ROI on Agentic AI automation

As an AI automation advisor, one discussion topic that comes up consistently is the ROI on agentic AI automation.

Agentic AI automation has significant potential to transform core business processes across HR, Marketing, Sales, Accounting, IT and several other functions.

However, the costs involved in enabling AI automation can vary and are often significant.

Many enterprises appear to be struggling to justify their spend on agentic AI automation. Most notably, leading analyst firm Gartner recently quoted:

“By the end of 2027, over 40% of agentic AI projects will be canceled”.

The research firm attributes this expected failure rate to several key factors: escalating costs, unclear business value, and inadequate risk controls.

It is critical that CIO’s and business leaders understand that while Agentic AI has tremendous potential, successful adoption of autonomous agents requires much more than just build and deploy.

In general, it is critical to consider these 5 factors when selecting processes for Agentic AI automation:

#1 Business Priority: When evaluating use cases for Agentic AI within your organization, consider if the process is a labor intensive process with direct ties to revenue. Also, look at this from a competitive lens and prioritize this process if it is a key competitive differentiator.

#2 Task Type: Organizations can gain maximum value from Agentic AI if the underlying tasks to automate have the following characteristics:

• Task is repetitive or rules based

• Task is high-volume/error prone

• Sufficient training data exists

• Process has less variability

#3 Cost Model: When building a cost model for your agentic AI process consider the following cost drivers:

• Dev Cost of initial solution.

• Recurring costs- For LLM execution

• Governance Risk Compliance & Security costs.

Cost should be 5-10X lower than current process costs including labor cost savings.

#4 Technology: Intended to execute autonomously, AI agents often cannot operate in pure isolation. The access to data on an ongoing basis (via Systems integration connectors) along with connectivity to tools via Model context protocol (MCP) or Agent-to-Agent (A2A) protocols are equally critical when selecting processes.

#5 Change Management: Successful adoption will heavily depend on how human workers integrate the agents into their existing workflows.

Historic evidence from deployment of transformative solutions such as ERP’s indicates that the human element of change management is critical to success. However, Agentic AI takes this one step further where not only do humans need to be comfortable with the new automated workflow for their processes, but need to also digest the fact that some decisions that would otherwise be performed by them, are now being delegated to AI. .Pick processes where executive sponsorship is high and on-going support exists. To maximize success, also invest in an AI center of excellence (COE) to providing on-going oversight around reliability, security, compliance etc.

Whether you’re mid-size or Fortune 500 CIO, a disciplined, criteria-based approach ensures every dollar invested in AI agents is aligned to business value and is risk-aware.

If you’d like to discuss how to tailor this model to your industry or build a business case for agentic AI automation for specific processes, I’m happy to connect.