Most companies understand the potential of generative AI but struggle to realize its ROI. While adoption is high with 50% of organizations reporting that employees use the technology daily, skills training lags behind with only 26% of companies prioritizing upskilling.
Without proper workforce education and application, much of generative AI’s ROI will remain dormant. Below are best practices from a recent webinar where leading technology experts Mark Palmer, Diana Spehar, and Matt Hoffberg shared how to turn generative AI anxiety into action.
Generative AI integration has many moving parts from use case identification to employee training and change management.
To maximize its impact, use the framework below to assess your organization's maturity level. If you’d like a free AI assessment from a Correlation One expert, schedule one here.
The possibilities generative AI presents are vast and alluring but make prioritization difficult. Below are sample recommendations for where to start based on your place on the maturity curve.
Stakeholders have a limited understanding of AI and no clear methods for implementation. There are basic IT systems set up, but they aren't yet integrated with generative AI platforms.
The workforce is trained on basic generative AI principles, and the organization has experimented with automation for mundane tasks. There are no dedicated generative AI roles yet, but recruitment for the talent is underway.
The company has a growing dedicated AI team, and the technology is being integrated into all workflows. Ongoing workforce AI training programs and data governance practices are in place organization-wide.
There is a highly skilled AI team working on driving efficiency and growth. Investment in generative AI tools is well underway with the technology integrated cross-functionally.
Top-tier generative AI talent exists within the organization and workforce proficiency has been achieved. The company has invested in cutting-edge generative AI platforms and the technology is a core part of the business strategy.
Organizations eager to amplify the impact of generative AI need to ensure that employees learn in a context that directly relates to their professional day-to-day.
This includes customizing training based on an employee’s role, industry, tech stack, skills gaps, and organizational goals.
Generative AI training efforts should also be implemented across teams and departments to avoid:
To ensure that newly developed generative AI skills make their way to practical application, organizations must provide opportunities for employees to put learnings into practice.
Capstone projects are an excellent way to achieve this as they empower employees to apply generative AI to internal workflows for greater efficiency and ROI.
Below is an example of the time and cost savings that capstone projects produce for Correlation One clients.
This Correlation One client is a Fortune 100, consumer packaged goods (CPG) enterprise that uses several data sources for its regional reporting. Before generative AI application, their process was highly manual and required eight hours per region.
That is until an employee made it the focus of their generative AI capstone project. To reduce human error and drive efficiency, the client’s commercial team first analyzed and cleaned regional data with ChatGPT. They then used ChatGPT for:
The results of the capstone project were 60+ hours of time savings, no more data gaps, and a shift to data-driven insights instead of report generation.
Successful generative AI integration goes beyond just adopting the technology. It requires a well-rounded strategy that prioritizes workforce education, tailored training, and real-world application.
By assessing your organization’s AI maturity level and implementing upskilling best practices, such as tailored training and capstone projects, you can realize the full value of generative AI and drive measurable ROI.
Keep your organization moving forward on generative AI with the How to get started with generative AI toolkit.