Key Best Practices For AI Adoption in Business

Although the U.S. Chamber of Commerce reports that 58% of businesses use AI, a phenomenon known as the “GenAI Divide” is prevalent. This term refers to how many businesses adopt AI, but very few see ROI. In fact, MIT reports that 95% of businesses that have adopted AI have seen zero returns on their investment. The good news is that you can be within the 5% that see results by strategically adopting AI in business.

Staying on top of changes while you roll out your strategic AI adoption process is also key. Businesses that wait for clear ROI benchmarks often fall behind competitors that are already learning how to use AI effectively. Early adoption allows teams to experiment, refine processes, and build internal comfort with the technology. 

To help you stay out of the “GenAI Divide,” the rest of this article will provide tips on how you can adopt AI in your business effectively. We also discuss common AI adoption challenges and explore what you can do to mitigate them.

Why Is Adopting AI in Business Important?

If so few businesses see ROI from AI, you may be left wondering why you should bother with it. While there may be some edge cases where AI implementation is not needed, the majority of businesses can still benefit from AI adoption. 

Adopting AI in business remains important because AI has already become embedded in the tools, platforms, and services that most companies rely on. Vendors now build AI into software for finance, customer service, marketing, logistics, and security. 

Businesses that adopt AI develop the ability to understand and control how those systems affect daily operations. Businesses that avoid adoption still absorb the impact, but without visibility or influence.

Additionally, there are still ways that you can benefit from these tools without seeing the results on formal ROI reports. For instance, businesses that integrate AI into workflows can build staff skills around it, which helps them adapt faster as markets change. 

How to Adopt AI in Business

1. Choose a Few High-Value Use Cases to Focus On

Instead of expecting AI to overtake all of your business processes, choose around 1 to 3 potential use cases. Consider the possible impact, implementation effort, data availability, and potential risks of using AI in each process. Then, zero in on the ones that offer the highest reward with the fewest risks and focus your AI adoption efforts there. 

2. Define What Success Looks Like For Each Use Case

Once you select your use cases, clearly describe what improvement you expect to see. Tie success to real work such as reduced manual effort, faster turnaround, fewer errors, or better decision support.

Having clear goals will drastically impact your AI project’s success. Gallup found that only 16% of employees actually find the AI tools that their employers implement useful. That same research showed that this disconnect comes from a lack of strategy. Employers that can show their employees clear goals for why they are using AI see 2.6 times higher internal adoption rates.

3. Decide How AI Will Be Used

Review the problems in your use cases and think about whether pattern recognition, automation, prediction, or summarization will actually help. Proceed only when these functions would clearly add value. AI cannot fix structural or organizational problems. In fact, it won’t deliver results without a solid organizational structure already in place.

If you have decided that AI is the best way to solve your problem, document how work flows today for each use case. Then, identify where AI will assist and where people still make decisions. Having these standards laid out will prevent people from wasting AI resources on tasks that it shouldn’t be used for. It also helps people see where the tools actually add value.

4. Prepare Your Data

Identify the exact data inputs the AI needs to perform its task. Clean obvious issues such as duplicates, missing fields, or unclear labels that could distort results. Limit data to only what is necessary, as this will make the tool’s outputs more reliable and easier to manage.

5. Run a Limited Pilot First

Launch the AI use case with a small group and live tasks. Keep the pilot contained and time-bound so results remain easy to evaluate. Capture feedback from users as work happens instead of waiting until the end to see where improvements can be made.

6. Expand Gradually or Stop With Intent

If results are promising and risk stays low, extend usage through phased roll-outs. If there is no added value, retire the use case and move on with lessons learned instead of forcing expansion. Forcing expansion in areas where AI is unhelpful will not make it more useful. It’s better to test alternative use cases to see if it adds more value there instead. 

Key Best Practices For AI Adoption in Businesses

Set Rules & Owners Before You Implement Anything

Assign a clear owner for each use case so someone remains responsible for results and decisions. Form a small review group that represents legal, privacy, IT, and the team that will use the tool, then involve them early instead of after problems appear. 

Also, write simple rules that explain which data and tools are allowed and when review is required. Remember to always treat AI as something you manage over time rather than a one-time setup.

Train People Before You Scale Tools

Many employees already use AI at work, yet very few receive any training on how to use it. Specifically, 75% of employees worldwide use AI for their jobs, yet only 39% have received any AI training. As a result, many organizations see poor results simply because their employees are using the technology incorrectly. 

