The unprecedented growth of publicly available AI tools far surpasses workers' top-down training in incorporating AI into their work. This isn’t a critique—it happened too fast to expect us to keep up. Unfortunately, sometimes people craft their work to use these new tools without understanding (or at least abiding by) organizational foundations and controls.

Work crafting is when workers independently (not guided by management) adjust their work to better suit their skills, preferences, and performance. The academic literature typically calls this “job crafting.” Still, I use “work crafting” as that term keeps us focused on the idea that anyone can do this whether or not they are in a traditional job setting.  

A business leader asked me last week what foundational skills their employees need to effectively apply AI from the bottom up. (The organization already had a good handle on top-down applications of AI in its business practices.) 

My answer to him is generalizable to us all.

Foundational Building Blocks

• Good data

• Good security

• Crafting skills

Before effectively leveraging AI to enhance and redefine our work processes through bottom-up work crafting, we must ensure that we have a scaffold of data security built on a foundation of accurate and well-understood data, energized by a culture of experimentation and work crafting.

This holistic approach is not merely preparatory but essential, setting the stage for a future where AI capabilities and human ingenuity converge as we create innovative and resilient work organizations. Many of you may see the opportunity to Think in 5T—and you’re right!

Our Target revolves around effective performance, the security of our organization and clients’ data, and the quality of our work lives

The Times are, as so often noted, unprecedented in the pace and capabilities of AI change

Talent means all of us in this context. We all need to know our options regarding data and integrations we can leverage, the tools we can bring to bear, and the security boundaries we cannot cross. In this case, there is a tight link between Talent and Technology. This interplay between Talent and Technology is critical, as it shapes our approach to leveraging AI and other digital tools.

The Technology node includes our security technologies, “white-listed” AI, and all the other tools we’re considering.

Technique is how we bring our talent and technology together. Performance management that supports the processing and training across security, available data and integrations, work crafting and bottom-up AI applications are effective techniques to the extent that we manage across the 5Ts in concert.

My colleagues and I have a well-known paper entitled, Information Technology and the Changing Fabric of Organization. If I had it to do over, I would argue for something more rigid than “fabric.” Security and data quality are more immutable than fabric suggests.

The good news is that training and resources around security and available data and integrations can be relatively stable. Similarly, the motivation and practices of work crafting are well known. Finding the best tools for the job is where it’s hard to keep up. 

Foundational Practices

Prepare your organization to leverage AI from the bottom-up. Evidence of the benefits (and complexities of implementation) means we all need to pay attention.

Stop-Look-Listen -- both internally and externally. Does everyone know the kind of data available to support their work, how to access it, and what integrations are feasible? Deeper understanding can be beneficial as people understand why data quality is critical in all our work.

Mix -- negotiate changes such that different stakeholders and requirements are involved. My work crafting shouldn’t be detrimental to my teammates’ process. We can benefit by working together as we make our adjustments.

Share -- I’m sharing here, and I bet many of your colleagues also have strategies they’d be happy to share. Develop a routine that helps you stay aware (internally and externally) of practices that work and those that are off the mark. No need to keep making the same mistakes. Similarly, if many of us are hitting the same roadblock, it may be time for some reengineering.