Implications of AI Growth for Businesses

The biggest AI impacts won’t be borne out in a calculus of jobs but rather in seismic shifts in the level of expertise required to do them. In our article in Harvard Business Review, Joseph Fuller, Michael Fenlon, and I explore how AI will bend learning curves and change job requirements as a result. It’s a simple concept with profound implications. In some jobs, it doesn’t take long to get up to speed. But in a wide array of jobs, from sales to software engineering, significant gaps exist between what a newbie and an experienced incumbent know. In many jobs with steep learning curves, our analysis indicates that entry-level skills are more exposed to GenAI automation than those of higher-level roles. In these roles, representing 1 in 8 jobs, entry-level opportunity could evaporate. Conversely, about 19% of workers are in fields where GenAI is likely to take on tasks that demand technical knowledge today, thereby opening up more opportunities to those without hard skills.   Our analysis suggests that, in the next few years, the better part of 50 million jobs will be affected one way or the other. The extent of those changes will compel companies to reshape their organizational structures and rethink their talent-management strategies in profound ways. The implications will be far reaching, not only for industries but also for individuals and society. Firms that respond adroitly will be best positioned to harness GenAI’s productivity-boosting potential while mitigating the risk posed by talent shortages.   I hope you will take the time to explore this latest collaboration between the The Burning Glass Institute and the Harvard Business School Project on Managing the Future of Work. I am grateful to BGI colleagues Benjamin Francis, Erik Leiden, Nik Dawson, Harin Contractor, Gad Levanon, and Gwynn Guilford for their work on this project. https://lnkd.in/ekattaQA #ai #artificialintelligence #humanresources #careers #management #futureofwork

Traditional ML completely transformed media and advertising in the last decade; the broad applicability of generative AI will bring about even greater change at a faster pace to every industry and type of work. Here are 7 takeaways from my CNBC AI panel at Davos earlier this year with Emma Crosby, Vladimir Lukic, and Rishi Khosla: • For AI efforts to succeed, it needs to be a CEO/board priority. Leaders need to gain firsthand experience using AI and focus on high-impact use cases that solve real business pain points and opportunities. • The hardest and most important aspect of successful AI deployments is enlisting and upskilling employees. To get buy-in, crowdsource or co-create use cases with frontline employees to address their burning pain points, amplify success stories from peers, and provide employees with a way to learn and experiment with AI securely. • We expect 2024 to be a big year for AI regulation and governance frameworks to emerge globally. Productive dialogue is happening between leaders in business, government, and academia which has resulted in meaningful legislation including the EU AI Act and White House Executive Order on AI. • In the next 12 months, we expect to see enterprise adoption take off and real business impact from AI projects, though the truly transformative effects are likely still 5+ years away. This will be a year of learning what works and defining constraints. • The pace of change is unprecedented. To adapt, software development cycles at companies like Salesforce have accelerated from our traditional three product releases a year to now our AI engineering team shipping every 2-3 weeks. • The major risks of AI include data privacy, data security, bias in training data, concentration of power among a few big tech players, and business model disruption. • To mitigate risks, companies are taking steps like establishing responsible AI teams, building domain-specific models with trusted data lineage, and putting in place enterprise governance spanning technology, acceptable use policies, and employee training. While we are excited about AI's potential, much thoughtful work ahead remains to deploy it responsibly in ways that benefit workers, businesses, and all of society. An empowered workforce and smart regulation will be key enablers. Full recording: https://lnkd.in/g2iT9J6j

In recent conversation with IT leaders across a range of industries, the topic of business model transformation has come up more than ever due to AI and AI Agents. Most companies are rapidly thinking through what the impact of their business will be in an AI-First world. Not all of the impact will be the same, and it’s clear that industries will evolve in different ways, including how each of the players in these industries adapt with AI. There are a variety of factories to consider, like whether your business model historically sold services by the hour vs. by outcome, how information-centric your product is, the level of critical thinking required to deliver your service, and more. For instance, if you’re a law firm today, AI Agents have the potential of compressing the hours needed for particular legal work. The industry often bills hourly, so fewer hours certainly can put more risk on revenue per account. However, firms are starting to think through multiple ways AI begins to drive growth or benefits firms. You can now expand with more customers because you can deliver more work at a lower rate, or you could deliver even better work in less time, which ironically could mean fees go up even over time. You can extend out this type of dynamic to a variety of other professional services firms, from marketing agencies to systems integrators. Or, take financial services, where large organizations like financial advisory firms are thinking through what AI Agents do to their business model. In this industry, client relationships and value add is the biggest imperative. Even as AI may lower the barrier to getting financial advice for anyone, AI equally provides the potential for even smarter investment decisions and closer customer relationships between the advisor and the client, which leads to greater stickiness. Ultimately, there isn’t a single industry that won’t be impacted in some small or large way due to AI. Some companies will use AI to win more customers, and others will be forced to compete with new AI entrants which deliver services at a lower cost. Not every firm will adapt to this new reality, however, and those will be at the greatest risk. One big implication to all of this transformation is it puts the technology department more in charge of determining the long term business model and execution of a company ever before. The right moves and partnership right now by those implementing AI in their companies are in a critical position to execute on this.

