Lately, I've been delving into the transformations occurring in expert professions, primarily driven by technological advancements and shifts in working culture. Instead of consuming more articles about how automation, data, and AI are poised to revolutionize our work and potentially render it obsolete, I've sought to understand what lies ahead and how we can actively steer this change.
Three significant points have caught my attention as I navigate through this transformation:
- Technology currently furnishes us with an unprecedented wealth of valuable data that we can and should utilize to continually enhance our operations. As humans, we ought to prioritize data analysis and delegate data collection to technology.
- Increasingly, human work will revolve around problem-solving and idea generation. Optimal execution of such tasks often occurs not in isolation but within small teams united by a common objective.
- Consequently, we must embrace novel collaborative methods. In Accounts Payable (AP), this might entail greater teamwork aimed at refining processes and enhancing service delivery to business units.
It's evident that automation represents merely the initial phase in our endeavor to modernize processes and operations, either to stay competitive or gain an edge in our respective markets. By freeing up time and resources, automation allows us to seek out and implement superior approaches. In a recent video interview, Michelle Wagner from our client SA Recycling talked about how they leverage data gleaned through AP automation to enhance their practices.
This morning, I attended a webinar on the application of generative AI. I discovered that its paradigm shift lies in providing technology that aids individuals in achieving success in their work. This is evident in various customer service processes, wherein generative AI assists employees by facilitating access to pertinent data and offering proactive suggestions for service improvement. Another avenue for employing generative AI is enhancing processes through service design, with AP being a prime candidate for such an application.
Many companies are rightfully concerned about AI's security and other potential drawbacks. The webinar noted, however, that the most talented workforce already avoids companies that lag behind in AI adoption. Secondly, a good first step is to ensure that any technology integrated into your company incorporates AI capabilities.
As technology liberates time and streamlines access to data, we are presented with immense opportunities. However, seizing these opportunities necessitates an active transformation of our daily routines to incorporate more collaborative learning. Learning, in this context, equates to continuous improvement. Experimentation emerges as an excellent method for leveraging data, fostering teamwork, and achieving tangible results—a practice increasingly embraced by forward-thinking organizations not only in product development but also in any task related to improvement.
As technology advances and reshapes our work methodologies, we must adapt to new capabilities. For example, the pandemic thrust us into remote work, which isn't conducive to collaboration and shared learning. Striking a balance between remote and in-person work is imperative. To maximize remote work efficiency, we must familiarize ourselves with collaborative tools and, crucially, show our faces in those online meetings! Online meetings lack many of the positive aspects of face-to-face interaction. Many of us leave things unsaid in online meetings as the discussion does not flow as naturally as it would face-to-face.
Analyzing data to derive insights requires thoughtful contemplation, whether individually or as a team. We must redefine our perceptions of efficient work. In the future, if you find a colleague gazing into the distance, pondering, refrain from assuming laziness! A significant aspect of our future work will involve asking questions: What problem are we collectively striving to solve? Once we know, how can data and AI assist us to solve it?
A key question in this ongoing transformation is identifying constraints rendered obsolete by AI. These constraints may have become so ingrained that we struggle to envision alternatives. Yet, failure to adapt may result in missed opportunities, as others are quick to capitalize on AI's potential.
