In today’s tight job market, human resources departments are seeking effective, cost-efficient ways to retain top talent, and study after study has shown that today’s employees are looking for organizations committed to championing their professional development. In fact, recent Gallup research has found that nearly 60% of millennials cite learning and development (L&D) opportunities as critical factors in their job searches, and Deloitte has found that 70% of young employees planning to leave their jobs cite a lack of development opportunities as a key reason for their departure.
If that’s the case, it follows that businesses would be clamoring to design and implement leadership development programs to both improve their employees’ skills and entice their talent to stay. Currently, it seems they do see the value in L&D. LinkedIn’s 2017 Workplace Learning Report shows that 90% of leaders consider L&D as critical to closing the skills gap. However, the follow-through isn’t there: The same report shows that only 8% of leaders see the impact of L&D on the organization.
So, why the disparity? L&D programs are traditionally a risky investment, primarily because they fall into one of two categories:
• Personalized leadership or skills coaching that’s prohibitively expensive for more than a very few select executives.
• Out-of-the-box workshops or computer-based trainings that are meant to be a one-size-fits-all program but, in reality, are far too generic to create any lasting change.
The programs in either category offer very little immediate (or long-term) evidence of return on investment (ROI), making it difficult for HR departments to discern whether their investment is making a difference in the organization and its people.
There must be another option. There must be a way to offer detailed, personalized feedback to large groups of employees without busting the budget — and there must be a way to track those employees’ progress to demonstrate the business value of a quality L&D program.
The Answer: AI-Driven Learning and Development
In recent years, we’ve been able to harness the power of machine learning and other forms of artificial intelligence (AI) to optimize a number of workplace systems and processes, from recruiting and hiring to billing to customer service and beyond. What’s exciting is that we can now do the same with L&D initiatives.
Advances in behavioral science give us the power to design programs that provide data-driven feedback and lasting improvement at scale, giving savvy organizations a true competitive advantage in today’s corporate landscape by empowering them to provide employees with the learning opportunities they crave.
These innovations are grounded in one key idea: that technology can accurately assess the performance capabilities of people significantly better than we can. This empowers L&D teams to take the human coaches and workshop facilitators out of the equation with programs designed to measure users’ proficiency in a given area and offer adaptive, personalized learning at scale.
Innovation over the past eight years in technologies like natural language processing, vocal recognition and facial analysis means that machines can assess, as accurately as a human coach, a professionals’ predicted impact on their audience, their team and/or their clients, and then respond with specific feedback, insights and actionable recommendations for improvement tailored to each user’s unique strengths and development areas. This is one example of innovation driving the capability for improved communication skills.
Imagine if you could take your new salesperson and use behavioral analytics to assess their five-minute pitch against the top 10% of salespeople in your organization, then deliver them a personalized development plan. If you do this for all your salespeople and continue to measure and tweak the feedback, then the system only gets smarter and faster.
Of course, given the reputation of outdated computer-based learning programs, the idea of taking the human experts out of the equation does tend to raise eyebrows. However, we know from research and our own experience with coaching both the “old” way and through analytics, that automating the learning process through these data-science and AI-driven programs leads to a more personalized learning experience — and more measurable results — than traditional, subjective coaching methods or content-only approaches.
3 Ways Machine-Driven Learning Outperforms Traditional Coaching
1. It’s Objective.
Professionals who rely on data and research to make every major business decision are more apt to accept data than opinion, no matter how experienced the coach. After all, for somebody with a mind for facts and statistics, “You didn’t seem very confident” sounds less compelling than, “Your latest presentation scored 30% less confident than average, because you used more tentative language than usual, including these five words…” The former feedback is subjective — maybe the audience felt differently — but the latter is concrete, data-driven and paired with an insight that can help drive improvement in the future.
Additionally, with data, learners can track their progress meticulously, watching their scores increase rather than relying on subjective comparisons from peers and leaders. It’s one thing to hear a coach tell you you’re getting better, but it’s another altogether to look at a chart showing measurable progress in distinct areas and highlighting areas that need improvement.
Finally, objective scores can be modeled against performance outcomes, so you can demonstrate efforts’ true ROI — in areas like performance, engagement, retention, selection and more.
2. It’s Impersonal.
Traditionally, giving feedback — in any area, from leadership to job performance to public speaking — has been a highly personal and subjective activity. No matter how skilled the coach or constructive the feedback, learners tend to take evaluations personally, dwelling on the negatives and defending their performance rather than focusing on the recommended solutions.
When it’s data rather than a human delivering the tough love, however, it creates a safer space in which learners can accept feedback. No longer is the learner hearing the coach’s perspective (which too often sounds like a judgment) but, instead, is an seeing objective analysis free of emotional impact. This means that, when trainers and coaches do get involved, they aren’t the “enemy.” Instead, they’re allies using the data to build a healthy coaching relationship on the foundation of improvement and progress.
3. It’s Scalable.
Elite training has historically been too expensive (easily adding up to thousands of dollars per day) for organizations to provide to anyone but C-suite executives. Alternatively, with machine-driven learning, powerful improvement opportunities exist within an arm’s reach for every member of an organization. Individual data and insights support improvement for dozens, hundreds, or even thousands of participants, and aggregate, group-level data can give team leaders or HR executives a holistic look at the group’s performance as a whole, helping them identify areas in which the entire team could benefit from a little extra support.
In today’s business world, the best talent is eager to continuously learn and improve. That is, after all, one of the characteristics that makes them such strong employees and team members. By withholding opportunities for development, companies are doing themselves a double disservice: They’re driving away their top talent, and they’re also stunting their own growth by preventing employees from learning new skills that could take their productivity, engagement and creativity to the next level.
While, once upon a time, there was an excuse for refraining from making significant investments in training, today’s technology allows companies of all sizes to provide world-class, personalized development programs to every team member, tracking measurable ROI, improving employee engagement and retention, and gaining an edge over the competition — all without breaking the bank.