Have you seen this post on GenAI – now almost an adage of the times?
“You know what the biggest problem with pushing all-things-AI is? Wrong direction. I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.”
The post is sassy, but spot on. It’s a stark reminder that humans want AI to assist with undesirable and painstaking tasks, not to assimilate creativity. Companies may ultimately want the same thing. They need creative productivity from humans, and they need GenAI to deliver the ROI that keeps the financial house in order, rather than falling into the AI money pit.
If you think this message is coming from yet another company looking to jump on the GenAI bandwagon, think again. We come at GenAI and data management – which is critical to GenAI success – from a position of unrivaled understanding, experience and commitment.
Here’s what we’ve learned, and what to do in transforming your organization with GenAI.
CEO of Hitachi Vantara.
Be extremely selective in using GenAI
Organizations sometimes fail to take the time to define what it is that they want to get out of GenAI. This has led many companies to go too big too fast or proceed with excess caution, or not move forward at all.
Everest Group says that 2023 saw more than 1,200 GenAI proofs of concept (PoCs), signaling strong enterprise engagement, but less than 18% of PoCs reach production stage. Gartner adds that growth in 90% of GenAI enterprise deployments will slow by 2025 as costs exceed value.
Position your GenAI efforts for success and growth by first defining the problem you are trying to solve. Specify what outcomes you expect. And be selective in using GenAI, because it requires tremendous compute resources and scaled out IT infrastructure that can handle large data sets, so it can get expensive fast. Also keep in mind that consuming a lot of compute and power will have an impact on the planet and your sustainability goals. So, make sure that you’re using GenAI in a sensible manner, and only use GenAI to solve problems that couldn’t otherwise be solved.
Implement robust policies and infrastructure
If someone is using GenAI to write a poem, there are no right or wrong answers. But mission-critical enterprise applications are going to require near 100% accuracy. If you don’t employ high-quality data in your GenAI efforts, you won’t get the results that you are expecting.
Assess where you are with your GenAI and data strategies. Our recent research indicates that less than half (44%) of organizations have well-defined GenAI policies, and even fewer (37%) believe their infrastructure and data ecosystem are well-prepared for GenAI implementation.
Work with data experts to establish and implement robust data management solutions and strategies that address data security and integrity wherever that data may reside.
Also, make sure your GenAI strategy positions you to be agile in this fast-moving environment in which there are a lot of acquisitions and consolidation. Plan and build for GenAI in a way that keeps you flexible because what worked a few months ago may not work in the future.
Understand and address risks and regulations
Using high-quality data sets is also important considering the growing regulatory scrutiny around AI and GenAI. For example, the European Union’s Artificial Intelligence Act went into effect Aug. 1. This applies to any providers that put AI systems into service within the EU.
The new EU AI Act calls for AI systems that are classified as high risk – such as systems that are used for energy and transport, medical devices, and systems determining access to education or employment – to implement risk-mitigation strategies. The EU explains that includes achieving high standards of accuracy, cybersecurity and robustness; ensuring human oversight; maintaining activity logs; providing detailed documentation; and using high-quality data sets.
But the unprecedented volume and complexity of data environments can make that a challenge. Employ the tools and DataOps processes to understand data lineage, do data cost optimization, and ensure reliability, resilience, and visibility throughout the data lifecycle.
Establish a data foundation for innovation
GenAI now makes it easy for virtually anybody to put AI to work, which is accelerating the pace of business transformation at an exponential rate. And that’s an extremely powerful thing. With GenAI, you can drive more automation and save on costs. GenAI also can enable product differentiation to drive revenues. If you can become more proactive by using GenAI’s infinite knowledge and capacity to act quickly, you can fix problems and deliver better solutions faster.
But, in the process, GenAI is increasing the storage demands and extending the infrastructure concerns at enterprises far and wide. To stay competitive, modern businesses like yours must establish a data foundation for innovation – allowing your business to run, manage, and harness data and applications wherever they exist – on premises, in the cloud, and/or at the edge.
Whether your organization opts to leverage GenAI to save on everyday tasks, build revenue by delivering differentiated services or all of the above, keep in mind that getting GenAI right is both an art and a science. And it requires people and organizations to leverage both the knowledge earned with years of experience and the latest innovations in data management.
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