make these models available to us? There are two main approaches that enterprises can take to deploy models, each with its own advantages. Do you need a “full control” option to access models on your own public cloud, or do you plan to use generative AI as a managed cloud service from an external provider for speed and simplicity?
• How do we adapt the model to our own data for use? AI and data have become a key component of a strong digital core, which is a major source of competitive advantage for today’s businesses. Getting the most value from generative AI requires leveraging your proprietary data to improve accuracy, performance and usefulness across your enterprise. There are various ways you can consider adapting pre-trained models to create custom tools that are relevant to your organization and people.
• What is the overall preparedness of the company? Start by thinking about your integration and interoperability framework. Is your base model secure and safe to use? The adoption of generative AI makes it imperative for every enterprise to develop a strong and responsible AI compliance program. Compliance with legal, regulatory and ethical standards is critical to establishing a sound AI foundation, as is managing controls during the design phase to assess the potential risks of generative AI use cases.
• What about our carbon footprint? Although the basic model is pre-trained, it can still consume a lot of energy in the process of adaptation and fine-tuning. How much is consumed and what the impact is depends on the approach taken to purchase, enhance or build the base model. If left unchecked, this has the potential to have serious environmental impacts, so it is increasingly important to weigh sustainability as a factor in advance to make the right choice for both the business and the environment.
• How to realize the industrialization of generative AI intelligent application development? The next step after selecting and deploying a base model is to consider what new frameworks may be needed to industrialize and accelerate application development. Rapid engineering is quickly becoming a differentiating capability. Through an industrialized process, you can build a corpus of efficient, well-designed prompts and templates aligned with a specific business function or domain.
• What do we need to operate generative AI at scale? The complexity of upending existing processes and reinventing new ways of working is a challenge in itself. But finding ways to monetize large-scale AI should be a concern of every CIO. AI has become a fertile ground for nurturing innovation, and CIOs should establish good connections throughout the entire enterprise structure. Finding opportunities for cross-functional collaboration will lead to new insights and informed decisions, promoting open innovation within the organization and across the industry, while unlocking new growth opportunities.
• Where do I start and how do we continue to guide future direction? AI-driven productivity is the next big milestone. Software development is an area ripe for CIO influence, and you should dig deep and share your use cases to demonstrate your team's real-world experience through actual results from pilot projects. For example, Accenture last year studied how generative AI can help software development teams launch products faster. Accenture used next-generation AI tools like Amazon CodeWhisperer and saw significant improvements in developer productivity and code quality, as well as faster overall release cycles, helping deliver the new AWS Velocity platform in record time. By becoming your own case study, you can show how to make it a reality and guide the rest of the organization to experiment and test, move quickly, and scale usage quickly. You'll be better equipped to guide your stakeholders on where technology is going, how fast it's going, and what results your organization can expect.

New Inflection Point

Technology is the key to stronger growth, greater agility and greater resilience for every industry, and generative AI is one of them An important differentiator, this technology will fundamentally change how we work and live. Accenture research found that 40% of work time is affected by large language models. A closer look reveals that, particularly in IT and technology roles, 73% of total work time can be changed through generative AI, highlighting the need to do this safely, responsibly, cost-effectively, and with business relevance. The importance of laying the right foundation for scaling generative AI in a valuable way.

CIOs have a significant opportunity to help their organizations navigate the complexities of today’s rapidly changing digital environment. Leveraging breakthrough advances in AI and an enterprise-wide approach to performance, they can find ways to make technology work for them, redefining themselves and their industries.

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