The hype (AI replaces people) and doom (AI doesn’t work) are both wrong. AI works, but few businesses know how to get it working. AI improves productivity by over 30% in some areas, but doesn’t replace people. If your business isn’t growing, AI won’t do much for your business. AI can also be the engine that restarts growth, but few businesses see the need for both sides (AI as growth and productivity engines) to be successful. If your business isn’t turning data into information, AI won’t do much either. AI is only reliable enough to power growth and productivity with the support of information architecture and knowledge management systems. AI requires customer/product process reengineering for growth and internal process reengineering for productivity gains. Higher productivity allows the business to scale and support new growth without hiring. AI productivity initiatives must align with AI growth initiatives. Think of AI products and the business processes that support them as living on two sides of the business’s technology model. Use AI to reduce the cost of scaling and the time it takes to do it. For example, AI helps businesses spend more on ads by spending less on ad agencies. However, without new products and features to advertise, that doesn’t lead to significant cost savings or revenue growth. AI helps software and AI engineers be more productive, but if there aren’t more customer-facing features and products in the backlog, higher productivity won’t cause significant cost savings or revenue growth. Businesses that try to take productivity gains by laying people off quickly realize that without people, AI-driven operations collapse. Businesses that try to scale AI products quickly realize that it’s impossible to keep up with their growth rate without AI-driven operations.

To all Executives looking to build AI systems responsibly, Yoshua Bengio and a team of 100+ of AI Advisory Experts from more than 30 countries recently published the International AI Safety Report 2025, consisting of ~300 pages of insights. Below is a TLDR (with the help of AI) of the content of the document that you should pay attention to, including risks and mitigation strategies, as you continuously deploy new AI-powered experiences for your customers. 🔸AI Capabilities Are Advancing Rapidly: • AI is improving at an unprecedented pace, especially in programming, scientific reasoning, and automation • AI agents that can act autonomously with little human oversight are in development • Expect continuous breakthroughs, but also new risks as AI becomes more powerful 🔸Key Risks for Businesses and Society: • Malicious Use: AI is being used for deepfake scams, cybersecurity attacks, and disinformation campaigns • Bias & Unreliability: AI models still hallucinate, reinforce biases, and make incorrect recommendations, which could damage trust and credibility • Systemic Risks: AI will most likely impact labor markets while creating new job categories, but will increase privacy violations, and escalate environmental concerns • Loss of Control: Some experts worry that AI systems may become difficult to control, though opinions differ on how soon this could happen 🔸Risk Management & Mitigation Strategies: • Regulatory Uncertainty: AI laws and policies are not yet standardized, making compliance challenging • Transparency Issues: Many companies keep AI details secret, making it hard to assess risks • Defensive AI Measures: Companies must implement robust monitoring, safety protocols, and legal safeguards • AI Literacy Matters: Executives should ensure that teams understand AI risks and governance best practices 🔸Business Implications: • AI Deployment Requires Caution. Companies must weigh efficiency gains against potential legal, ethical, and reputational risks • AI Policy is Evolving. Companies must stay ahead of regulatory changes to avoid compliance headaches • Invest in AI Safety. Companies leading in ethical AI use will have a competitive advantage • AI Can Enhance Security. AI can also help detect fraud, prevent cyber threats, and improve decision-making when used responsibly 🔸The Bottom Line • AI’s potential is massive, but poor implementation can lead to serious risks • Companies must proactively manage AI risks, monitor developments, and engage in AI governance discussions • AI will not “just happen.” Human decisions will shape its impact. Download the report below, and share your thoughts on the future of AI safety! Thanks to all the researchers around the world who took created this report and took the time to not only surface the risks, but provided actionable recommendations on how to address them. #genai #technology #artificialintelligence