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December 4, 2023
On the first anniversary of ChatGPT in a year that saw generative AI go mainstream, I thought it essential to examine the impact of the technology, and AI more broadly, on the workforce while providing recommendations to stay ahead of the coming transition for individuals, companies, educational institutions and governments, as each has a role to play in raising the AI IQ of society.
Artificial intelligence promises to profoundly transform economies and workplaces globally in the coming years. According to a report by McKinsey, AI technologies could deliver more than $13 trillion in additional global economic activity by 2030. Additionally, generative AI tools such as ChatGPT are estimated to contribute an additional $4.4 trillion in the same timeframe. These projections highlight immense opportunities in AI, including higher corporate profits, productivity gains, innovative solutions and new ways of working.
However, any rapid technological shift inevitably brings some disruption.
As AI automates specific tasks and jobs, McKinsey expects that 15% to 25% of current work activities globally, equivalent to 400 million to 800 million jobs, could be displaced by 2030. McKinsey goes on to predict that this workforce evolution driven by generative AI and automation will occur in over just a 10-year transition period − an unprecedented pace compared to prior workforce transitions over more extended timelines.
Unlike past industrial revolutions focused on manual labor alone, experts warn this AI shift could impact white-collar office jobs as much as blue-collar ones. For individual workers, transitioning to entirely new roles or evolving responsibilities may require focusing more on uniquely human abilities.
The good news from prior workforce shifts is that technologies often create more prosperity and jobs long-term than they initially eliminate. However, the transition brings growing pains that require collaboration on reskilling and training. Preparing for AI's impact will demand urgent action and investment from governments, educational institutions, businesses and individuals. Schools must equip students with versatile, future-proof skills and hands-on AI experience. Companies must provide ample reskilling opportunities and internal mobility. Policies should remove barriers and incentivize adult-learning programs. Individuals must take ownership of the development of lifelong skills. With preparation, organizations and workers can harness AI to unlock new potential rather than simply react to disruption.
An important aspect that makes this AI workforce shift unprecedented is the compressed timeline. McKinsey predicts much of this disruption could unfold over a decade. To put that into perspective, here is how it compares to the pace of past workforce evolutions:
First Industrial Revolution (1760-1830) - Spanned approximately two to three generations for the workforce to adapt
Second Industrial Revolution (1870-1914) - Spanned about two generations
AI Revolution - Predicted to affect jobs over the next 10 years
This expected, rapid change means today's workers have only about half a generation to reskill and transition to the AI-powered economy. Educational systems, government policymakers, corporate training programs and social safety net structures will all need to undertake rapid adaptations at an unprecedented speed to address the workforce disruption driven by advances in generative AI over the next decade.
The condensed timeline makes urgent, coordinated action critical. Unlike past transformations centered on manual labor, the breadth of impact spanning white-collar and professional roles means no workforce segment can ignore this trend.
Education plays a critical role in equipping future generations for the AI-transformed economy. Most current curricula and teaching methods need to align with the future needs of students in a technological landscape transformed by advances like generative AI. Educational institutions must incorporate new technologies like generative AI into learning faster. This reluctance is counterproductive. If the purpose of education is to prepare youth for the workforce and full participation in society, then curricula must evolve to provide technology literacy relevant to the modern world. Students need exposure to AI through hands-on learning so they gain practical experience.
Here are some recommendations for how educational institutions can reshape learning by building AI IQ for the AI age:
Incorporate AI instruction into curricula and requirements at all levels, from primary school to university. Teach both AI ethics and hands-on skills.
Emphasize transferable cognitive abilities such as critical thinking, creativity, collaboration, communication, cultural awareness and ethical reasoning. These skills allow students to adapt as occupations change.
Encourage an experimental mindset by allowing students to safely test AI tools for content creation, analysis, productivity, etc. Develop policies guiding ethical AI use.
Provide credentials and certifications aligned with in-demand technical abilities like data science, machine learning operations, AI ethics, etc.
Offer career counseling for students on how AI could impact various fields of interest and help shape academic plans accordingly.
Train educators on effectively leveraging AI technologies in instruction, assessment and administration to augment, not replace, their expertise.
Incorporate training on mitigating AI biases, fostering responsible use, maintaining accountability, and other aspects of AI ethics.
With these changes, students will gain skills to be adaptable for the future and become collaborative workers who can use AI responsibly to enhance innovation and productivity.
