Table of contents
- 3 Ways Leaders Can Leverage AI Education
- Three challenges in building a career centered around AI
- 1. Transition to a tech role leading AI across the organization
- 2. Use AI functions to enhance practical expertise
- 3. Think outside the box to create new organizational roles
- put strategy into action
- Facilitate career change with AI by increasing credibility
3 Ways Leaders Can Leverage AI Education
Artificial intelligence (AI) is disrupting businesses and jobs in every industry , casting fears not only on low-skilled manual labor, but also on the long-term job security of managers .
To prepare for this AI-driven economy, many experienced managers and veteran executives are turning to Massive Open Online Courses (MOOCs) for basic data analysis and AI skills upskilling . is doing. This trend is unlikely to slow down anytime soon. The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion in 2023 at a compound annual growth rate of 40.1% .
Business and technology courses make up 40% of these online courses . And many universities are joining forces to fill the AI leadership gap (due to a lack of skills) by offering (courtesy) high-touch executive education programs.
Upskilling programs are readily available, but many executives are unsure how to use their newly acquired skills to advance their careers. Becoming an AI “practitioner” has high technical hurdles, so it may not be the right choice for your current job. Also, some might rule out choosing “juniorization” (for an AI career) as it can take a few steps backwards in their career.
- A Gartner report published in January 2020 states that robots will replace 69% of manager jobs by 2024 .
- According to a global report from Pega, a provider of AI-powered low-code operations, 78% of executives surveyed believe that increased use of AI and robots will degrade middle management.
Graphs created by translators based on Class Central articles
Three challenges in building a career centered around AI
Organizational leaders who have completed AI programs have three questions in common.
- Is it possible to transform into a technology leader leading your organization’s AI initiatives?
- How do I combine my hands-on leadership experience with my newly acquired AI skills?
- In today’s AI economy, what are the newly created (AI-related) middle careers that can be turned around?
These questions are not easy to answer, and mid-career change is never easy either. Based on my experience mentoring individuals (turned into AI management) and helping companies adopt AI-related technologies, here are three strategies leaders can adopt to rebuild their careers. introduce.
1. Transition to a tech role leading AI across the organization
Recent companies have created AI Advanced Institutes and added new senior roles such as chief data officer (CDO) and chief analytics officer (CAO) to spearhead data and analytics. Gartner found that data executives such as CDOs are leading or significantly influencing digital transformation in 72% of the organizations surveyed . Senior executives with AI skills can transform their careers by leading AI initiatives within their organizations and shifting to technology-centric roles.
These organizational transformations require leaders who can map out how to infuse analytics into the core of the business. It also requires strong execution capabilities to build data and analytics teams, ensure fair and ethical use of AI, and facilitate data-driven decision-making. Overall, the role requires a good mix of thought leadership, emotional intelligence, and conflict resolution skills.
A NewVantage Partners study also found that 49% of companies would like to bring in new and emerging data-centric leadership roles from within and serve as change agents within their organizations . (*Translation Note 7) .
Graphs created by translators based on Gartner articles
Graphs created by translators based on NewVantage Partners reports
2. Use AI functions to enhance practical expertise
Companies are embracing AI broadly in both customer-facing services and internal operations. As companies become more analytics-driven, they need leaders who can champion AI-driven strategic initiatives through practice, facilitate cross-team collaboration, manage change and drive adoption. A key differentiator for such “AI orchestrators” is deep work experience and a solid understanding of how AI can be applied to create business value.
When it comes to AI, most companies focus first on senior technical roles such as principal data scientist , AI manager, and chief analytics officer. But success with AI is not possible without strong operational leadership. This biased focus (focusing on specific senior technical roles) may help explain one of the biggest challenges in AI today. While over 95% of organizations are investing in AI, only 26% say they are building a data-driven organization. Business leaders must proactively sell their vision for AI, ensure collaboration across teams, and make their own decisions about adopting AI solutions in their businesses.
3. Think outside the box to create new organizational roles
A global survey of more than 1,000 large companies by Accenture found that three new categories of AI-related jobs are emerging.
- Trainers work with machines to teach AI systems to do effective and precise tasks.
- Explainers bridge the gap between AI experts and business leaders to provide clear explanations.
- Sustainers help avoid unintended consequences of AI automation and keep operations running. Managers and leaders with deep industry experience will be better prepared for the new roles of explorator and sustainer.
Take, for example, a traditional loan manager whose main job is to review, evaluate and process credit lines. The rise of online loan origination platforms like Rocket Mortgage could make loan managers obsolete. However, the US credit and lending regulatory framework is not currently set up to implement fair AI .
These traditional loan managers can supplement their existing knowledge to become a ‘loan explorer’ or ‘loan etiquette manager’. These people can explain, for example, why a customer’s loan was declined by a “black box” machine-learning algorithm. Likewise, the role can help lenders avoid potential discrimination issues and ensure compliance with rapidly evolving regulatory guidelines on the use of AI.
Graphs created by translators based on reports from the Brookings Institution
According to a Brookings Institution report, the foundations of today’s US lending laws were enacted in the 1960s and 1970s, when discrimination was rampant . If an AI loan screening system is built according to these outdated laws, the system risks amplifying racial discrimination.
Put strategy into action
Choosing the right strategy out of the three above depends on many factors, including your career aspirations, the opportunities you have, and your willingness to take risks. Senior managers and executives need to be creative, blending their current strengths with newly acquired AI skills to lead in the AI era.
It is worth noting that leaders can pursue entrepreneurial opportunities within their industry without being bound by the opportunities that exist within their organizations. In the midst of the pandemic, venture capital funds invested a record $268 billion globally in 2021 . Startups can be attractive businesses for experienced managers. They deeply understand that the new possibilities that AI unlocks will solve the unmet needs and pains of their customers in their industry.
Facilitate career change with AI by increasing credibility
Today, career change is facilitated by easy access to upskilling programs, rich social platforms to network with peers, and access methods to build a strong personal identity.
Leaders are often encouraged to build their personal brand by building a LinkedIn presence, writing articles in the media, and attending events. And while a compelling personal brand may make your profile attractive and get lots of likes on your social media posts, strong personal credibility means you’ll find people who believe in your work. It helps to connect deeply .
To increase credibility, build your network intentionally by adding value, develop a solid perspective on the application of AI to business, and be able to express your ideas in a compelling way. For example, the field of AI is still in its infancy and there is no heavy regulation yet (but regulation is sorely needed). Share your thoughts on how organizations and communities can responsibly adopt AI. Get involved in participatory regulation of the use of AI in your industry .
Developing personal credibility takes time and effort. Ensure continuous learning, consistent engagement, and a consistent footprint. Doing so will lead to better career options and a more fulfilling career.
