As an executive, the arrival and adoption of artificial intelligence in the workplace seems like a dream come true. A powerful, tireless, relentlessly self-improving asset that never asks for a raise, a promotion or a vacation? Sign me up.
Scratch beneath the surface, however, and you’ll quickly learn that — like most things that seem too good to be true — AI isn’t quite the “easy button” we’d like it to be. Why?
- Like employees, your shiny, new generative AI engine needs training. The more data you provide, the better and more personalized the results.
- You also need to be careful what you “feed” it. Without proper precautions, employees could train an OpenAI model with proprietary information (that’s hard to rein in once in the wild) or inadvertently introduce bias into the system by curating content from a particular point of view.
- Once you’re confident your AI is trained and ready to go, you’ve got to ensure you’re effectively prompting the system to execute and automate tasks. Get it right, and you’ll achieve new levels of efficiency and effectiveness. Get it wrong, and you’ll make high-volume errors at scale.
AI is unquestionably the most disruptive tech to hit the workplace since the advent of the internet and cloud-based computing, but it’s not a turnkey system. To maximize the value of AI — and to safeguard your business — you must have the proper foundation in place. And for all the promise and potential of the technology, the success or failure of your company’s AI strategy ultimately comes down to people.
Leveling the playing field: Getting teams comfortable with the tech
One of the most common challenges we hear from employers is that attitudes toward AI are somewhat “lumpy” across the business. Some employees have already embraced AI and are anxious to put it to work, while others are more apprehensive. This dynamic is understandable and relatable, but it’s a challenge that will grow and ultimately impede the forward progress of your team if left unchecked. Traditional video-based/self-paced training doesn’t measure up to AI. You need to take a more holistic boot camp-like approach:
- Learn as a team. Training as a team (aka cohort-based learning) ensures everyone is simultaneously engaged in the learning process. It produces greater consistency across the organization and, more importantly, ensures the entire team shares a common language and understanding of AI and how it will work in your business.
- Learn from people, not pixels. Human-led instruction, complete with coaching and mentoring, keeps students engaged, particularly with a topic like AI, which is vital to ensuring employees understand the practical application of the platform.
- Learn by doing. Portfolio projects — activities encouraging team members to apply their newly acquired skills to real-world challenges — ground the lessons in something practical, tangible and immediately relatable. Learners are supported throughout the project by a mentor. After the boot camp, they present their projects to fellow team members. This reinforces the lessons and improves their storytelling skills.
This human-led, hands-on approach is tailor-made for changes as sweeping as AI. It’s so easy for an individual to feel lost, but bringing people together with peers creates a shared experience, bolsters camaraderie and empowers leaders to level up entire organizations.
Data literacy, data analysis and strategic thinking
Now that we’ve covered how to train employees, let’s dig into what training should cover. Although “garbage in/garbage out” may be an old computer programming phrase, it’s very applicable to AI. Even the most sophisticated large language model requires the right inputs to function effectively.
As a result, before teams even begin applying AI tools to business challenges, they must first understand:
- What problems they’re trying to solve.
- What data should they use to train the model in pursuit of attacking that problem.
- What prompts they should use to get the best results.
- How to analyze the output and refine their actions to drive continuous improvement.
Each task is vital to successfully leveraging AI within the workplace, but many workers need more data literacy to make it all happen. Consulting firms are masters at teaching associates how to acquire and exercise this level of analytical and critical thinking, and an increasing number of employers are taking a page from their book.
Through the cohort-based learning model outlined above, a solid curriculum in data-driven strategic thinking teaches learners how to break down any problem into its constituent parts, explore and interpret data, and turn raw data into actionable information.
Over and above the immediate applicability to deploying and using AI tools, this training will pay dividends across the business and throughout each participant’s career. And during a time in history when skills gaps are at an all-time high and access to skilled knowledge workers is at an all-time low, investing in such programs for your employees helps attract and retain talent.
Chris Duchesne is general manager of Springboard for Business at Springboard. Springboard for Business redefines skill development, empowering leaders and their teams to master the digital marketing, coding, design, AI and data skills critical to success in today’s rapidly changing world. Through human-led instruction, collaborative learning programs and real-world projects, Springboard delivers a personalized experience with instructors, coaches and mentors focused on skills mastery, not general subject matter familiarity.
Opinions expressed by SmartBrief contributors are their own.