The recent convergence of information technology (IT) and operational technology (OT) has ushered in a new era of efficiency, collaboration and innovation for industrial organizations. This departmental merger has also necessitated a new kind of workforce, with companies in search of highly skilled teams capable of navigating both domains.

But many organizations face a critical roadblock to IT/OT convergence: They lack the talent to get there. The tech talent shortage is so dire that only 65 out of every 100 open roles are getting filled—and four in 10 companies expect employee shortages to worsen in the next five years.

Organizations are taking varied approaches to fill critical roles, whether it’s boosting compensation for new hires, upskilling current employees or broadening recruitment methods. But there’s a promising solution that’s mainly gone untapped: generative AI.

Advancements in generative AI hold the power to augment and amplify the capabilities of existing employees—filling many of the open skills gaps across the industrial sector without overreliance on new talent alone. The question is: Do teams know how to use these tools effectively?

Why Organizations Struggle To Find IT/OT Talent

Right now, it’s hard enough to find technologists to fill a single team. The problem compounds when there’s the added layer of bringing together IT and OT staff and technologies. However, understanding the complexities common to hiring environments underscores how generative AI solutions can best support efforts across industrial organizations.

On the IT side, specialized talents like software architects, ML engineers and software security experts are already difficult hires to make. But, finding the right talent becomes even more challenging when organizations are looking for an ML engineer with sufficient OT domain knowledge in addition to more standard IT expertise, e.g., the ability to support a complex chemical process managed across facilities.

OT responsibilities, on the other hand, require in-depth expertise and domain-specific knowledge in areas such as safety protocols, compliance standards and the integration of hardware and software systems across facilities. Add the need for an OT professional who knows how to leverage containerization and other cloud-native development practices, and it’s easy to see why many organizations can’t find (or afford) the right person for the job. While generative AI tools can’t solve the IT/OT talent shortage completely, they alleviate some of the hiring pressure companies feel. Through effectively harnessing this technology, organizations can amplify the capabilities of their existing teams to drive efficiency, enhance productivity and achieve more fully integrated IT/OT operations. For example, generative AI tools can help automate IT/OT knowledge retrieval, reduce the breadth of knowledge necessary for a role and increase the pool of candidates who may be qualified for a position.

What IT/OT Leaders Should Know Before Using Generative AI

To harness the full potential of generative AI, industrial organizations require a holistic strategy that accounts for the technology's nuances, its evolving nature and both its benefits and limitations. Here are three baseline considerations for organizations interested in adopting generative AI tools.

1. Be aware of the rewards—and the risks.

Generative AI is rapidly evolving, and like any other emerging technology, each stage of evolution introduces unique capabilities, opportunities, and challenges. It’s important to understand the possibilities and risk profile of generative AI applications for every use case across both OT and IT settings.

For instance, IT employees can leverage generative AI for code generation and enable code translation from one programming language to another. But if the AI model uses training data that includes non-permissive open-source code or unsecure code, it could generate code that falls under these licenses or leads to detrimental effects when deployed in production.

Leaning on generative AI to accelerate coding efforts may still be the right choice, but it should be overseen by employees who are aware of common pitfalls and prepared to navigate potential automation issues.

2. Build a robust data infrastructure and security plan.

AI solutions rely on massive amounts of data to train and refine models. That’s why it’s vital to develop a centralized, streamlined and secure data backbone that can support AI applications and any future integrations.

For many organizations, a good starting point is a data historian, which provides a dedicated, centralized repository that collects, stores and disseminates data from varied origins across the organization. A strong data strategy also includes privacy and security roadmaps.

Consider an OT team that uses AI to design more efficient manufacturing operations or refine a patented process. In this scenario, AI tools rely on continuous learning, which can lead to unintentional IP leakage if left unchecked. Consistent and secure data processes help prevent such issues and ensure competitive advantages are never inadvertently exposed.

3. Blend technology and human expertise.

There are some tasks AI handles better than humans, and other areas where people outperform digital tools. But most often, AI and employees work best when paired together—and it's crucial teams blend human expertise with technical prowess.

Industrial data scientists who possess a combination of domain expertise and AI proficiency can better understand the unique challenges of IT/OT, more effectively leveraging generative AI solutions to address problems over time

Likewise, employees can provide guardrails and support to ensure generative AI tools and use cases work as intended. For example, recommender systems that rely on large language models can produce results that sound plausible but are incorrect, making it necessary to have an expert in the loop to adjust the question or select and modify answers.

Conclusion

The ongoing tech talent shortage doesn’t just affect individual teams and divisions. It impacts digital strategy enterprise-wide. Skills shortages are the biggest barrier to the adoption of 64% of new technologies.

Generative AI is one of many solutions industrial organizations have at their disposal to ensure IT/OT plans maintain momentum and performance across both domains remains cutting-edge. With the talent shortage showing no signs of slowing down, companies can’t afford to let their digital plans fall behind, and generative AI offers a way to make sure they don’t.

This article originally appeared on Forbes.com