The Future of Work: How AI and No-code Are Merging Roles in the Business World

by | process automation, technology

The business landscape is being reshaped by artificial intelligence (AI) and no-code platforms and automation, blurring traditional roles. Nevertheless, the benefits of specialization are unlikely to go away. That means we need to rethink how we approach work, develop new skills, and continue to learn how to collaborate more effectively.

A Shifting Landscape

The landscape of traditional business roles is dramatically changing with the rise of no-code automation and AI. The line separating subject matter experts (SMEs), business analysts (BAs), and developers is blurring as these technologies democratize access to technical capabilities.

  • Subject matter experts: No longer are SMEs merely knowledge bearers. With no-code platforms, they can now directly apply their expertise to build applications, reducing the need for intermediary roles. That is, SMEs are becoming “citizen developers”. Gartner estimates that as of 2023, “the number of active citizen developers at large enterprises [is] at least four times the number of professional developers.”
  • Business analysts: BAs have traditionally bridged the gap between SMEs and developers. They’ll continue to play that role but are evolving into increasingly active drivers of digital innovation. They can implement solutions, analyze complex data sets, and even generate predictive models, thanks to AI and no-code tools.
  • Developers: No-code platforms are automating traditional coding tasks, freeing developers to focus on more complex and creative responsibilities. AI is also stepping in, particularly in automating parts of the software development process, like testing and debugging.

However, this convergence of roles doesn’t spell the end of any particular role but a shift in skills and responsibilities. It also implies a more collaborative work environment, fostering shared understanding and cooperation between SMEs, BAs, and developers.

Not Just for Large Enterprises

The tools, technologies, and methods that have spurred and supported digital transformation at large enterprises are now available to all of us at low cost through software-as-a-service (SaaS) models. In fact, people at small businesses have been more likely to embrace the citizen developer movement than their counterparts at large enterprises. That creates something of a “Red Queen effect.”

  • The accessibility of highly capable no-code tools increases process automation opportunities.
  • The application of process automation creates the potential for competitive advantage.
  • That, in turn, drives the need for process thinking skills, which drives investment in business process management capability.
  • Higher levels of business process capability generate additional process automation opportunities, creating a reinforcing cycle of competitive advantage.

However, the creation of a competitive advantage usually triggers a competitive response. That is, your application of AI and no-code is likely to spur others to do so, as well. That acts as supercharger to the cycle, which contributes to the trend toward hyper-automation.

Still, the World is Not Flat

The journey isn’t without its bumps. There’s a risk of oversimplifying complex tasks that specialists should handle. Not all business problems can be resolved with no-code solutions, and not all data analysis should be entrusted to AI. Understanding the limitations of these technologies is crucial. Moreover, organizations must reskill their workforce to adapt to these changes. The new capabilities provided by AI and no-code tools require new skills and a shift in mindset.

Human language is fundamentally imprecise, not least because it isn’t “tethered” to a specific computational implementation, and its meaning is basically defined just by a “social contract” between its users. But computational language, by its nature, has a certain fundamental precision—because, in the end, what it specifies can always be “unambiguously executed on a computer”. —Stephen Wolfram

Effective SMEs are conversant in natural language but may not be trained to think programmatically. They’ll need to learn to do so. Programmatic thinking involves a few key components:

  • Decomposition: Segmenting a large problem into manageable parts.
  • Abstraction: Focusing on essential features and ignoring unnecessary details.
  • Pattern Recognition: Identifying common themes or sequences within problems.
  • Algorithmic Thinking: Creating clear, step-by-step procedures to solve problems.
  • Debugging: Spotting and fixing errors in logic or code.
  • Iteration: Repeating actions until a condition is met.
  • Recursion: Having a function that uses itself within its own definition.
  • Optimization: Making a solution more efficient in terms of speed, memory, or other resources.
  • Generalization: Adapting a solution to a broader range of problems.

As SMEs are increasingly able to develop automation on their own, highly trained developers will be freed—and required—to focus on more complex applications. More than ever, developers must be able to relate to SMEs and develop a deep understanding of use cases.

BAs, as ever, must be able to apply business process management techniques to help facilitate collaboration among SMEs and developers as they continue to co-create ever more sophisticated and creative information technologies.

The performance bar for everyone is rising. That represents an opportunity for us to engage in more intrinsically and extrinsically rewarding work, but it’s also a significant personal and organizational challenge. Learning and adaptation are rarely effortless.