In the realm of transformative technologies like generative AI, initiating experiments serves as an effective means to comprehend its capabilities and applications. At Amazon, we’ve observed numerous organizations engaging in such experiments, becoming increasingly adept at formulating insightful inquiries about the technology’s trajectory and its implications for their operations. However, many remain trapped in the proof-of-concept (POC) phase, never advancing to full production, despite their initial successes.
One contributing factor is the perception of risk. This sentiment is understandable given the plethora of reports surrounding AI hallucinations, harmful biases, and inaccuracies. The introduction of any new technology is inherently risky; thus, leaders must navigate these concerns to a manageable extent. Even deploying human resources can present similar risks—individuals may also exhibit biases or disseminate incorrect information. Ultimately, the goal is to balance these risks against the potential advantages presented by generative AI capabilities. There are existing frameworks for managing AI risks, and further developments are anticipated.
Some executives are astute enough to contemplate future costs associated with scaling generative AI applications. A primary objective of a POC should be to gauge these potential expenses. Fortunately, as foundational models advance and competition among providers increases, costs are likely to decrease over time.
Yet, I question if the hesitation truly stems from risk. Perhaps the crux of the issue is that many companies lack genuine commitment to transitioning their POC applications into production. Without getting bogged down in terminological nuances (experiment, proof of concept, pilot, etc.), it’s crucial to recognize that a proof of concept is designed to minimize risks and provide insights into a technology intended for deployment. Businesses typically define a clear objective, strategize on employing technology to achieve it, identify associated risks or challenges, and subsequently design a POC to address these concerns before proceeding with a full investment. The success of a POC is clearly defined: either mitigate the identified risks, understand the implementation process, or fulfill other specific goals. The POC should be a stepping stone toward utilizing the technology to achieve a significant business objective. While it may be scrapped if the POC reveals insurmountable obstacles or excessive risks, the process begins with the intent to harness the technology for a meaningful goal.
In contrast, many current generative AI experiments involve exploring numerous potential use cases—testing a foundational model across 100 scenarios to assess its applicability. This initial approach is beneficial for grasping the technology and sparking innovative ideas. However, it often falls short of paving the way to production.
The primary drawback of this approach is that it solely evaluates the foundational model, its present functionality, prompts, and integrations—without examining the underlying business case. Furthermore, a lack of clear success criteria exists; since the company did not initiate the process with an intent to deploy or identify specific risks to address, the outcome of the POC might only be a vague acknowledgment of its coolness. Additionally, the POC fails to systematically address the risks that will arise during deployment. Finally, the necessary resources for production readiness often remain unallocated, requiring a subsequent business case to secure them. At best, the prototype indicates that an application can perform a relevant function; however, this does not equate to substantiating a comprehensive business case.
Having engaged with generative AI and conducted experiments, it is now imperative to extract tangible value from it. Similar to past technological deployments, this process involves pinpointing crucial business objectives that generative AI can address, outlining a solid business case, managing associated risks, securing necessary resources, and ultimately moving toward production. It transcends mere experimentation with use cases; it necessitates crafting a solution for a significant business challenge and progressing toward its resolution. Adopting this mindset fosters a natural progression toward production.
Throughout this journey, it becomes evident that production-ready generative AI applications demand robust security, privacy safeguards, compliance, agility, cost management, operational support, and resilience. Most generative AI solutions must integrate seamlessly with other enterprise applications, connect to data sources, and adhere to established governance frameworks.
A genuine proof of concept, distinct from a mere learning exercise, incorporates a clear path to deployment featuring all enterprise requirements. It is essential to enhance the POC, evaluate it in real-world environments, apply the enterprise security model, and execute all standard practices inherent in enterprise IT.
This philosophy aligns with Amazon’s vision for generative AI, as well as classical machine learning and future technologies. The core focus lies not on the technology itself but on how it enables AWS customers to achieve business, mission, or social objectives. Our AI tools are meticulously designed to meet rigorous standards for security and reliability while integrating seamlessly within existing enterprise frameworks for compliance, governance, operational efficiency, and data management. They are built for agility; for instance, Amazon Bedrock provides streamlined access to multiple foundational models via a single API, facilitating the adoption of emerging models as they evolve. Notably, Anthropic’s Claude 3, currently recognized as the industry benchmark, is accessible through this API alongside other models that present varying trade-offs in terms of price, speed, and accuracy. AWS envisions generative AI as an integral component of the broader enterprise technology landscape.
If you are intent on leveraging generative AI to fulfill a significant business objective, approach it as a functional capability progressing toward production and value generation. Proofs of concept play a crucial role in managing risks and validating your business case—not just for the technology itself, but for the business functionality you aim to create.
While it is impossible to eliminate all risks associated with new technology deployment (even human employees carry their own risks), efforts can be made to manage these risks to an acceptable level while adhering to responsible AI guidelines. AWS has undertaken substantial efforts to facilitate risk mitigation, architecting the most secure and reliable infrastructure available. The risk management frameworks already in place for your IT systems can be effectively applied to the deployment of new generative AI applications. The pathway to production is clear.
For further insights, check out this other blog post on generative AI. They are an authority on this topic and offer excellent resources for understanding how to implement these technologies effectively.
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