Today, more and more organizations are exploring the power of generative AI to drive innovation, increase productivity, and deliver superior customer experiences.
But the field is changing rapidly, with more capable and cost-effective infrastructure models emerging every week, new use cases emerging, and best practices constantly shifting, leaving some organizations wondering how to keep up with the technology’s evolving capabilities.
Still, there’s one generative AI strategy to avoid: taking a wait-and-see approach and doing nothing.
To succeed with generative AI, organizations must be proactive, prioritizing an approach that allows for customization and adaptability, allowing them to securely integrate their own data into generative AI solutions and remain flexible as technology advances.
By applying a strategic approach to generative AI that prioritizes security, agility, and flexibility from the start, companies can realize the technology’s full potential, rapidly customize applications for the specific use cases that serve customers today, and build a competitive advantage.
Embrace agility
When adopting any new technology, it can be tempting to stick to one solution and tackle all challenges and opportunities at once. But generative AI is evolving so quickly that organizations need to embrace an experimental, agile mindset to test ideas, learn from the results, and innovate quickly.
For example, rather than expecting a single generative AI model to serve as an all-purpose solution, companies should test and iterate over different foundational models or large language models (LLMs) to identify which one best suits their use case and desired outcome. With the right tools and infrastructure, organizations can use multiple models and update or fine-tune them over time, or even build models from scratch.
An agile approach to generative AI breaks down the development process into small sprints and enables teams to regularly review and adjust models based on real-time feedback and results. This allows inaccuracies and other issues to be addressed quickly, improving business outcomes. Regular retrospectives and feedback loops enable teams to continuously learn and improve, a process that’s essential for keeping up with the latest advancements in generative AI.
Collaboration is another hallmark of an agile approach. Cross-functional teams consisting of data scientists, developers, domain experts, and end users must work closely to ensure that AI applications are technically sound and aligned with business goals. Diverse perspectives and expertise are essential to address the complex challenges posed by generative AI.
Adopting agile methodologies enables organizations to rapidly respond to new data insights, evolving user needs, and emerging technologies to keep their generative AI applications 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 Organization
Organizations across industries are adopting an agile approach as they innovate with generative AI: Thomson Reuters, a global news and technology company serving law, accounting, publishing and other professions, is using generative AI to help its teams unlock insights and automate workflows, freeing them to focus on the larger needs of their customers.
To foster exploration and innovation across the organization and make AI solutions accessible to both technical and non-technical teams, Thomson Reuters Amazon Bedrocka fully managed service that offers a broad choice of high-performance foundational models, joined forces to develop Open Arena, a web-based, self-service enterprise AI and machine learning (ML) application suite designed to empower employees to innovate quickly and securely with generative AI.
By leveraging Amazon Bedrock’s simple user interface and enterprise-grade security and privacy features, Thomson Reuters can now deploy AI models in hours instead of days, streamlining testing and innovation, making generative AI tools more accessible, and simplifying the user experience.
“Given how rapidly this field is evolving, being able to use different models as they emerge was a key driver for us,” says Joel Fron, head of AI at Thomson Reuters Labs.
“The ability to easily launch new use cases is really powerful,” says Hron. “In a rapidly evolving field, you need to be diversified and ready to take advantage of the latest advancements.”
Keeping up with rapid evolution
The strategic idea of deploying generative AI with security, flexibility and agility applies across sectors.
The PGA TOUR, a premier golf organization featuring the world’s best players, used Amazon Bedrock to select the best LLM for a proof of concept, building and optimizing a virtual assistant with the end goal of helping golf fans access the information they need. And freight transportation startup Nexxiot is using the same technology to host a conversational assistant for its customers, providing them with the best real-time conversational insights about their transportation assets. “Having direct access to a variety of underlying models enables rapid and seamless experimentation, development, and deployment, which drives innovation at Nexxiot,” said Maja Rudinac, Chief Technology Officer at Nexxiot.
Regardless of industry, the key to a strong generative AI strategy is agility – the flexibility to safely and reliably embrace the spirit of experimentation that drives innovation.
In a constantly changing generative AI environment, organizations can’t afford to sit on the sidelines or cling to rigid approaches and quickly outdated technologies. Remaining flexible and agile when planning and building generative AI is essential to ensure organizations benefit from the latest advancements that drive growth.
Innovative organizations rely on rapid experimentation and iterative refinement to improve customer and employee experiences while being ready to evolve with rapidly changing technology and business needs. The biggest risk a company can take with generative AI is to never take any experimental risks.
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