While educational institutions create programs for future generations, everyone should develop basic AI literacy and look for opportunities to use these tools to enhance productivity and creativity.
For many roles, AI offers the potential to accelerate critical workflows:
Writers can utilize language models to generate drafts and summarize reference materials rapidly.
Analysts and consultants can leverage AI to process data, bring out insights and compile presentations.
Scientists can speed up literature reviews, hypothesis generation and data visualization.
Marketers can optimize campaigns and creative collateral with AI-generated messaging and designs.
Sales professionals can qualify leads faster with AI-powered data analysis and outreach.
Customer service reps can automate information searches and focus their live support on complex issues.
Educators can generate personalized content and then validate alignment with teaching principles.
Health care workers can use AI to speed up scheduling, documentation and administrative tasks.
Engineers, designers and creative professionals can stimulate ideas and rapid prototyping with AI.
The key across all contexts is maintaining human oversight as capabilities advance. Blind delegation or replacement is ineffective, while hands-on learning enables mastery.
Beyond adopting AI in one's current role, individuals should do the following:
Pursue supplemental training and certifications in data science, analytics, digital marketing, UX design and other in-demand skills.
Gain hands-on experience with AI systems to understand their strengths and limitations.
Maintain an external professional network and explore alternative career pathways aligning to personal strengths.
Discuss desired training and career development in the AI age with managers.
With lifelong learning, individuals can take charge of their trajectory and remain irreplaceable as AI matures.
Organizations must also invest heavily in upskilling programs for those already in the workforce. Employees across fields will need new technical capabilities to increase their AI IQ to work collaboratively with AI. Companies should provide ample training opportunities and incentives to train staff on relevant emerging skills continuously. Hiring approaches should value adaptability and learning aptitude over specialized expertise with a shorter shelf life. Policies should support internal mobility and redeployment as roles evolve.
Here are some best practices for organizations managing AI-driven workforce transitions:
Conduct skills audits of existing roles and develop employee reskilling plans over the next five to 10 years.
Budget for robust training programs on AI technologies applicable to the industry and business (for example, machine learning, NLP, computer vision, robotic process automation, among others).
Incentivize continuous learning and development through tuition reimbursement programs and time allowances.
Hire for adaptability, critical thinking and learning aptitude over specialized skills alone. Look beyond traditional credentials.
Develop programs to redeploy employees into new roles as their current jobs are augmented or replaced by AI.
Communicate frequently and transparently about how AI will impact skills demands in the organization. Provide ample lead time and support resources for transitioning staff.
Lobby governments to expand educational access and financial incentives for adult learning programs.
Governments should fund technology-focused vocational programs, subsidize professional certifications in impacted fields, and offer tax credits for approved reskilling initiatives. Regional economic planning can help identify training gaps.
Governments have a significant role in building society’s collective AI IQ as the workforce transitions to an AI economy through policy and funding. Here are some of the critical responsibilities of regional, federal and local governments:
Expand vocational programs focusing on training for digital skills such as data science, machine learning and others that will be in high demand.
Subsidize professional certifications and credentials in fields expected to be transformed by AI automation.
Provide tax credits to companies that invest substantially in reskilling and upskilling their workforce.
Increase funding for community colleges, trade schools and continuing education programs providing technology-focused adult learning.
Policymakers should incentivize organizations to retain and retrain existing workers. Incentivizing retention can occur by implementing subsidies, tax breaks and preferred bidding for government contracts granted to companies demonstrating workforce development and reskilling initiatives.
Raise public awareness of continuous learning opportunities and promote a culture of lifelong learning.
Develop regional economic plans to identify reskilling gaps specific to local labor forces and direct public funds accordingly.
Governments can smooth workforce transitions to an AI-powered economy with supportive policies and funding schemes.
The rapid pace of AI progress brings immense opportunities but also some risks if appropriately managed. However, with ample foresight, investment and training, we can create an inclusive future where AI augments human capabilities for the betterment of all.
This comprehensive approach focuses on transforming our institutions to keep pace with accelerating technology. Collaboration among sectors will be crucial. We must start now because the future is closer than it appears. With preparation, those organizations and individuals who build their AI IQ and embrace AI proactively will be positioned to maximize its benefits.
The recommendations here are starting points. Continued experimentation, dialogue and innovation are essential to meet the challenges ahead. But the tremendous potential makes it a goal worth striving for. The AI revolution is here – and with wisdom, we can shape it for the common good.
Read more about:ChatGPT / Generative AI
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