AI Integration: Signals, Pitfalls, Next Steps, and What to Expect

According to the CBIZ Middle Market Survey, companies across industries view increasing efficiency as a high priority over the year ahead. Among firms that achieved meaningful efficiency improvements over the prior 12 months, many attribute “significant efficiency gains” to automation and process improvement. Encouragingly, 96% of leaders that made investments in automation and digital transformation felt the investment (in terms of technology and training costs) were worthwhile.

Notably, the Middle Market Survey didn’t specify the nature of the automation and process improvement technology that yielded such positive responses. It’s possible these were largely investments into ERPs and more traditional business software programs. Still, the efficiency boosting promise of AI tools continues to draw the attention of investors, developers, and businesses eager to benefit.

Diagnosing Mistakes and Learning from Integration Failures

Around this time last year, many of the first businesses to embrace AI were entering unchartered territory. Now, businesses can look to 12 months or more of high-profile projects to determine how to plan their own approach to AI, mitigate risks, and unlock the technology’s efficiency improving potential.

In supporting AI integration at the enterprise level, the CBIZ technology team has identified the following signals, pitfalls, and next steps that can help AI ambitions become successful implementations.

Signals

AI technology is not easily comparable to other enterprise tools. Where digital meeting rooms and interoffice chat software eased communication, they were comparatively simple enterprise level deployments within an existing technology stack. AI is a fundamentally different animal with demands that are nearly as intense as its capabilities.

That shouldn’t be taken as a reason to avoid AI, instead, it’s a reason to prepare carefully and thoroughly across your organization before embarking on AI adoption. That means designing sufficient governance and controls, security, and training initiatives with enterprise alignment that goes well beyond your IT team.

Here’s how you might know you’re ready to undertake a successful AI integration process:

• You are determined to realize and have identified clear measurable (AI’s) efficiency benefits;
• You have specific, well-defined, and realistic use-cases in mind;
• You have the time and funding necessary to undertake an aligned change management process, inclusive of staff training, strategy development, and a cross-department AI readiness assessment;
• Strong data practices have generated large quantities of structured, consistent, and uniform data to inform your AI; and
• You recognize the risks associated with the unsanctioned use of AI and want or need to offer an approved, capable, and secure platform to protect sensitive information.

Pitfalls

There are always potential pitfalls associated with new technologies. These are often the result of users misusing new systems or lacking the technology-specific skills to realize their value. With AI, the risk calculus is much more complex and the reputational and monetary damages can be more severe.

Common pitfalls of AI adoption include:

• Implementing AI or allowing employees to use free or low cost commercial models without a strategy, governance and controls, security protocols, use policies and training programs in place;
• Adopting use-cases that take too long to realize benefits or too complicated to measure and track;
• Applying AI toward tasks it is not well-suited to handling;
• Allowing AI to access sensitive information or complete transactions without a human verifying, overseeing, and permitting influential actions (e.g., wrapping proper controls around the platform); and
• Neglecting to cultivate the accurate and uniform data your AI needs to generate useful outputs.

It is essential that anywhere AI is employed, its output is reviewed and verified by a human. In addition, businesses often overlook the data practices that are essential for AI to succeed. That means that data is collected, validated for structure and accuracy, and available for your AI to analyze.

Next Steps

With knowledge of how you plan to use AI and where common pitfalls occur, it’s time to develop a roadmap to adoption.

First, make sure your AI strategy centers the primary motivators behind your adoption efforts. That means building a strategy around use-cases that make sense for your business, baselining performance, and measuring the success or your project. Then, avoid costly missteps by preemptively controlling for pitfalls like those detailed in the previous section.

Consulting with technology professionals can help ensure the resulting AI strategy is appropriate. There are a lot of unknowns with a technology as complicated and capable as AI, so advice from those with specialized knowledge goes further the earlier it is available.

AI relies on the accuracy and availability of data to generate useful results. Your data collection and management practices will likely require a good deal of attention in the preliminary stages. The importance of data quality can not be overstated and refining your data practices may be the single most intensive step in setting the stage for a successful implementation.

Next, make sure your governance and controls reflect how you are and are not comfortable using AI within your organization. That may require baking in new standards for using the technology ethically and in compliance with developing AI laws and regulations (e.g.,NIST Risk Management Framework.)

Finally, you’re ready to select the platform that best suits how you plan to use AI. Many of the current Generative AI platforms are very similar. It is important to pick one that aligns with your technology stack and your objectives.  CBIZ has developed its own platform, Vertical Vector AI, designed to meet the needs of middle market businesses by prioritizing security features.

What’s Ahead

Many see the next stage of AI in promising agentic applications. Agentic AI refers to AI systems that can autonomously set goals, plan, take multi-step actions, and adapt based on feedback, rather than merely responding to single prompts. These agents direct tools, call other AI models, monitor progress, and self-correct to accomplish complex tasks. It is seen as a major next trend because it turns AI from a passive assistant into an active problem-solver that delivers outcomes with minimal human supervision.

Regardless of what is to come, taking advantage of AI’s current capabilities requires paying attention to the signals your business can benefit from AI, understanding and addressing the common pitfalls, and developing concrete plans to embed it in your organization.

If you have any questions about your current approach to AI, or how to optimize your capabilities and maximize the benefits of the technology, contact a CBIZ professional today.