As organizations grapple with the transformative potential of generative AI, experimentation serves as a valuable tool for understanding its capabilities and applications. At AWS, we’ve observed numerous companies engaging in these experiments, gradually becoming more adept at formulating insightful inquiries about the direction of this technology and its implications for their operations. However, many of these organizations find themselves mired in the proof-of-concept (POC) stage, unable to advance to full production despite successful initial tests.
A common barrier to progress is the perceived risk associated with deploying new technologies. This apprehension is understandable, given the frequent media reports surrounding AI pitfalls, such as hallucinations, bias, and inaccuracies. The introduction of any new technology inherently involves risks, and leaders must effectively manage these to acceptable levels. Notably, even the hiring of a human employee entails similar risks—including potential bias or misinformation. Therefore, deploying generative AI applications becomes a matter of weighing risks against the potential benefits of enhanced capabilities. There are strategies for risk management in AI, and emerging solutions are continuously being developed.
Moreover, some leaders rightfully concern themselves with the long-term costs associated with scaling generative AI applications. However, a crucial objective of a POC should be to gauge these expected costs, which are likely to decrease as foundational models advance, competition increases, and businesses are presented with diverse models boasting varying price-performance ratios.
However, I contemplate whether the issue truly lies with risk. Many companies may not be genuinely committed to transitioning their POC applications into production. Without delving deeply into semantics (like pilot, experiment, or proof of concept), it’s essential to recognize that a POC is designed to mitigate risks and gain insights into an application intended for deployment. Typically, organizations will pinpoint a business objective, strategize on using technology to achieve it, identify associated risks, and then create a proof of concept aimed at addressing those risks before making a comprehensive investment. Success in this context is clearly defined: alleviate the anticipated risks, understand the implementation requirements, or fulfill any other objectives established for the POC. The POC serves as a stepping stone towards leveraging technology for meaningful business outcomes. It’s true that if the POC reveals insurmountable risks or impracticality, the deployment may be scrapped. Yet, the process originates from the intent to utilize the technology because the business goal is deemed worthy of pursuit.
In stark contrast, many generative AI initiatives today involve a company exploring numerous potential use cases, experimenting with a foundational model to assess its applicability across various scenarios. While this approach is beneficial for initial learning and idea generation, it may not lend itself well to production readiness.
This method primarily evaluates the foundational model in its current form, along with its prompts and integration capabilities, without assessing the underlying business case. Furthermore, there is often no clear success metric; since the organization didn’t set out with a deployment intention or identify specific risks to mitigate, the outcome can only be summarized as “That’s interesting!” Additionally, the POC fails to systematically address the concerns that may arise when it’s time for deployment. Lastly, the necessary resources for production readiness are typically lacking, requiring a subsequent business case to justify their acquisition. At best, the prototype indicates that an application can perform a relevant function within a use case; however, this falls significantly short of proving a viable business case.
Having had the opportunity to experiment with generative AI, it’s crucial to pivot towards deriving tangible value from it. As with past technologies, this entails identifying significant business objectives that generative AI can address, developing a compelling business case, managing associated risks, securing necessary resources, and ultimately moving towards production. The focus should not solely be on experimenting with various use cases; rather, it should be on crafting a solution to a relevant business challenge and progressing toward its resolution. This mindset naturally leads to production readiness.
In this journey, it’s important to recognize that production-ready generative AI applications require robust security, privacy protection, compliance, agility, cost management, operational support, and resilience. Most generative AI solutions need to be integrated with existing enterprise applications, linked to enterprise data sources, and governed by enterprise protocols.
A genuine proof of concept, as opposed to a mere exploratory experiment, should include a clear path to deployment encompassing all necessary enterprise features. It’s essential to build upon the proof of concept, evaluate it in real-world scenarios, implement enterprise security measures, and conduct all the standard practices traditionally associated with enterprise IT.
This approach aligns with AWS’s vision for generative AI, as well as classical machine learning and future technologies. The crux of the matter is how technology can empower AWS customers to achieve their business, mission, or social objectives—not the technology itself. Our AI tools are meticulously designed to adhere to rigorous standards for security and reliability, fitting seamlessly within existing enterprise frameworks for compliance, governance, operability, and data management. They prioritize agility; for instance, Amazon Bedrock provides access to a variety of foundational models through a single API, simplifying the process of leveraging new models as they develop. Notably, Anthropic’s Claude 3, recognized as a top-performing model by industry metrics, is accessible via that API, alongside other models offering different trade-offs in terms of price, speed, and accuracy. AWS has always envisioned generative AI as part of the broader technology landscape within enterprises.
If you are serious about leveraging generative AI to fulfill a defined and significant business objective, consider it a functional capability on a trajectory toward production and value generation. Proofs of concept are vital for risk management and validating your business rationale—not merely for the technology itself but for the business functionality that it facilitates.
While it’s impossible to eliminate all risks associated with deploying new solutions (as noted earlier, even human employees pose risks), organizations can work towards mitigating these to acceptable levels while adhering to the principles of responsible AI. AWS has laid the groundwork to assist you in managing these risks; it is structured to provide the world’s most secure and reliable infrastructure. The risk management framework applied to your other IT systems can seamlessly extend into the generative AI applications you deploy. The pathway to production is clear.
For further insights on this topic, you can explore this blog post and learn from experts at this authority on the subject. Additionally, for those looking to expand their skills, this resource is an excellent opportunity.
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