Digital Experimentation Instead Of Digital Transformation
A recent Gartner report reveals that while 91% of companies embark on digital initiatives, only 40% achieve organization-wide scale [1]. This underscores the eagerness of companies to leverage digital tools and systems, yet it also points to a common shortfall: the widespread adoption of innovative technology remains elusive. Despite advancements in generative AI, a gap persists between the enthusiasm for the technology and its actual implementation. A CNBC article noted that “about 75% of respondents experimented with generative AI in 2023, but only 9% reported widespread adoption.”[2]
The enterprise-wide adoption of digital applications is not progressing rapidly. To accelerate this process, consider adopting an agile approach to technology implementation. Start with a pilot project, spearheaded by a team eager to embrace the innovative technology. Include a skeptic within the team that can offer balance and lend credibility to the pilot.
An effective strategy is to break down the initiative into manageable segments, preventing the organization from becoming overwhelmed. For example, when implementing generative AI, frame the process as a series of trials to determine what is effective. This approach may seem to downplay the potential impact, but it also makes the change more attainable organization-wide. Climbing Mount Everest isn’t achieved in a day; it requires incremental preparation. Similarly, adopting innovative technology, particularly generative AI, should be viewed as a series of steps rather than a monumental shift.
Once the tasks are delineated, prioritize those with the greatest potential impact, keeping them small and within a defined period. The aim is to foster rapid experimentation and build momentum in altering technology workflows. However, avoid rushing to the point of overwhelming the team or overlooking issues. Appropriately sized tasks can mitigate risk, allowing for swift directional adjustments if necessary. The primary advantage of this experimental phase is gaining a clearer understanding of the objectives. Subsequently, these trials can be expanded to other business areas, effecting transformation without explicitly labeling it as such. Transitioning from a transformation-centric to an experimentation-centric mindset offers teams the psychological safety to explore without fear of causing significant downstream issues or facing blame for failures.
Assuming the problem and technological solution are already identified, consider the following steps to initiate the process:
Segment the work into small, manageable tasks.
Timebox the tasks to 2-4 weeks to achieve a concrete result.
Select teams willing to undertake the implementation.
Emphasize the experimental nature of the project, with the potential to lead to transformation.
Aim to discern what was successful, what wasn’t, and the necessary adjustments before proceeding with those changes.
While more comprehensive methods exist, this approach provides a solid starting point for your team. Ultimately, the emphasis should be on experimenting with new digital applications to enhance productivity with business and technology workflows.