INTRODUCTION
In the first article of a multi-part series on AI adoption in the workplace, we explained the steps businesses need to take to assess and plan their AI needs. Using the findings from the audit of your workflows, it is now time to plan out and test AI pilot programs.
CHOOSE PROCESSES TO TEST WITH AI
Your processes should be ranked based on criteria such as feasibility, relative importance to the business, potential cost, etc. Another is having sufficient data so that your AI tool can recognize patterns, make predictions, and automate decisions. For example, a relatively new workflow that only has relatively little data may lead to incorrect assumptions or inconclusive results.
You also want to choose processes with minimal compliance or regulatory constraints. This will help lead to faster implementation, fewer risks and easier stakeholder buy-in.
Lastly, it’s typically better to choose processes that affect internal operations before customer-facing ones for controlled experimentation and in case something goes wrong that could damage your reputation with your customers.
With all these criteria, pick the top 1-3 processes for the AI pilot programs.
DETERMINE YOUR METRICS FOR AI
Now it’s time to create metrics for the AI objectives you previously developed (from the first article). Here are some examples:
- Objective: Use AI to analyze production data to reduce machine idle time by 20% in the next 30 days. Metrics: machine utilization rate, throughput rate, downtime events per shift
- Objective: Use AI to create personalized wellness and training programs that improve employee satisfaction by 20% this year. Metrics: employee survey results, positive reviews on Glassdoor, turnover
- Objective: Use AI to qualify leads and increase the average closing rate to 80% in the next quarter. Metrics: closed leads via calls, closed leads via email, closed leads via social media
RESEARCH AND SELECT AI TOOLS
When selecting an AI solution, start by researching available options that align with your specific requirements. Identify tools and platforms that address your key challenges while ensuring they offer the features and capabilities you need. You can use sources such as Gartner, Forrester, or McKinsey; browse AI tool directories such as G2, Capterra and Product Hunt; and even investigate what AI solutions your competitors are using.
Once you’ve shortlisted potential solutions, your cross-functional team should evaluate how easily they integrate with your existing systems to minimize disruptions and reduce the need for costly infrastructure changes.
Next, consider the level of vendor support, the quality of documentation, and the strength of the user community. A well-supported solution with accessible resources will make implementation and troubleshooting more efficient.
Regarding costs, expect to pay anywhere from less than $10/month to several hundred dollars/month, and make sure this amount is budgeted.
LAUNCH AND EVALUATE YOUR AI PILOT PROGRAMS
For cloud-based AI software, the process begins by signing up for the service and configuring API access to ensure seamless communication between the AI system and your existing infrastructure. If you’re opting for an on-premise solution, the software must be installed on company servers while ensuring compatibility with existing hardware and adherence to your company’s security policies.
Once the installation is complete, the next step is to connect the AI tool with critical business systems such as databases, customer relationship management (CRM) platforms, enterprise resource planning (ERP) software or other essential applications. This integration allows the AI to access and process relevant data efficiently.
After integration, the AI tool needs to be customized to align with business workflows. Settings should be adjusted to reflect operational needs, automation rules should be defined to streamline processes and user permissions must be configured to ensure appropriate access levels.
Now it’s time to run a pilot program for your processes, which means testing the AI software in real-world scenarios. Focus on the metrics you’ve set to assess both the effectiveness and usability of the AI solution.
Alongside performance metrics, gather qualitative feedback from the users about their experience. Are they finding the AI easy to use? Does it help them accomplish tasks more efficiently? Are there any pain points, such as system errors, user interface issues, or features that could be improved?
Throughout the pilot phase, maintain a feedback loop with stakeholders and the AI vendor to address concerns promptly and make adjustments in real-time. Regular meetings or check-ins with the pilot group can also help identify patterns or issues early on. If the pilot program proves successful, you can gradually scale the implementation while continuously improving the AI’s performance based on insights gained during the test phase.