Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.<\/p>\n
The \u201cbuild it and they will come\u201d model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.<\/p>\n
\u201cWhen we integrate FinOps capabilities with automation, we\u2019re looking at a change from being very reactive on cost management to being very proactive around value engineering,\u201d says Holmes.<\/p>\n
This shifts the assessment criteria for technical leaders. Rather than waiting \u201cmonths or years to assess whether things are getting value,\u201d engineering teams can track resource consumption \u2013 such as cost per transaction or API call \u2013 \u201cstraight from the beginning.\u201d<\/p>\n
The unit economics of scaling intelligent automation<\/p>\n
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.<\/p>\n
\u201cIf a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that\u2019s really successful,\u201d says Holmes. \u201cBut what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn\u2019t over-provision to that degree during a real production rollout.\u201d<\/p>\n
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. \u201cAPI calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,\u201d he adds.<\/p>\n
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.<\/p>\n
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and \u201cnot just looking at labour hours that they were saving.\u201d<\/p>\n
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance \u201cback in the hands of the developers into their development tools and workloads.\u201d<\/p>\n
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.<\/p>\n
\u201cRather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,\u201d Holmes explains, companies can verify they are \u201cdeploying the right things at the right time.\u201d<\/p>\n
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.<\/p>\n
\u201cThis translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,\u201d says Holmes. \u201cIt\u2019s having a common language between technology and finance and with the business.\u201d<\/p>\n
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.<\/p>\n
\u201cI don\u2019t necessarily know what goes into all the IT layers underneath it,\u201d Holmes says, describing the business user\u2019s perspective. \u201cBut because we\u2019ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving\u00a0 it to be more expensive as I consume more.\u201d<\/p>\n
Addressing legacy debt and budgeting for the long-term<\/p>\n
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is \u201cjust trying to mask inefficient processes and not redesign them,\u201d they are merely \u201cbuilding up more technical debt.\u201d<\/p>\n
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications \u2013 of various maturity stages \u2013 to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.<\/p>\n
\u201cJust because of something\u2019s legacy doesn\u2019t mean you have to retire it,\u201d says Holmes. \u201cSome of those legacy systems are worth maintaining just because the value is so good.\u201d<\/p>\n
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. \u201cSometimes when you add up the TCO approach, and you\u2019re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it\u2019s those extra layers,\u201d Holmes argues.<\/p>\n
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.<\/p>\n
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.<\/p>\n
\u201cBecause you\u2019ve made those longer term commitments and you\u2019ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,\u201d Holmes says.<\/p>\n
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.<\/p>\n
IBM is a key sponsor of this year\u2019s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM\u2019s booth at stand #362.<\/p>\n
See also: Klarna backs Google UCP to power AI agent payments<\/p>\n
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.<\/p>\n
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\nThe post Apptio: Why scaling intelligent automation requires financial rigour appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour. The \u201cbuild it and they will come\u201d model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide [\u2026]<\/p>\n
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