3 lessons for navigating AI transformation

AI is substantially disrupting and transforming the way people work. To guide the next technological wave, draw from what was learned during the rise of cloud computing.

3 lessons for navigating AI transformation

4 min read
  • Previous iterations of technology were once questioned — like cloud computing — but are now widely accepted and used.
  • AI will likely follow suit, with early adopters gaining an advantage and skeptics playing catch up.
  • Three essential lessons from cloud computing’s adoption can be applied to AI. Leaders should take time to look for opportunities to integrate AI into everyday operations to increase performance and drive innovation.

Generative AI has advanced at an astonishing rate. Since ChatGPT launched in late 2022, new iterations of GenAI have been released several times a month. 

The pace of technological change has major impacts on organizations and the workforce. According to PwC’s 27th Annual Global CEO Survey, 45 percent of CEOs said that their companies would not be viable within the next decade if they continue on the same path. Automation is set to change a third of global jobs over the next 15 to 20 years. 

AI and the pace of change have forced leaders in every industry to answer the following question: How can your employees benefit from new tech, like GenAI, and prepare workforces to adapt as quickly as technology evolves?

Luckily, there are lessons to be drawn from the disruption and transformation cloud computing created in the 2000s.

Related content: See how AI-native talent intelligence has the potential to change the way we work.

1. Don’t worry about the initial hesitation.

Cloud computing enabled faster time to market, scalability, collaboration, data loss protection, and more. Yet even with these potential benefits, many organizations were hesitant to move data and operations to the cloud due to security concerns, legacy investments in on-premise infrastructure, and uncertainty around reliability and uptime.

Eventually, competitive pressures and tangible values shown by those who made the jump to the cloud overcame that skepticism. A mindset shift had to happen before people could move from on-premise to cloud technology. What can be learned from this when considering AI? 

The same initial hesitation is present. Leaders are concerned about ethical issues, accessibility to data, lost jobs, the possibility of human obsolescence, and more. 

With AI, the big disruption is that improvement as a linear process is coming to an end. The technology allows for sustainable transformation where complexity is simplified. Every process that emerges can be captured and examined for future improvement. 

Organizational leaders can reevaluate regularly, in real-time, so continuous improvement becomes organic. Yet another widespread mindset shift has to take place to realize the exponential way AI creates the opportunity to improve and become more effective. 

2. Reskilling and upskilling are required to take advantage of new technology capabilities.

Cloud computing introduced new disciplines like cloud architecture, DevOps, and site-reliability engineering that required extensive retraining. IT staff were upskilled through certifications, and cloud specialists were engaged to manage migrations.

With AI, mountains of data can be shifted through more efficiently. AI prompt engineering and model training/deployment reskilling programs are becoming essential.

This shift also means that companies must understand what skills their workforces have and what skills will be needed for the future. This will require a shift from focusing on jobs to skills

What differs from the past, is that AI isn’t going to replace the work that people do — it’s going to augment the work people do. People and AI will need to work together to learn from and improve each other. 

This new way of working essentially treats AI more like a coworker than a static technology. 

3. Some legacy roles will become obsolete, but new jobs will emerge. 

New technology can sometimes mean that the workforce is treated like a bottom-line cost that machines can reduce. While it’s true that some jobs will become obsolete, emerging tech also creates jobs that didn’t exist before. 

With cloud computing, the need for maintenance, upgrades, and procuring hardware was drastically reduced. Roles like data center operator and system administrator for on-premise infrastructure diminished. However, new roles like cloud operations, cloud security, cloud developers/engineers, solutions architects, and managed services providers have emerged.

Roles with repetitive tasks are declining due to AI, but new jobs like machine-learning engineers, data scientists, and MLOps are emerging. Other roles will likely be created including AI product managers, AI ethics managers, and conversational AI developers. 

It’s important to remember that organizational leaders have the responsibility of deploying AI to harness the skills and capabilities of their workforces. It will fall to them to look toward innovation and new ways of working to set their organizations apart from the competition. Their employees’ creativity will be one of the most important assets in this new AI-powered age. 

Like cloud computing instigated widespread change, AI has the potential to be the “printing press” moment of the current times, reinventing how new skills, roles, processes, and business models are promoted as it becomes embedded across industries. ​​

This article first appeared in Inc. magazine.

Sania Khan is the former Chief Economist at Eightfold.

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