By Darren Tredgold, General Manager at Independent Steel Company
Most digital transformation projects fail. Not because the technology is bad. Because companies treat it as an IT project instead of a business change.
The numbers are brutal. 42% of companies scrapped most of their AI initiatives in 2025, up from 17% in 2024. Only 1 in 4 AI initiatives deliver expected returns. That’s a lot of wasted money and effort.
The problem is not the tools. The problem is the approach.
Bolting On vs. Building In
Here’s where most companies go wrong. They take existing processes and add technology on top. Same workflows. Same bottlenecks. Now with a fancy dashboard.
The companies getting results do it differently. They redesign workflows from scratch. They ask what the process should look like if they were starting today, not how to make the old process slightly faster.
Microsoft reported a 37% productivity boost and 64% users said reduction in email processing time through AI deployment. Palo Alto Networks grew automated IT operations from 12% to 75% in less than two years, cutting IT operations costs in half. Insurance claims processor CED achieved a 93% straight-through processing rate and reallocated 80% of staff to higher-value work.
These companies did not just buy software. They rethought how work gets done.
The Expectation Problem
Companies expect payback in 7-12 months. Typical payback takes 2-4 years. When boards want quick wins and reality delivers slow progress, projects get cancelled before they can prove their value.
High performers set realistic timelines. They commit at least 20% of their digital budgets to transformation. They set both growth and innovation objectives, not just efficiency targets. And they get senior leadership actively involved, not just signing off on budgets.
Organisations with adequate change management investment achieve 2-3x higher ROI. Change management is not a nice-to-have. It’s where the ROI comes from.
Why Legacy Systems Kill Transformation
Legacy system integration frustrates 78% of enterprises. Old systems were not built to talk to new ones. Data sits in silos. Formats don’t match. Every integration becomes a custom project.
Data quality issues affect 37% of organisations. You cannot automate decisions if the data feeding those decisions is incomplete or wrong. Garbage in, garbage out applies to AI just as much as any other system.
Skills gaps create bottlenecks because demand for AI specialists outpaces supply. Companies either pay premium rates for scarce talent or wait months to hire. Neither option is good for transformation timelines.
Small Business vs. Enterprise Approaches
Small businesses and large enterprises approach transformation differently.
55% of U.S. small businesses used AI in 2025, up from 39% in 2024. They focus on quick wins in sales and marketing. They use accessible tools like Microsoft 365 Copilot, Google Workspace with Gemini, and ChatGPT. They want production results in 90 days.
Enterprise focuses on platform-level capabilities. Multi-department deployment with formal governance. Custom solutions on cloud platforms. Multi-year transformation roadmaps.
Neither approach is wrong. But they require different expectations and different measures of success.
Where to Start
Small businesses typically invest $1,000-$100,000 over 1-6 months. A practical budget framework dedicates 40-50% to technology and infrastructure, 20-25% to integration, 15-20% to training, and 10-15% to change management.
Start with high-volume, rule-based processes that currently require significant manual effort. Invoice processing, email personalisation, and report generation are common starting points. These are low-risk, high-visibility wins that build momentum for bigger changes.
What Comes Next
Agentic AI represents the next frontier. These are systems that autonomously plan and execute multi-step tasks. 23% of organisations are already scaling agentic systems. Another 39% are experimenting.
By 2026, agents will evolve from assistants to virtual employees handling end-to-end workflows. Marketing agents that draft, test, launch, and adjust campaigns without human intervention. Logistics agents rerouting thousands of shipments automatically.
The AI agents market is projected to grow from $5.4 billion in 2024 to $50.3 billion by 2030. That’s a 45.8% compound annual growth rate.
Transformation is not a one-time project. It’s an ongoing capability. The companies building that capability now will have a significant advantage over those still treating digital as an IT problem.
Editor’s note: This article is a guest contribution by Darren Tredgold. The views expressed are the author’s own and do not necessarily reflect the views of Industry Examiner or its editors. Guest submissions may be edited for clarity, style, and length.
