9 Steps to Ensuring AI Use Is Safe and Mission-Focused

There’s no escaping near-daily headlines calling out the need for guardrails around artificial intelligence and large language models. The unknown power and speed of these nascent solutions spark fear and caution. Yet the appeal and potential of AI use cases is undeniable. Companies at the forefront of AI adoption have optimized processes, improved analytics, enhanced product discovery and introduction, and more.

However, adopting any technology for technology’s sake, particularly in the case of AI, is not a sound business plan. The need to establish both technical and process guardrails before authorizing or relying on AI models within the context of day-to-day decision-making cannot be overstated. Failure to protect company data or relying on AI blindly without oversight can result in massive financial and reputational losses.

For leaders, the goal should not be to stop teams from leveraging AI. Instead they should work with their teams to create a defined, responsible business process that governs all AI use, controls data flows, and taps the brakes on higher-risk applications.

Establishing and codifying guardrails around AI processes allows companies to control access to data, define acceptable use cases, assess risks, select and deploy the appropriate tools, and train teams to deliver on AI’s promise.

Here are nine steps leaders can follow to foster the safe and productive deployment of AI.

1. Start with a Clear Business Purpose

Without a clear roadmap detailing what the model, agent, or AI process is expected to accomplish and why—think reducing workloads or increasing team capacity—it will be harder to tie efforts back to ROI or the overall mission-critical KPIs. When defining AI’s purpose, consider the broader implications of organizational change. What roles will it affect? What training is necessary? How long will teams have to onboard and incorporate AI into their day-to-day processes?

2. Inventory AI Use Cases and Weigh Value Against Risk

List potential applications, estimate the time and cost savings, and assign risk levels based on what could happen if the system gets it wrong; for instance, hallucinated or incorrect outputs. Would product quality or efficacy suffer? What about timing for product delivery? What mitigations can you put in place?

3. Centralize Governance and Approvals

Don’t allow employees or teams to deploy AI tools on their own. Establish an oversight team or center of excellence to vet all AI concepts and provide pre-approval guidance. Eventually, this group should serve as a central validation hub for all AI tools and models companywide. This will help keep everyone working on the same goals and ensure multiple teams aren’t using conflicting versions of the same solution.

Include the right oversight functions, including IT/cybersecurity, risk management, compliance, quality assurance, and operations. Task this team with establishing standards for intake and prioritization, ROI scoring, testing processes, validation rigor, and team training. They should also communicate about pilots in progress to ensure visibility and awareness across the organization.

4. Establish Tiered (and Ongoing) Training

Distinguish between builders and consumers, power users and other user types, and align AI-related training to those roles. Consumers will need safe-use guidance, for example. Builders need to understand appropriate prompt engineering, evaluation, and architecture. Provide adequate time and space in the workday for this training to continue as the company’s AI use cases, compliance, or regulations evolve.

5. Verify the Underlying Data

Leaders need to understand precisely what data will be used, whether it is the appropriate data, and whether the model will use the data correctly. It’s not uncommon to see two dashboards seemingly built on the same data that come up with different results. Implement safeguards such as ongoing and repeated testing for hallucinations and deploying a failsafe model that can verify another model’s results.

Consider function- and industry-specific guidelines as well. For instance, finance must comply with SEC rules, and healthcare, HIPAA. Regulatory frameworks in many industries are adjusting rapidly to account for the shifts in data uses in the era of AI. The central oversight group should stay abreast of changes and provide direction.

6. Review Data Security and Confidentiality Needs

When employees access data by logging into a password-controlled system, that system typically controls what data they have access to. However, when they query an AI model, that application-level security is no longer available. A person may ask a question whose answer uses information that would not have been available to them before.

For this reason, companies must carefully evaluate which data to load into AI models and who can access them. Some organizations may decide the value of shared intelligence outweighs the risk. Others may exclude sensitive data or create separate model versions for different access levels.

7. Understand How Vendors Source and Handle Data

When using third-party AI providers, companies must understand how their data enters vendor systems, how it remains secure, and if the vendor intends to use it to train publicly available LLMs. Ensure the contract includes data protection, privacy, and security before moving forward with a third-party solution.

8. Don’t Mistake Polish for Accuracy

One of the easiest mistakes any team makes when using AI for the first time is to trust the output simply because it looks professional and well designed. AI can make anything look accurate, even if it’s based on incorrect assumptions or data. Get subject matter experts involved in a review and inspection process. And test results before deploying anything based on AI’s output.

9. Keep Humans Accountable

At the end of the day, human employees are responsible for the work they’re producing, whether or not AI helps them get it done. Ensuring everyone understands AI risks and outcomes, and that the buck stops with them, helps teams adopt and follow the approved AI business processes. And that will help keep the broader business and its customers safe and secure.