Agentic AI's first big challenge

This article was drafted by Philip Atkinson, Analyst, and Rhea Oliyath, Research Specialist.

Two weeks ago, a new AI automation tool from Anthropic triggered a selloff across software, financial research, and broader markets. In recent years, Generative AI has grabbed headlines, and valuations have reached sky-high levels. The selloff has once again put Agentic AI under close scrutiny, as the market seeks evidence of tangible, scalable use cases.

Enterprises are already investing heavily in AI. The financial sector has been among the earliest adopters of agentic AI, as our experts say its high-volume, compliance-heavy operations offer the grounds for clear efficiency gains and measurable ROI. According to Statista, the financial sector will spend $97 billion on AI in 2027, while global AI spending is expected to reach $632 billion in 2028, with a 29% compound annual growth rate, and financial services accounting for around 20% of total AI spending.¹

Source: Agentic AI Sector – Taking Market Share in Financial Institutions' Tech Stacks

The biggest challenge: Proving ROI

Despite the large sums being invested in AI, our discussions with multiple experts across different interviews point to a common theme: the biggest challenge remains proving ROI. A report from MIT found that 95% of enterprise AI initiatives deliver 0 measurable return.²

Conversations with our industry experts also point to a cautious sentiment: monetisation is still largely unproven, and competition is likely to erode pricing power. This issue is compounded by ticket sizes that are roughly ten times higher than previous robotic process automation (RPA) solutions.³  Looking into 2026, most experts we spoke with expect monetisation to remain difficult and to plateau, even though agentic capabilities may still be necessary for competitive differentiation.

While many companies are willing to explore agentic AI solutions, experts say CFO sign-off remains limited, with only around 20–30% of trials moving into full implementation, and momentum is beginning to slow. 

As one expert noted during our September 2025 interview: 

“CFOs appear cautious when transitioning from the exploration phase to full implementation… When it comes to results and signing off on things, I think the numbers would probably be on the lower end [20–30%].” 

Agentic AI for ERP Software – How Vendors & ISVs are Competing for the Future of Automation

Agentic AI’s first big payoff may be process automation

Industry experts we spoke to agree that agentic process automation (APA) is the most likely next “win” for agentic AI. Agentic process automation (APA) uses AI agents to autonomously execute end-to-end business processes—such as invoice processing, customer onboarding, IT helpdesk support, and claims handling—by planning, making decisions, and acting across multiple systems without constant human intervention.

Unlike broader AI applications, APA offers clear and measurable ROI through cost reduction, faster processing, and improved compliance. It also builds on existing RPA infrastructure, making adoption easier for enterprises that already use automation tools. This makes APA a more realistic and immediate value driver than other agentic AI use cases that are harder to measure or riskier to deploy.

However, more complex applications, such as customer experience business process outsourcing (CxBPO)⁴, have struggled to prove the value of automated Agentic AI solutions, with some players re-prioritising the need for human involvement.

APA has also led to the emergence of a broader software ecosystem, allowing process mining and workflow-related companies to capture spend. While they may not offer automation solutions directly, these ecosystem tools help identify processes suitable for automation and simplify workflow creation. This enables less technologically skilled personnel to benefit from APA.

Agentic AI growth is coming, but incumbent RPA vendors may not be the winners

Investors remain skeptical about existing automation players’ ability to capture market share. An example is UiPath. Its strategic partnerships with Nvidia and OpenAI initially sparked sharp gains, with shares rising more than 20% around the announcements in late September 2025, but much of that upside later retraced as enthusiasm faded and questions about near-term monetisation persisted. Shares are down 27% year-to-date as of 5 February 2026.

Source: Stock Analysis

An expert we spoke with said UiPath and other incumbents are prioritising the conversion of their existing RPA customer base to secure revenue during this shift, and have consequently captured about 20% of current APA spend. 

However, as competition intensifies, incumbents are likely to face slower expansion, with growth moderating compared with earlier years. In the third quarter of fiscal 2026, UiPath reported revenue up about 16 % year‑over‑year and ARR up roughly 11 %, reflecting a meaningful deceleration from the ~24 % ARR growth seen in 2023–24. These current growth rates remain well below the rapid RPA‑driven revenue expansion of 40–80 % in 2021 and 2022. 

Taking share are new automation entrants including hyperscalers  (Microsoft, Amazon, Google) and independent software vendors (ISVs) such as SAP, Salesforce, and ServiceNow, with both buckets capturing 40% of spend each.  They are committing billions of dollars in capital expenditures to enable agentic functionality. Private markets are also backing companies with billion-dollar valuations, such as n8n.

Hyperscalers have lowered the costs of agent creation, quoting prices up to 37% cheaper than incumbents, with consumption-based spend difficult to displace later.

However, our experts say it is the ISVs that may capture the most spend going forward and potentially pose the greatest threat to incumbents. By focusing on business processes and offering simpler, pre-packaged agent frameworks, ISV solutions such as SAP’s Joule are already displacing existing automation solutions.

Automation pure-players, including incumbents, may be able to claw some of this back by prioritising customisation; however, this risks complicating customer IT infrastructure.

Further reading: see relevant transcripts and key insights here.

All insights in this article are based on information shared by Third Bridge experts. 

For media enquiries, please contact us at comms@thirdbridge.com.

^References:

^{1. https://www.statista.com/statistics/1446037/financial-sector-estimated-ai-spending-forecast/#:~:text=The%20sources%20provided%20the%20financial,AI%20spending%20(20%20percent).}

^{2. https://www.forbes.com/sites/andreahill/2025/08/21/why-95-of-ai-pilots-fail-and-what-business-leaders-should-do-instead/}

^{3. Robotic Process Automation (RPA) is a technology that uses software robots (bots) to automate repetitive, rule-based tasks that are typically performed by humans.}

^{4. Customer Experience Business Process Outsourcing (CxBPO) is the outsourcing of customer-facing processes—such as contact centre support, customer onboarding, and complaint handling—to specialised service providers (for example, a bank outsourcing its call centre and customer support operations).}

^{Transcript references:}

^{1. Agentic AI Sector – Taking Market Share in Financial Institutions' Tech Stacks}

^{2. Customer Success BPO Sector – Tackling the Agentic AI Question}

^{3. Agentic AI for ERP Software – How Vendors & ISVs are Competing for the Future of Automation}

^{4. UiPath – Agentic AI Transformation & Implementation Journey}

^{5. Celonis – Customer Footprint Expansion & ROI}

^{6. N8n – Demand for Low-code Workflow Tools in a Changing Automation Environment}