More and more organizations today are exploring the power of generative AI to drive innovation, boost productivity, and deliver exceptional customer experiences.
But with higher-performing and more cost-effective foundation models appearing every week—along with new use cases emerging and best practices shifting constantly—the space is changing rapidly. That leaves some organizations wondering how they can keep up with the technology’s advancing capabilities.
Still, there’s only one generative AI strategy to avoid: taking a wait-and-see approach and doing nothing.
To succeed with generative AI, organizations need to jump in, prioritizing an approach that accommodates customization and adaptability, letting them securely integrate proprietary data into their generative AI solutions and stay flexible as the technology advances.
By applying a strategic approach to generative AI that prioritizes security, agility, and flexibility from the start, businesses can realize the technology’s full potential and quickly tailor applications to specific use cases that help their customers today—and allow them to build a competitive edge.
Embracing Agility
When adopting any new technology, it may be tempting to lock in on a single solution and tackle every challenge or opportunity at once. But with generative AI evolving so quickly, organizations should embrace an experimental and agile mindset that allows them to test ideas, learn from results, and innovate quickly.
For example, instead of hoping a single generative AI model will serve as a one-size-fits-all solution, enterprises need to test and iterate with different foundation models or large language models (LLMs) until they identify the best one for their use cases and desired outcomes. With the right tools and infrastructure, organizations can choose to use multiple models, updating or fine-tuning them over time, or even to build a model from scratch.
An agile approach to generative AI also breaks down the development process into smaller sprints, enabling teams to regularly review and adjust models based on real-time feedback and results. This ensures they can quickly address inaccuracies and other issues, leading to better business outcomes. Regular retrospectives and feedback loops allow teams to continuously learn and improve, a process that is vital for staying up to date with the latest advances in generative AI.
Collaboration is another hallmark of an agile approach. Cross-functional teams, comprising data scientists, developers, domain experts, and end users, should work closely together to ensure that their AI applications are technically sound and aligned with business objectives. Diverse perspectives and expertise are essential for addressing the complex challenges generative AI brings.
By embracing agile methodologies, organizations can respond quickly to new data insights, evolving user needs, and emerging technologies, ensuring that their generative AI applications remain relevant and effective. This approach not only improves the technical performance of AI models but also ensures that solutions align with user needs and business goals, ultimately leading to more impactful and sustainable AI initiatives.
Generative AI Across the Organization
Organizations across all industries are embracing the agile approach as they innovate with generative AI. Thomson Reuters, the global news and technology company serving the legal, accounting, publishing, and other professions, is using generative AI to help its teams draw insights and automate workflows that free them up to focus on their customers’ bigger needs.
To encourage exploration and innovation across the organization, making AI solutions accessible to both technical and nontechnical teams, Thomson Reuters used Amazon Bedrock, a fully managed service offering a broad choice of high-performing foundation models, to develop Open Arena. A web-based suite of self-service enterprise AI and machine learning (ML) applications, Open Arena is designed to help employees innovate quickly and securely with generative AI.
Backed by Amazon Bedrock’s simple user interface and enterprise-grade security and privacy features, Thomson Reuters can now deploy its AI models in hours rather than days, streamline testing and innovation, increase accessibility to generative AI tools, and simplify user experience.
“Having the ability to use a diverse range of models as they come out was a key driver for us, especially given how quickly this space is evolving,” says Joel Hron, head of AI and Thomson Reuters Labs.
“The ease of spinning up new use cases is really powerful,” Hron says. “In a quickly evolving space, you want to diversify and be able to capitalize on the latest advancements as they come.”
Keeping Up with Rapid Evolution
The strategic mindset of introducing generative AI with security, flexibility, and agility applies to any sector.
The PGA TOUR, the premier golf organization for the world’s greatest players, used Amazon Bedrock to select the best LLM for its proof of concept so it could build and optimize its virtual assistant with the ultimate goal of helping golf fans access the information they need. And Nexxiot, a freight transportation startup, uses the same technology to host a conversational assistant for its customers to give their customers the best real-time, conversational insights into their transport assets. “The direct access to a variety of foundation models powers Nexxiot’s innovation by allowing for rapid and seamless experimentation, development, and deployment,” says Nexxiot’s Chief Technology Officer, Maja Rudinac.
Regardless of the industry, the key to a strong generative AI strategy is agility: the flexibility to safely and securely embrace the spirit of experimentation that fosters innovation.
In the ever-changing generative AI landscape, organizations cannot afford to sit on the sidelines or become attached to rigid initiatives and quickly outdated technology. Keeping flexible and agile as they plan and build with generative AI is critical for organizations to benefit from its latest advances, which can help them grow.
Innovative organizations depend on rapid experimentation and iterative refinement; that way, they can enhance their customer and employee experiences while they stay ready to evolve with rapidly changing technology and business needs. The greatest risk a business can take with generative AI is to not take any experimental risks at all.
Learn more about AWS Generative AI.
