How can you mitigate risks during digital transformation and ensure future success?

Discovery is utmost the foundation of how good or bad the transformation would be. There could be many approaches and frameworks leveraged to it like BOD - the 9 box model, 5 Ws , MOSCOW or others but it is important to understand the organisation Vision, Mission and Strategy. Followed by the Sponsor's Empathy map and its Ecosystem map to derive a HOLISTIC SWOT. This will help to make an informed scientific decision of "What" needs to be done. Based on other CXO stakeholders units & partners maturity the "When" - defining the transition state and "How" with "One Team ethos". Fail fast methodology with ToC/PoC and grabbing the lower hanging fruits as initial transition state would imbibe confidence in team and build trust across the board.

DMAIC (Define, Measure, Analyse, Improve and Control) is a proven methodology for data-driven improvements. The tool is part of a Six Sigma continual process improvement cycle; the evidence is statistically based, and solutions can be modelled. DMAIC advocates incremental and measured change and considers risk appetite. Fail fast can be an acceptable method for certain risks as long as they are clearly understood using a consistent methodology. The Six Sigma methodology is based on statistical methods to determine solution success and change impact, applying solution scenario testing in DMAIC where failure fast is not an acceptable method due to risk appetite.

It's never about the job or the person - it's about the process and connected data. Engaged as many stakeholders at the lowest level possible - the folks who really do the work day to day know what's the pain point - they may not have an idea on the solution but can share the details needed to build the right solutions

Scope creep happens all the time and especially with digital transformation. Building a realistic roadmap that includes the industry, market and company headwinds is a must. It's easy to jump to technology as the solution without really looking at the business process and connected data - is there a simple way to solve the same problem.

Use case and proof of concept - as many as you can schedule, resource and fund to build the knowledge network and confidence in the journey. Data can be overwhelming - the average size plant has thousands and thousands of data tags and a high percent without contextualization. When ready - find the transformational use case - delivering the "art of what's possible"

Most failures are not due to budget overrun or scheduling, most PMs are well trained on tracking and monitoring both areas. The actual failure in digital transformation projects is a corporate failure of businesses to recognize that users are inherently resistant to change. Change is not easy, and if a user's awareness of the reason for the change is not sufficient their urgency to adapt to the changes required of them is not high enough to stick long term. In time the users will cease to be motivated to follow through on the changes. This common behavior is why most projects fail, it's a people issue more than a logical failure due to someone not guessing the outcome of a project accurately enough.