Future businesses will all be centered around technology and technology will be a core part of the business for every company. Companies must be bi-lingual across the language of business and the language of technology and both business and IT can fluently speak the languages of each other. Building technology proficiency in business must be a critical undertaking for every single company.
More and more companies have realized that to compete effectively in the future, they need to make technology a core part of their business. This implies that the company must be bi-lingual: business needs to be able to speak the language of technology and IT needs to be able to speak the language of business. It should be no longer business vs. IT. Instead, it is business AND IT. Only when both business and IT can fluently speak the languages of each other, a company can consciously and continuously identify ways to apply technology to change every part of a company.
To achieve this, companies must address the skills issue. A number of recent researches highlighted the urgency on this. For example, in a recent McKinsey Global Institute research on AI, McKinsey has found that investing in AI talent and training is a key differentiator between AI high performers and all other companies. Where 35% of AI high performers have reported investment in continuous-learning program on AI for employees, only 10% of all other companies have made similar investment. To address clients’ skill gaps in digital capabilities,
I led the launch effort for McKinsey’s Digital Academy.
What are the new skills?
The good news is that companies don’t need to start from scratch. They can build on a lot of existing knowledge and capabilities. The following table summarizes the key skills and what they can build on.
Let me pick a couple of subjects and double click into the details:
Even though words like product/process digitization and digital transformation sound intimidating, in reality, it’s about continuing to improve the business processes and products/services, now with more emphasis on using technology. Many companies have been relying on tools and techniques such as LEAN and Six Sigma for years and have achieved great results. The goal now is to continue to evolve those tools and techniques with the addition of newer concepts such as design thinking and data-driven process improvement.
Furthermore, technology can enable process improvement using simulation tools such as Digital Twin. In addition to traditional simulation tools such as statistical modeling, companies can now use technology to fully simulate how the future would look like. Even though Digital Twin was a concept that started in manufacturing, Digital Twin can be broadly applied to all facets of company operations. For example, for one of my clients, I led an effort to simulate new inventory management processes. We leveraged tools such as augmented reality to allow business executives to visualize how future parts can be received, tagged, used, and shipped.
Data is one of the areas that a lot of changes have happened over the last 5 years. While many business users were familiar with reporting tools such as Crystal Reports and data warehouse tools such as Teradata, data analytics is now about predictive and prescriptive analyses. Artificial intelligence and machine learning are additional topics that business users need to familiarize themselves with. To fully get the value out of data, data visualization is also a topic for business to pick up. Data by itself is probably going to be an academy of courses for business users to learn and master.
In the past, companies have relied on tools such as PMP certification to instill discipline and rigor in project management. With many companies moving towards enterprise agile where both business and IT will push Agile, companies now need to train both business and IT staff to understand how to do Agile, how to shift from projects to products and conduct product management, and how to do DevOps etc.
JPMC has mandated that new investment banking and asset management analysts must go through mandatory coding training. “Coding is not for just tech people, it is for anyone who wants to run a competitive company in the 21st century,” said Mary Callahan Erdoes, head of JPMorgan Asset Management, who learned to code at university. “These are skillsets of the future . . . By better understanding coding, our business teams can speak the same language as our technology teams, which ultimately drives better tools and solutions for our clients.”
Probability and Statistics
I know, when we discuss technology proficiency, not many people would be thinking about probability and statistics. Machine learning, however, is about using what happened in the past to predict what’s going to happen in the future. Statistics is the analysis of the frequency of past events and probability forecasts the likelihood of future events. A foundation in probability and statistics is a prerequisite for any effective machine learning practitioners.
How do we build new skills?
To build those skills, companies must segment learning into different levels and tailor curriculum accordingly.
- 101: Be aware: Awareness is about understanding the concepts. Many MOOCs (Massive Open Online Courses) on the Internet can offer great content for people to get basic understanding and even hands on exercises on subjects mentioned in Figure 1 above. Today’s MOOCs offer not only recorded lectures and reading materials, but also innovations such as offline team collaborations and peer reviews. Some of the leading MOOCs include edX, Coursera, and Udacity.
- 201: Be able to do and collaborate: The intermediary step of skill is so that the person can confidently be a part of the team and collaborate with the subject practitioners. Think as the Six Sigma Green Belts, now digitally capable – the new “Digital Green Belts”. The collaboration is not just about listening, it’s also about asking probing questions, debating pros and cons, and jointly reaching the decisions. Ideally, it’s also about the ability to do many of the activities, e.g., using tools such as Tableau to visualize data or use concepts such as design thinking to create new customer journeys.
- 301: Be a hands-on master practitioner: This is when the staff is able to not only expertly perform hands-on tasks on the subject, but also be able to explain the key philosophies and principles behind a number of the 10 areas laid out above in Figure 1. Examples could be to become a Product Manager in an agile set up or be a data translator and drive data analytics efforts. Ideally this is when an employee becomes the “Digital Black Belts”.
How to get started?
To get started, companies should take a top down and bottom up approach. Top down means to clearly articulate the capability building strategy to understand what skills are most needed, how to build those skills on top of the company’s current capabilities, and how to systematically build up the new skills.
The bottom up part aims to leverage existing in-flight initiatives and try to address them immediately. For example, one of my clients recently decided to replace all the Excels that have been floating around with advanced analytics and visualization tools such as Tableau and Microsoft PowerBI. They launched company-wide training to all the analysts on the tools. But they mandated one thing that made the training extra useful: every employee who attended the training must replace at least one Excel during the class, as a part of hands-on learning. This not only made the classes more impactful, it also helped reduce dozens of Excel spreadsheets after just a week.
Building new skills is not easy. But employees are eager to learn the new skills because they know their jobs are changing quickly. It’s a win-win for companies and employees. The most successful companies are the ones who can continue to evolve its business models and evolve its people to gain the latest skills.