Why Does Unlearning Matter for Business Continuity and Agility?
As companies adopt AI systems, the workforce will need new tech skills. But smooth adoption is not just about skills, technical ones. Letting go of out-of-date mindsets is key to making AI work well. This “unlearning” of the past is crucial.
What is this unlearning? Unlearning means challenging ways of thinking that are no longer relevant and upgrading knowledge so they don’t block progress. Three key mindset shifts needed are around mental models, old beliefs, and generational divides.
Mental models cover how employees see things working. If their familiar models stay in the past, AI struggles for traction. Workers must also reconsider views like “AI threatens jobs” or “AI can’t match humans.” Balancing expectations helps work smoothly with AI.
Long-held beliefs and assumptions about technology also need rethinking as AI enters. AI is there to enhance unique talents and augment existing competencies. Modernised outlooks set companies up for joint human and AI successful collaboration.
There are also generational splits on overhyping or downplaying AI. Younger staff may overshoot its near-term impact. More experienced people can underestimate innovation. Leaders must support cross-generational talks to build balanced reality checks on AI’s evolving abilities and limits.
Why are leadership shifts as vital as skill growth? Reskilling alone has workforce gaps in adapting to AI long term without the fluid rethinking of biases that block absorbing new skills. With guidance to question old assumptions, people stay primed to keep developing alongside AI. This flexibility means less friction in adjusting operations.
Unpacking outdated assumptions around how work functions, technology’s role, and what people can accomplish with AI and understanding what knowledge is obsolete become urgent to reskill smoothly. With the weight of the past lifted, companies may innovate workflows to match AI’s future.
Why does Unlearning allow Company and Strategy Agility?
Unlearning gives companies speed and flexibility to keep pace with AI’s rapid enhancements that require workflow shifts.
Otherwise, organisations end up confined to the status quo without actively challenging rigid thinking. People risk being forced to use outdated work models on AI tools rather than reinvent processes to sync abilities. Progress gets blocked when companies try to put AI changes on top of inflexible old operating methods. This risk is not different from the one warned time ago for Digital Transformation.
On the other hand, practices making unlearning continuous allow workflows to smoothly adapt. Employees are receptive to rethinking operations and their roles with an open mentality as AI expands. Rather than just mechanically bolting AI to current structures, the business questions how to best remodel human-AI collaboration.
Making unlearning integral can shape an agile business oriented to realign as novel AI functionalities emerge. Companies that foster unlearning cultivate the constant learning essential to stick to intelligent applications’ swift evolution. Rather than long lag times around major upgrades, once AI has transformed capabilities, continual small-scale role and responsibility shifts happen across departments.
Workforce adaptivity remains strong through light and frequent exercise in questioning old models. In the long-term, dedicating resources to bake in company-wide unlearning delivers lasting organisational responsiveness and continuity amidst the technology landscape where the only constant is change.
The Path Forward?
Rather than solely chasing AI efficiency and productivity as proficiencies, leaders prioritise nurturing unlearning. Getting past old ways of thinking, outdated knowledge, false beliefs, and generational divides is key to taking in new AI training and using AI tools smoothly.
When ongoing questioning removes thinking barriers, companies can flexibly shift jobs to match AI as it keeps advancing.
Workforces who make unlearning a regular practice to stay current avoid major disruptions as AI becomes fixed into company operations.
The aim is to build a mindset across the organization that’s ready to change and align workflows to work with AI capabilities over time.
Leadership Guide: How to Lead Continuous Learning with AI
• Reinforce the mindset that productive AI implementation relies on people’s agility in upgrading mental models, not just technical skills
• Design and model around evolving workflows and AI’s growing promise.
• Conscious and effective support of unlearning cycles develop workforce readiness to resonate with AI’s ongoing enhancements following upgrades
• Guide incremental bias inspection through regular small group dialogues, raising fresh perspectives.
• Ask thoughtful questions more than proposing fixed new models
Patience and compassion for integration issues beat pressure to adopt innovations overnight.