Successfully implementing Artificial Intelligence (AI) in the enterprise requires more than just tech skills. Here are three strategies to help you become a leader in AI technologies
Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike.
To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. 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 by 2023, a CAGR of 40.1 percent.
Business and technology-related courses make up 40 percent of these online courses. Many universities have also joined the drive to fill the AI leadership gap by offering high-touch executive education programs.
While upskilling programs are easily accessible, many executives are unsure how to leverage their newfound skills to advance their careers. Becoming an AI “practitioner” may not be the right option for some given the high technical bar for these roles. Others may rule out “juniorizing,” which could take them a few steps back in their career.
Organizational leaders who complete such programs commonly have three questions:
These aren’t simple questions to answer, and mid-career transformations are never easy. Based on my experience mentoring individuals and helping companies embrace AI-related technologies, here are three strategies leaders can adopt to reshape their careers.
Enterprises are carving out AI Centers of Excellence and adding emerging senior roles such as Chief Data Officer (CDO) or Chief Analytics Officer (CAO) to spearhead data and analytics. Gartner found that digital transformation at 72 percent of organizations surveyed was led or heavily influenced by data executives such as CDO. Senior executives who have acquired AI skills can reinvent their careers, shifting into a technology-centric role by leading AI initiatives within the organization.
These organizational transformations require leaders who can envision and strategize how to infuse analytics into the core of a business. They also need excellent execution abilities to build data and analytics teams, ensure fair and ethical use of AI, and help promote data-driven decisions. Overall, the role calls for a good mix of thought leadership, emotional intelligence, and conflict resolution skills.
NewVantage Partners found that 49 percent of companies preferred staffing such emerging data-centric leadership roles from within, utilizing people who can act as internal change agents.
Enterprises are implementing AI widely in both customer-facing offerings and internal operations. As companies become more analytics-driven, they need leaders who can champion AI-driven strategic initiatives within their function, promote collaboration across teams, and drive adoption by managing change. A critical differentiating attribute of these “AI orchestrators” is deep functional experience and a strong understanding of how to apply AI to generate business value.
When it comes to AI, most enterprises focus primarily on senior technology roles such as principal data scientist, AI manager, or chief analytics officer. However, success with AI is impossible without strong functional leadership. This lopsided focus could help explain one of the biggest challenges in AI today: While over 95 percent of organizations have invested in AI, only 26 percent report creating a data-driven organization. Business leaders must take the lead in selling their vision for AI, ensure collaboration across teams, and own the adoption of AI solutions in their business.
Accenture’s global study of more than 1,000 large companies points to the emergence of three new categories of AI-related jobs:
Let’s take the example of a traditional loan manager whose primary job is to examine, evaluate, and process lines of credit. The rise of online loan origination platforms like Rocket Mortgage could make loan managers obsolete. However, the credit and lending regulatory framework in the U.S. is currently not structured to enforce fair AI.
These loan managers can complement their existing knowledge to become “loan explainers” or “loan ethics managers” – people who can explain why a customer’s loan was denied by a “black-box” machine learning algorithm, for example. Similarly, this role can help lenders avoid potential discrimination issues and ensure compliance with the fast-evolving regulatory guidelines around the use of AI.
Picking the appropriate strategy from the three described above depends on a number of factors – your career aspirations, opportunities at hand, and your appetite for risk. Senior managers and executives must get creative by fusing their current strengths with newly acquired AI skills to lead in the age of AI.
It’s worth noting that leaders needn’t be constrained by opportunities that exist within an organization; they can pursue entrepreneurial opportunities in their industry. Right in the middle of the pandemic, venture capital funds have globally invested a record $268B in 2021. Startups can be an attractive proposition for experienced managers who combine a deep understanding of the industry’s under-served needs and customer pain points with the new possibilities unlocked by AI.
Today, career transformations are facilitated by easy access to upskilling programs, abundant social platforms to network with peers, and accessible ways to build a strong personal identity.
Leaders are often advised to build a personal brand through LinkedIn presence, news bytes in articles, and event participation. And while an engaging personal brand might make your profile attractive and get you more likes on social media posts, strong personal credibility will help connect deeply with people who believe in your work.
To boost your credibility, network with an intent to add value, develop a strong point of view on the application of AI to business, and be able to articulate your ideas in a compelling way. For example, the AI space is nascent and yet to see heavy regulations (though it badly needs them). If you have an opinion on how organizations and the community can responsibly adopt AI, share it. Engage in participatory regulations on the use of AI in your industry.
Building personal credibility takes time and effort. Ensure you learn continuously, engage consistently, and establish a coherent footprint. This can lead to better career options and a more fulfilling career.
Originally Published On: https://enterprisersproject.com/article/2022/3/artificial-intelligence-advance-career