Digital transformation return.

Digital transformation is large-scale technology adoption that changes how a business operates: moving to cloud, implementing AI/ML, automating workflows, adopting modern CRM/ERP. Typical mid-sized project costs 500k-5M and promises productivity gains, new revenue streams, and cost savings. Real programmes deliver 15-40% of claimed benefits in practice.

2M total cost, 300k productivity gains + 200k new revenue + 150k cost savings = 650k annual benefit. 5-year horizon: 3.25M total benefit, 1.25M net value, 63% ROI, 3.1-year payback. Reasonable for well-executed programme.

Digital transformations fail frequently. McKinsey research: ~70% of projects don't achieve stated goals. Main failure patterns: boiling-the-ocean scope (too many initiatives at once), lack of executive sponsorship (middle-management push-back kills momentum), under-investment in change management (tools fail on adoption not capability), trying to transform without improving underlying processes first.

Quick example

With transformation total cost of 2,000,000 and productivity gain annual of 300,000 (plus new revenue annual of 200,000 and cost savings annual of 150,000), the result is 62.50%. Change any figure and watch the output shift — it's often more useful to see the pattern than to memorise the formula.

Which inputs matter most

You enter Transformation Total Cost, Productivity Gain Annual, New Revenue Annual, Cost Savings Annual, and Horizon (years). Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

What's happening under the hood

Total benefits = (productivity + revenue + savings) × years. ROI = (benefits - cost) ÷ cost × 100. Payback = cost ÷ annual benefit. The formula is listed in full below. If the number looks off, you can retrace the calculation by hand — that's the point of showing the working.

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.