Squeezing the Maximum Value Out of Generative AI

In many different areas, talent is an important yet often elusive goal. Just ask anyone whose piano keyboard skills have never moved beyond pecking out the first few measures of “Heart and Soul.” When it comes to generative AI, large language models (LLMs), trained on massive quantities of data, supply the capabilities needed to drive multiple use cases and applications, as well as handle an almost endless array of tasks.

To get the most out of generative AI, think of it as a tool rather than a replacement, suggests Daniel Wu, an AI research fellow at Stanford University in an email interview. He notes that LLMs can already do great work. “They’re being used in coding assistance and customer service, but they work best with clear prompting.”

Every organization produces large amounts of text as part of its normal business operations, observes Manfred Kügel, data scientist and IoT industry advisor for AI and analytics provider SAS, via email. Before LLMs, organizations needed to perform complex text analytics in order to get value out of unstructured text data, such as maintenance records or shift logs in a production environment. “LLMs can be used to structure text data and prepare it as inputs for machine learning models used for production optimization and predictive maintenance.”

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Pushing it to the Max

To gain maximum value from generative AI, users need to clearly define their problems and objectives, says Kevin Ameche, president of ERP software provider RealSteel, in an email interview. “Identify-specific use cases, such as content generation, data analysis, or automation,” he advises. “Then, ensure you can access high-quality data for training the AI model.”

Ameche recommends collaborating with internal or external AI experts to fine-tune and customize their model to align with specific needs. “Continuously evaluate and refine the model’s performance and stay updated with the latest advancements in generative AI technology to maximize its potential for your organization.”

To maximize generative AI’s value, users should first understand its inherent capabilities and shortcomings, Kügel says. “We are still in the early days of realizing the full potential of generative AI,” he states. Kügel believes that everyone involved in core business processes should interact with models in the same way they interact with their colleagues. “This will drive quick adoption and encourage organizations to provide the necessary and user-friendly generative AI tools to overcome any structural or cultural hurdles.”

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Achieving Effectiveness

Generative AI’s effectiveness lies in its ability to automate creative processes, generate content, and provide data-driven insights at scale, Ameche explains. “It can handle repetitive tasks, freeing-up human resources for more strategic work.” Meanwhile, the technology’s adaptability and capacity to learn from data make it a valuable tool in various industries.

An AI agent can’t read minds. “If you ask a poorly defined question, you’ll get one of any number of valid responses,” Wu says. “But by giving AI a stronger sense of what you’re searching for, either through clear prompts, data, or even model fine-tuning, you’ll get more useful responses.”

To empower team members, organizations should invest in generative AI training and development programs, Ameche says. “Start by identifying the specific skills and knowledge needed for working with AI,” he recommends. “Consider partnering with AI vendors or educational institutions for tailored training.”

Ameche believes that it’s also important to encourage employees to experiment with AI tools in real-world projects to gain hands-on experience. “Create an environment of continuous learning and provide access to resources, such as online courses, webinars, and AI communities,” he suggests. “Collaboration and knowledge sharing within the team can also accelerate the learning process, helping team members harness the maximum value from generative AI.”

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Common Mistakes

Wu notes there’s a common saying in AI research: junk in, junk out. “Users may inadvertently harm their projects by providing biased datasets or creating poor prompts,” he explains. “Model outputs should always be taken with a hint of salt,” Wu recommends.

Both over- and underestimating generative AI’s potential is a serious concern, Kügel says. “So is seeing AI as a threat when an AI model produced insights that we didn’t see ourselves.”

As with any breakthrough technology, Kügel sees skepticism among many IT leaders. He highlights that it’s important to clearly show that generative AI augments and supports, rather than replaces, human experts. He recommends taking a balanced approach to AI adoption by deploying guardrails and plausibility checks. “The model should report on its own when it drifts too far from reality,” Kügel says.

Final Thought

Generative AI holds immense potential for enterprises across many domains, Ameche says. “However, successful implementation requires careful planning, ongoing training, and vigilance to avoid pitfalls.” He believes that organizations should view generative AI as a tool to augment human capabilities, not as a replacement. “When used strategically and responsibly, generative AI can transform efficiency, creativity, and decision-making, driving innovation and competitive advantage.”