Practical AI: The Ideal Approach to Generative AI
In this article
The AI hype cycle is here and thrumming at full throttle.
Since ChatGPT-3's public release in November 2022, the ensuing arms race between Silicon Valley heavyweights has inundated governments, businesses and consumers alike with waves of excitement and apprehension regarding the potential of generative AI (GenAI) to impact our lives.
If your organization hasn't yet waded into the morass, trying to track the daily developments while developing a framework for mapping GenAI solutions to business use cases, odds are it will before long.
It's more likely AI and machine learning (ML) are already top of mind for your business and IT leaders. Especially following a pandemic that saw accelerated digitization across industries, forcing leaders to seek out new ways to extract more intelligent insights from their data.
Data designed to deliver starts with strategy
If you suspect the velocity of change in the AI landscape is tough for experts to track — and it is! — consider how organizations still learning the basics of GenAI, ChatGPT and large language models (LLMs) must feel. Despite a lack of expertise, many feel pressure to adopt this new technology as quickly as possible. Understandably, this pressure can lead to organizational analysis paralysis — fear of making the wrong decision that ultimately results in no decision at all.
But that's not all. What many organizations fail to consider when researching AI solutions are the many different elements needed to make an AI engine run — the combination of underlying IT infrastructure and strategy that must align to enable the awe-inspiring manipulation of data into human-like outputs at enterprise scale.
Our approach to AI helps clients avoid this common stumbling block by balancing the actionable outcomes that stem from early GenAI adoption with the benefits that come from investing in a scalable long-term strategy.
We call this Practical AI.
Practical AI is WWT's proven approach to delivering AI solutions. It's a methodology we've stress-tested and refined over the course of a decade in which we heavily invested in research and development (R&D) while delivering end-to-end AI/ML solutions to clients across industries.
We've found that the efficacy of our approach is ultimately tied to the maturity and alignment of an organization's AI and data strategies. This important work, crucial to long-term AI success, entails upfront goal setting to identify business outcomes; assessing current capabilities and deficiencies across a range of related technologies; modernizing IT infrastructure to ensure reliability, handle growing data demands and enhance scalability; and ensuring the visibility needed to accurately forecast an organization's data demands.
If you imagine the range of AI solutions in the market as a bell curve, you'll find off-the-shelf (OTS) solutions on the left that can be integrated in a standalone manner, contrasted with hyper-custom AI solutions, on the right, that require significant R&D investment to build.
Practical AI falls in the sweet spot between these extremes. It's a purposefully balanced approach that prioritizes targeted AI solutions that deliver fast outcomes while accounting for the viability, maturity and scalability of your long-term AI and data strategies.
In practice, Practical AI entails determining when it's in your best interest to buy and integrate an OTS AI solution and when it's smarter to invest in building and training your own model. As you can see from the chart above, there are short- and long-term impacts following whichever approach you choose.
A practical approach to assessing and adopting AI solutions can help you bypass the hype cycle, overcome analysis paralysis, and position your organization for long-term AI success that scales with your data capabilities and business ambitions.
Adding a layer of AI to your organization's existing data capabilities can seem relatively simple at first glance, especially given the many OTS solutions flooding the market. You can think of it like holding up a powerful magnifying glass to your data. It's relatively easy to do and will certainly help you see your data better. But if your data strategy and supporting infrastructure aren't mature enough, you may not like what AI reveals in terms of your data quality. Your customers may not be too impressed, either.
That's why it's vital to align your AI strategy with your data strategy. Once you goal-set for outcomes and chart a course for maturing your strategies in tandem with your infrastructure, you'll be ready to take advantage of the next exponential leap in GenAI and the data demands that accompany it.