Enterprise ‘Digital Transformation’ Initiatives Are Severely Lacking in Ambition According to Innovative New Research From Transforma Insights
Implementations of new technologies including Artificial Intelligence and Internet of Things continue to focus on simple projects that deliver quick returns based on streamlining internal processes
Transforma Insights published its latest Case Study Insight Report entitled ‘Digital Transformation Implementation Best Practice’ which uses analysis of over 600 real-world Digital Transformation (DX) deployments to provide enterprises with best practice on how they should be thinking about using new technologies.
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The key findings are:
![Digital Transformation project deployment time, payback time and projected lifetime [Source: Transforma Insights, 2020] (PRNewsfoto/Transforma Insights)](https://mma.prnewswire.com/media/1155620/dx_deployment_lifetime.jpg?p=publish&w=950 "Digital Transformation project deployment time, payback time and projected lifetime [Source: Transforma Insights, 2020] (PRNewsfoto/Transforma Insights)")
Most organisations are hesitant to extend beyond relatively safe deployments focused solely or predominantly on simple efficiency savings. Over 70% of projects can be considered to have a significant impact on process efficiency. In contrast less than 30% have significant impact on the organisation’s products and services, and barely 10% are potentially disruptive to the industry as a whole.
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Further supporting the idea that enterprises are playing it safe, only 24% of projects can be categorised as ‘mission critical’, although that figure rises to 68% for Robotic Process Automation (RPA), which is generally the most mature and well understood of the technologies.
Given the focus on easy wins, it is unsurprising that the emphasis is generally on deployments that are quick to deploy and pay back. On average projects take 12 months, with payback in 20 months, although there are some outliers. RPA is substantially slower to deploy, reflecting the fact that its is being used more comprehensively, and PLM slower to generate a payback, as illustrated in the chart below.
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