Short, role-based training helps people apply AI to real tasks, reduces misuse, and creates shared expectations. Consider implementing this training before you roll out AI so that your employees are ready to help you optimize its value from the start.

Keep Humans in Any Process

AI outputs aren’t perfect. Despite sounding confident, these tools frequently make mistakes. The frequency of these mistakes will vary depending on the AI system you use. However, the concern is still prevalent. Research indicates that AI may be wrong in as many as 60% of its answers. 

Choosing more accurate tools is part of the solution, but even with the best tools on the market, human oversight is needed to reduce risk. Place a required review step before the work leaves your company, triggers a customer action, updates financial records, changes a policy, or affects hiring decisions. Having this step greatly reduces the risk that an error could become public-facing and potentially harm your reputation.

Limit Tool Sprawl

When teams use too many AI tools, leaders lose visibility into how work gets done. 59% of IT professionals report SaaS tool sprawl at their organizations. Don’t expect AI tools to be immune. Tool sprawl increases confusion, cost, and inconsistent outcomes.

So, provide a short list of approved tools with clear ownership to keep usage manageable. Central review and purchasing also make it easier to evaluate value and retire tools that do not support your goals.

Monitor Output Quality Over Time

AI performance quality can change over time, for better or worse. One study by Scientific Reports tested 128 combinations of AI models and data. Their researchers found that performance declined in 91% of cases after a long usage period, even when the systems had no major changes.

Source: Vela, D., Sharp, A., Zhang, R. et al. 

That’s why it’s so important to reassess your systems regularly. Track things like error rate, rework rate, time saved, customer complaints tied to the workflow, or approval/rejection rates. Also, when you change forms, data fields, scripts, or policies, rerun tests. New AI issues may come from internal process changes, not necessarily the model itself.

Challenges of AI Adoption in Business & What You Can Do About Them

1. Organizational Pushback

Employees often worry about how AI may affect their roles, how leaders will judge their work, or whether expectations will change without warning. These concerns slow adoption because people hesitate to engage or quietly avoid using new systems.

You can lessen this friction by explaining what AI will and will not change early. Clear role boundaries help people understand where human judgment still matters. When employees don’t feel threatened by the tools, they are more likely to actually use them.

2. Legacy System Integration

AI tools cannot easily connect to older software systems. Older systems often lack clean data paths or flexible connections, which forces teams to rely on manual steps. Requiring these manual workarounds reduces the value of AI.

Review your current systems before selecting an AI solution. A clear map of how data moves today helps identify weak points early. When integration costs outweigh the value of the use case, shifting focus to a better-fit scenario protects your time and budget.

Here is an overview of some legacy systems you should consider upgrading before you implement AI.

3. Data Privacy Concerns

Data privacy concerns arise when people are unsure about what data AI tools can access or how outputs may affect them or others. This uncertainty often delays adoption, especially when personal or sensitive data appears in the datasets the AI tool can access.

Address this concern by reviewing privacy needs at the start of your planning process. Clear rules around data use and limits reduce confusion later.

4. Vendor & Contract Risks

When AI features enter software agreements without clear terms, you may put some of your vendor contracts at risk. So, procurement teams should treat AI as a distinct capability during review. Clear contract language around data use, access, and retention reduces future disputes. Shared checklists across legal, IT, and business teams keep reviews consistent.

5. Underestimated Infrastructure Needs

Infrastructure needs often look small during planning but grow quickly during adoption. Usage-based pricing, data movement, and system upgrades add costs that teams did not expect.

Reduce this risk by modeling full operating needs early. Budget plans should include ongoing costs, not just pilots. Designs that allow usage limits and cost tracking help teams stay in control as adoption expands.

Need Assistance Adopting AI in Your Business?

InfoTECH Solutions helps businesses adopt AI with purpose and control. We focus on strategy first, so AI supports real work instead of adding noise. Our team reviews your systems, data, and workflows so AI fits into what you already use and trust.

We guide you through clear use cases, clean data, and safe rollout plans. We also support the IT foundation that AI depends on, including cloud platforms, security controls, and daily IT support. You gain practical guidance, steady oversight, and a plan you can manage over time.

Contact us today to tell us about what you want to accomplish with AI.