BLOG | Tips on optimizing AI to achieve business results

In the first half of this year, many AWS customers I’ve spoken to had been hesitant to scale generative artificial intelligence (AI) beyond proof-of-concepts, despite its promise for enhancing productivity.Common concerns include data privacy, output accuracy, unclear return on investment, and potential legal and regulatory implications.To build confidence and clarity, customers are implementing robust AI governance, policies and standards, clear usage guidelines, and deliberate rollouts.

However, demonstrating clear ROI with AI to justify project costs, especially at the C-suite and board levels, remains a significant hurdle.This challenge stems partly from the difficulty in quantifying the productivity gains in knowledge work that generative AI optimizes. For example, how do you translate a reduction in resolution time from 10 hours to 1 hour by an HR chatbot into business value? Without this, calculating ROI to convince boards to invest further becomes challenging.



And without business value, how do you calculate the exact and deliberate ROI to convince the board to invest further? Given these challenges, companies are increasingly exploring a variety of AI solutions to find the right balance of performance, cost, and ease of implementation.Powerful AI models from Anthropic, Mistral, Meta, and of course, Amazon (we just announced our Amazon Nova family of advanced models at AWS re:Invent) are making generative AI more accessible than ever.These models can produce text, from creative writing to code generation, as well as trend analysis to language translation, video analysis, and image/video creation, driving increased productivity and creativity.

Over the past 12 months, as customer generative AI adoption has expanded on Amazon Bedrock, our fully managed service offering for building generative AI applications, customers have reinforced that broad and flexible model choices, guardrails for safety, knowledge base, and other key features to simplify building AI applications is important to tackling business problems with generative AI.Today, tens of thousands of customers are using Amazon Bedrock to build generative AI applications to solve a wide variety of business problems across every industry vertical.We’re seeing that getting the best results from models isn’t just about selecting the latest and greatest.

Combining fit-for-purpose models with best practice prompting techniques, often called prompt engineering, can produce significantly better results in terms of accuracy and cost-effectiveness.One impactful technique is named multi-shot prompting. By sharing multiple examples of desired outcomes, users can effectively calibrate the model for each use case, resulting in better accuracy, consistency, cost, and performance.

Another approach to level-up customers’ generative AI game is through retrieval-augmented generation (RAG). AI models are trained on specific data and their knowledge does not extend beyond the data used for training. RAG complements a model’s knowledge by providing more up-to-date or context-specific data or both, and ground model responses in that data to increase accuracy and relevance, reducing follow-up human intervention.

For example, PEXA recently launched an internal generative AI Assistant that leverages real-time company data using RAG and Amazon Bedrock, to ensure every employee chat interaction is secure, accurate, and contextually relevant.While optimal prompting and RAG are powerful, it is not a panacea. Model choice remains paramount and we maintain there is no one model that will rule them all.

That’s why we’ve just launched six new Amazon Nova models, that deliver industry-leading price-to-performance, to expand the growing selection of the broadest and most capable models in Amazon Bedrock for customers.While most tasks can be performed by the most sophisticated models, using a model that is too sophisticated for the task will cost more and cause higher latency; the trick is to select the model that is “just right” for a given task, which usually means the smallest, cheapest, fastest model that can perform it.Human oversight, curation, and feedback loops are also essential to ensure the right quality outcome and adhering to responsible AI principles.

No generative AI system today is reliable enough to fully automate the end-to-end business process. This human-AI collaboration is key as we work towards more robust, responsible, and trustworthy AI systems.To make it as successful as possible, we need to train the workforce with the right skills.

We’ve already trained more than 100,000 people in the Philippines in cloud skills since 2017, and we have a dedicated global Amazon goal to provide free AI skills training to two million individuals by 2025.The demand is high, with recent research commissioned by AWS finding almost 80% of employers in the Asia Pacific region prioritize hiring individuals with AI skills.We are committed to democratizing access and training to generative AI, robust tools for responsible AI development, and initiatives for Philippine customers and partners to deliver a more sustainable future.

Responsible use of these technologies is key to fostering continued innovation.One way we’re doing this is by providing customers with the tools and guidance needed to build and scale generative AI safely, securely, and responsibly, ensuring the Philippines is an AI global leader in the digital economy.The author is the head of solutions architect for Asean at AWSThe post BLOG | Tips on optimizing AI to achieve business results appeared first on Newsbytes.

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