Practical Ways to Use Generative AI Tools

By Chelsea Barnes, Senior Manager - Data Science & Digital Transformation, Kalypso: A Rockwell Automation Business

According to our latest “State of Smart Manufacturing Report,” 83% of manufacturers surveyed anticipated using Generative AI (GenAI) in their operations. While ChatGPT and other GenAI tools carve the frontier of AI maturity, a common question among manufacturers is, "How do I put these tools into practice for my organization?”

Practical strategies for taking advantage of GenAI tools include the following:

• Identifying the problem before searching for a solution.
• Ensuring governance is part of your information security strategy.
• Opting for purpose-built agents vs. general-use models.

Problems First, Solutions Next

In terms of the Gartner Hype Cycle™ (see chart) GenAI sits between the Peak of Inflated Expectations and the Trough of Disillusionment. While some early movers lost hope from failed experiments, others still dream GenAI will solve all their problems.

By defining your business’s big bet and benefit use cases and aligning those goals to the right enabling technology, you can steer away from these missteps and walk on the desirable Slope of Enlightenment. To accomplish this, start by defining the use case and consider these questions:

• Articulate the problem statement. What challenge do we face?
• Identify the target personas. Who does the problem affect?
• Assess the potential value. How does the problem affect them?
• Determine the urgency. What consequences arise if we ignore the problem?
• Define success. How will our world be different if we solve the problem?

You can then assess the best tool available to solve the problem. GenAI’s power comes into play primarily for content consumption (summarization, search and other knowledge acquisition tasks), content creation (drafting, ideating, auto-filling) and in new technology creation (innovations made from multiple AI components).

Casual AI, or “traditional” machine learning (ML), serves more deterministic needs, such as forecasting, anomaly detection and outcome optimization.

Considering the best technology fit, let’s examine two sample contrasting approaches to getting started.

Wrong Path: Defining the Solution Before the Problem. “We have an issue with root cause analysis from quality incidents. Can we use a Large Language Model (LLM) to sift through and guide actions from quality incident data to determine the root cause?”

Although we stated a problem and postulated using an LLM for a language-based task (summarization and deduction), we concluded an LLM to be the solution before we fully articulated the problem.

This solution-first thinking could result in biasing the problem-solving process and potentially lead down the path of building a solution without sufficient ROI, especially if a simple solution such as an “if/then” rules engine is all that’s needed.

Right Path: Determining the Problem Paves the Way for a Solution.“We have an issue with root cause analysis from quality incidents. When the engineering team receives quality incident records for review, they find 50% of the records are misclassified and 60% are incomplete. This leads to unnecessary time rerouting incidents and hunting down people to fill in the missing information needed for root cause analysis.

“On the other hand, operators cite spending 15 minutes on average to log a quality incident, and when they are too busy, they only record the minimal information required. We need to make it easier for operators to properly record complete quality incidents and help engineers quickly understand and act on them.”

In the right path version, we clearly describe our challenge, the quantifiable business consequences, the affected groups and what we wish were better.

With all that information, we might conclude that two solutions are needed — the first is an LLM that can consume quality incident text and auto-classify the type of incident based on a predefined picklist. The second could be a combination rules engine and LLM to streamline the creation of quality incident records, with rules controlling required fields and the LLM offering auto-fill suggestions to speed up the data entry process.

A successful business case leads with the value proposition and then finds the right tool for the job. The tool should work with the structure and nature of the data (natural language, time-series data, hierarchical data structures, etc.), the output (probabilistic vs. deterministic), and the complexity of the problem.

Investing in Governance & Security

As you move from identifying use cases to putting them into action, prioritizing information security is crucial. The first step is choosing a platform that offers a private cloud version of these advanced models, such as Azure’s OpenAI Services or Google’s Vertex AI, for your enterprise. This ensures your intellectual property doesn’t contribute to training publicly accessible models.

However, security measures and safeguards against misuse must go further. Robust governance frameworks and multi-layered security protocols are essential to project your data, maintain compliance and mitigate risks associated with enterprise AI deployment.

Three categories of security and governance to consider include:

1. Data protection and access controls. Data anonymization mechanisms, role-based access controls, and other data-exchange safeguards help to protect against data leaks of sensitive information such as personally identifiable information (PII) and confidential company data.
2. Usage guidelines and guardrails. Ethical-use guidelines, prompt shields and output validations, for example, control how users interact with the model and act on it, mitigating risks such as bias, hallucinations or general misuse.
3. Auditing and performance monitoring. Third-party risk assessments of selected vendors, additional security testing for vulnerabilities, and continuous model-performance monitoring and updates add extra security layers to address the unique vulnerabilities presented by these novel solutions.

Integrated, Purpose-Built Agents

Many AI use cases seek to increase productivity, but AI alone is not enough to improve work processes. The most valuable productivity gain emerges when pipelines and processes are integrated to bring AI insights and assistance to users when and where they need it.