Digital disruption fueled by artificial intelligence differs fundamentally from prior eras of tech-driven change in speed, scope of impact, and the level of uncertainty it provokes. There are six dimensions pushing the boundaries of traditional change management frameworks.
Innovation Velocity
The frequency of groundbreaking new artificial intelligence capabilities has accelerated to the point of defying even insiders’ ability to forecast impacts. This is aided by AI’s capability to self-accelerate (artificial intelligence using artificial intelligence to advance itself). With the paces of changes in the industry regularly surprising even the handful of companies that have the largest and most advanced AI teams in the world, it has already outpaced even the most savvy of us.
Uptake Velocity
The speed of user adoption of artificial intelligence has outpaced every other tech cycle. Sophisticated AI is now in a click’s reach of everyone with an open internet connection, with usage spreading virally and further accelerating innovation and use case discovery.
Unsanctioned or Unsupported Adoption
While organizations are under great pressure to leverage artificial intelligence through planned, centralized adoption, it’s flowing more rapidly into the organization through other channels. People, whether sanctioned or not, are using the tools for their work. This kind of “silent adoption” carries risks, but attempting to "outlaw" AI technology carries the opportunity cost of not only missing out on building vital AI capabilities, but also potentially forgoing discoveries that could be unlocked by those closest to workflows and customers.
Undertone of Fear
Emotions around artificial intelligence in the enterprise read like a tale of two emotions. On one hand, there is excitement about how artificial intelligence can automate repetitive tasks, allowing employees to focus on more meaningful and human work. However, there is also anxiety that as AI capabilities grow more sophisticated, many jobs, departments and even entire industries could face displacement. The typical organization is not well equipped to navigate this magnitude of charged emotion.
Heightened Distrust and Skepticism
AI carries the biases of its training data and has a propensity to generate false information (called hallucinations). It may not be surprising then to hear that studies are revealing that consumers trust a brand less if they know it is using AI. This skepticism is fueled by a lack of transparency surrounding artificial intelligence algorithms, training data, and processes, leaving individuals uncertain about how decisions are made and whether they can trust the outcomes.
Reevaluation of the Human-Machine Relationship
AI forces leaders to grapple with philosophical questions rarely contemplated in business: What capabilities should remain uniquely human? Where do we need or want people to remain in the loop? How do we maintain our humanity while innovating with AI? The answers are growing more complex as artificial intelligence rapidly evolves. While current thinking advocates for human oversight of AI systems, determining the ideal role for people in AI-human partnerships remains a moving target.
New Pressures on Change Management Frameworks
Every one of these dimensions is putting pressure on the traditional digital transformation playbooks that organizations have honed over many years. As pioneers discover how to effectively harness and scale AI, it is expected to produce rapid and dramatic impact on competitive landscapes over a relatively short period of time. For this reason, organizations that seek to be leaders in the next epoch can’t afford to sit on the sidelines waiting for new digital transformation best practices to solidify. Instead, they must actively
engage in research and experiments to build the AI muscle of their organization.
Alison McCauley is an artificial intelligence keynote speaker and executive advisor focusing on the human dimensions of AI.