“If I walk 30 paces this way, I have a good understanding of where I am going to end up. But let’s say it’s not 30 steps linearly, but 30 steps exponentially. Where do I land? That would land me on the moon. That is how hard it is to think exponentially.”
Ian Beacraft made this point at the beginning of his talk “The Future of an AI Powered Workforce” at SXSW 2024 – understanding the pace of change and its implications in the coming years will be very hard. Beacraft, who is the CEO of AI consultancy Signal and Cipher, states that “we’re not really built to see things exponentially. We are linear human beings.”
Historian Yuval Noah Harari has echoed this concern, stating in his book, 21 Lessons for the 21st Century, that advanced technologies, particularly artificial intelligence, health tech, virtual reality among others, will disrupt the ways we understand and identify ourselves. “For as the pace of change increases, not just the economy but the very meaning of ‘being human’ is likely to mutate.”
He goes on to describe a world of such rapid fluidity that requires a constant re-examination of who one is, what one does, and where.
So at twenty-five you might introduce yourself on a dating site as “a twenty-five-year-old heterosexual woman who lives in London and works in a fashion shop.” At thirty-five you might say you are “a gender-nonspecific person undergoing age adjustment, whose neocortical activity takes place mainly in the NewCosmos virtual world, and whose life mission is to go where no fashion designer has gone before.” At forty-five both dating and self-definitions are so passé. You just wait for an algorithm to find (or create) the perfect match for you.
Harari has a hard question and answer for folks: “How do you live in a world where profound uncertainty is not a bug but a feature? To survive and flourish in such a world, you will need a lot of mental flexibility and great reserves of emotional balance. You will have to repeatedly let go of some of what you know best, and learn to feel at home with the unknown.”
Martec’s Law
Citing Martec’s Law, Beacraft explains that like people, the pace of change for organizations is much slower than the pace of change of technology. That gap, unfortunately, creates disruption, tension, and sometimes crisis.
This makes sense. Harari states that individuals enjoy change in their teenage years as their bodies undergo dramatic change, but then seek greater stability in their middle age. In a similar way, organizations become more structured and process-oriented, more geographically complex, more integrated into markets, more regulated, more risk averse, all resulting in greater resistance to change.
For example, leaders of large organizations know that silo mentality is thwarting inter-departmental collaboration and overall performance, but they are hard-pressed to overthrow the way organizations are structured in order to cater to critical processes that cut through, or should cut through, silos.
The AI tools available today offer established organizations opportunities to increase cross-functional collaboration for greater innovation and productivity. AI should be able to optimize and automate a multitude of tasks across an organization. Internal communications, for example, can be fairly quickly consolidated and customized into a coherent process using a large language model chatbot that makes your emails or Slack posts better, multilingual and consistent in style and messaging.
But I suspect that most AI applications these days focus on the accountabilities of individual functions: chatbots for customer service, fraud detection for risk management, copy writing for marketing. In actuality, AI can do much more, assuming organizational leadership and culture allow.
AI-Ready Organizations
When COVID sent everyone home, leaders saw a need to leverage a more robust, silo-busting virtual community. Our mindset of meetings as a factor of place and time was dispelled by the online world, as we relieved ourselves of the painful step of finding a meeting room available at the same time people were.
When the world became virtual, we realized that face-to-face interactions were not essential to getting things done, and that people would collaborate across geographical boundaries and time zones. We also realized that skills were a commodity to be traded, globally. When I worked at MetLife during the pandemic, HR contracted with a company called Degreed to offer a virtual talent marketplace, matching skills of individual employees to short-term projects established by managers. A tax accountant in Bolivia, who has phenomenal skills in Excel, could participate in a data analytics project in Korea, organized by an executive in HK.
As Beacraft explains, truly AI-ready organizations are embracing that silo-busting mentality, which could lead to significant organizational re-design in the coming years. “(COVID’s) no longer just a special case. That’s every day with AI. Focusing on work that is based on projects and not roles, on skills and not jobs.”
Here’s a summary of Beacraft’s views on how organizations will change in the coming years:
- Agent-Driven Automation: In Beacraft’s envisioning of future organizations, being “agent driven” underscores a significant shift towards integrating AI agents as key components of the operational structure. In this model, AI agents are not just tools but primary actors within the organization, handling a range of tasks from data analysis to management of routine administrative functions. This automation, in theory, frees up employees to focus on more strategic, creative, and interpersonal activities. Such a setup could lead to enhancement of organizational efficiency and agility, creating opportunities to scale and adapt in this rapidly changing and chaotic business environment.
