We know AI and analytics improve business operations and provide a competitive edge in a multitude of use-cases. With that in mind, companies are taking steps to develop their people, focusing on key data skills and even standing up their own internal AI academies. Some are working with MOOC platforms—like Udemy, Coursera or Udacity—and essentially paying for subscriptions for their employees to have access to whatever they want to learn. Others are pursuing a more structured plan where there's a very specific curriculum that they've assessed and want their people to learn. Whatever the approach, scaling AI and fostering a data driven culture in a real, sustainable way can be challenging.
At AlignAI, we’ve been observing various companies’ approaches to AI education and taking note of what’s getting in the way of broad success.
Coordination and Consistency
While many companies are providing AI education resources to their employees, the expectations are unclear to the individual and guidance is generally lacking. Optional training is available for employees who want to upskill themselves when they have the time. Purely on-demand training is asynchronous so individuals often feel alone in their learning path and struggles.This ad hoc approach fails to engage, get everybody on the same page and coordinate learning with jobs to be done to drive real impact.
When companies provide resources without coordination, we see more of a top-down push structure as opposed to a need-based pull structure. Employees are encouraged to pursue training to learn a general topic rather than to learn specific processes and techniques that will help them with an actual work problem or project. Online content is overly general because it’s intended to reach a massive audience. Because of this, employees find it difficult to apply what they’re learning to tasks and projects that they are facing right now.
People typically see training as a “nice to have” that’s completed on an individual basis. Companies are focused on keeping the lights on over education and learning. Left to their own devices, most people will let training slide to the bottom of their to-do list. That’s why you see MOOCs with 3 percent average completion rates. Even when companies support something like a centralized resource center for training, the expectation is on employees to not only do their job but also learn new capabilities on their own time (usually nights and weekends). Employees are overwhelmed just trying to do their job while staying on top of other tasks, a factor contributing greatly to burnout and attrition in the industry.
Convenience and Support
It’s one thing to offer training resources to employees, it’s another to make it easy for them to use what they learn and change how they work. We don’t often see internal communities and spaces that allow employees to talk about what they’re learning, share notes and reflect on lessons learned. This company-specific knowledge is so necessary for that ‘last mile’ of learning application on the job. We’ve been surprised to see that many senior employees want to be teachers to their colleagues, but they are burdened with their daily workload and there is no convenient way for them to share their knowledge within the organization in an efficient way.
Throughout our observations at AlignAI, we’ve also seen companies succeed with specific strategies and partnerships.
As leaders plan for and implement an org-wide strategy to have all employees go through an AI education process, they're thinking of it like crossing the chasm. This is important and has to happen at a very specific time inside of a company. There's still plenty of room, however, for incremental improvements that will make a team more effective and efficient. So many leaders overburden themselves trying to create this all-encompassing AI Academy with one big bang, when really it pays off to make incremental changes over time and communicate those quick wins.
One way to start AI education is with a pilot group of individuals that are hyper focused on making an improvement and a very specific capability. Similar to building and designing an AI model for the first time, you have to find that first use case that’s going to prove its value and foster buy-in. It’s no different showing people what coordinated efforts of education and training inside of a company looks like and how it's beneficial. Start with a small team and topic that you know is going to provide value and make that incremental change inside of the company. When you start seeing results, communicate them to the rest of the stakeholders across the organization and get more people involved. We’ve seen success when a team goes through AI education together before starting a project because the team is aligned on common methods and language to work more effectively together. We’ve also seen success in starting with a group of leaders, whether it's middle managers, a set of directors or even executives, so they understand what their employees will be learning and can better support them as they're going through the training.
Work Backwards from a Project
The more a company can tie AI education back to a specific job to be done, the better. Bringing in outside perspectives is important, but exercises and examples specific to the task at hand make the training real and tangible. Working backwards from a project is a great model that we recommend. Identify projects where these types of skills can be applied immediately. Centralize those individuals who are responsible for getting the projects across the finish line so they are all on the same page, taking the same curriculum and growing together. Share with the rest of the organization how they’ve applied newly acquired skills to those projects that are on the horizon or in flight.
There are so many wins in terms of helping individuals learn together. A great networking exercise is to have employees put together an internal case study as they complete projects, something as simple as a science fair style poster. Employees can connect about similar projects and ask questions about the process and hurdles. It gives folks a way to learn from each other while also building an archive of past projects to show how teams have developed these new capabilities over the years. Setting up these smaller communities inside of a company gives people a place to advocate, ask questions and teach each other about analytics and AI. We’ve seen topic-specific councils that are created after employees complete a specific course or analytics centers of excellence (CoEs) that are stood up organically as teams learn and apply new skills. Across companies and industries, people want to collaborate and solve problems. That’s so valuable to an organization’s overall culture and helps with capability uplift and employee retention.
Change the Culture
It’s not enough for a company to just say it wants to be more ‘data-driven’. There has to be an intentional effort to show employees the importance of data in making decisions to drive that kind of change. Executives need to lead by example from an education standpoint and spotlight projects where they’re making key decisions based on data. There’s also intrinsic motivation from showing employees what’s in it for them as individual contributors and how data makes their job more interesting. We’ve seen organizations carry through on their commitment to upskill existing employees by tying 10 percent of personal development to AI training. We’ve also seen more advanced organizations implement some form of AI education and AI connection to strategy as part of their new employee onboarding process. Whether it’s an individual that is entering a company for the first time or growing into a role, undergoing up-front training and education will get them up to speed on what the company is trying to accomplish, accelerating their productivity as they continue to learn on the job. The other part of the culture change is recognizing that everyone touches data and everyone needs to be accountable for it. That is where a lot of companies are not making the entire shift. They may be starting with the folks that are building models, but the education must go far beyond that to reach the general population of the organization. There is a huge fluency component and a huge change in roles and responsibility. That doesn't just happen overnight.
AI education benefits more than the individual employee and their career goals. The right training can be highly beneficial for the company when aligned with a strategy and integrated in the employee’s flow of work. If companies are more intentional about including analytics and AI education as a part of projects and process improvements, they’ll move from firefighting to being proactive with a competitive edge in the market. We’ve seen significant AI transformation happen when employees leverage education as the baseline for methods, best practices and terminology. If enabling and scaling AI is the goal, consider the people that will make it happen.
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