- Focus on Skills Over Roles: In traditional job-centric models, employees are typically hired for specific roles or positions within a department, with clear and often rigid job descriptions. Beacraft believes we are entering an era where organizations will focus on the diverse skills employees bring to the table rather than the specific job they are hired to perform. The hunt for skills will extend to external consultants who have highly honed technical skills. Effective execution of this model could result in a more flexible and dynamic use of human resources where individuals contribute across various projects and teams based on their skills and competencies, not their job titles.
- Project-based Work: Beacraft suggests moving away from traditional departmental structures to more project-based work. This change allows organizations to assemble teams based on the specific skills needed for each project, rather than being constrained by departmental boundaries. This method not only optimizes the use of skills within the organization but also aligns with the way AI and automation are reshaping tasks and workflows, making them more project-oriented and less routine.
- Adaptability and Continuous Learning: A commitment to a skill-centric organization means a commitment to continuous learning and adaptability among its workforce. By focusing on skills, organizations encourage employees to develop a broad set of capabilities that can be applied flexibly in various contexts. This is particularly important in an AI-driven world, where continuous learning is the only way to keep up in the face of rapid technological change.
If the above assumptions are correct, new jobs will be created. But many more jobs will disappear. That’s serious. That’s real. But, to a certain degree, that’s only wordplay. As Beacraft stated, “jobs are dead. But work isn’t.”
Whether organizations like it or not, we are on the cusp of monumental shifts in the way we work, with major implications for organizational culture, resource allocation, and organizational design.
So leaders, get to work. Get your companies AI Ready.
ARTICLE FAQS
1. Why is exponential change so difficult for organizations to handle?
Humans and organizations naturally think in linear terms, while technologies like AI advance exponentially. This creates a widening gap—captured by Martec’s Law—where technology outpaces organizational ability to adapt, leading to disruption and crises if not addressed.
2. What does it mean for a company to be “AI Ready”?
An AI-ready company embraces flexibility, cross-functional collaboration, and skill-based work rather than rigid job roles. It integrates AI agents into workflows, supports continuous learning, and encourages project-based work that adapts quickly to new challenges.
3. How is AI changing the way organizations are structured?
AI allows work to shift from siloed departments to project-based teams formed around skills and capabilities. It also enables automation of routine tasks through AI agents, freeing people to focus on strategic, creative, and interpersonal contributions.
4. Why focus on skills instead of jobs in an AI-driven workplace?
Jobs with fixed descriptions are becoming less relevant as tasks evolve quickly with new technologies. By prioritizing skills, organizations can flexibly redeploy talent across projects, make better use of specialized expertise, and keep pace with constant change.
5. What role does continuous learning play in preparing for AI?
Continuous learning ensures employees can expand and refresh their skills as technology evolves. In an AI-first world, adaptability and willingness to learn are more valuable than static expertise, making ongoing development a central pillar of AI readiness.
6. Will AI create or eliminate jobs?
Both. Many traditional jobs will disappear as automation scales, but new forms of work will emerge, especially project- and skill-based roles. As Ian Beacraft suggests, “jobs are dead, but work isn’t”—the concept of work will shift, not vanish.
7. What should leaders prioritize now to get their companies AI ready?
Leaders should break down silos, invest in AI literacy and skill development, pilot agent-driven automation, and redesign organizational structures around flexibility and adaptability. The goal is not only efficiency but resilience in the face of exponential change.
All very true and I completely agree that while there is so much excitement on what AI can do for us (more than anytime before), there is the same level of uncertainty. How leaders navigate in this level of uncertainty and also not to fall in the loop of rat race is really important. While I agree on how the organizations change in coming years, I am not sure if that has to do with the advent of AI. Organizations should be operating with automation, project management, skills and roles etc and that us not new.
I think what is new is –
1. Future state and use cases are everywhere. Everybody is talking about how the world will change. It is important to know “how” to get there” I always say it is not the what but how that is important.
2. Bite size implementation or end to end implementation. This is a question I ask myself everyday. While I have a good image of where we are going my theory is to test solutions in parts of processes that have been re-engineered knowing that these will eventually be stitched together.
3. Uncertainty is when humans use their brains the most so I really think leaders need to work in uncertain environment.