‘AI is eating software’ and it is redefining supply chain decision-making as a result

Supply chain technology continues to evolve, and one of the changes taking place revolves around autonomy and the increasing use of artificial intelligence.

“The difference between deterministic and non-deterministic systems is where we’re headed,” said Pano Anthos, founder and managing partner at XRC Ventures, during a recent conversation with Supply Chain Management Review.

For decades, enterprise supply chains were designed around deterministic logic, standard operating procedures, Six Sigma rules, and rigid workflows. “Enterprises are deterministic,” Anthos said. “You will do this and do it this way.”

But agentic AI is challenging that structure, Anthos said. “What would happen if you put AI out there and said, ignore the SOP … don’t break any rules, but get the job done?” he asked.

From rigid rules to adaptive systems

Anthos argues that many corporate rules exist not because they are optimal, but because they were designed for human constraints. Historically, processing power and analysis costs forced simplification, he said.

For example, deciding whether to ship via air or ocean often defaulted to air for simplicity. “You always ship air freight … keep it simple,” he said, noting that an analysis might have turned up a different answer, but the costs to run that analysis didn’t justify doing so.

With AI, computing tradeoffs becomes instantaneous, though, and decisions once considered too complex or time-consuming can now be modeled continuously.

Computer vision and the “blind warehouse”

Beyond decision logic, Anthos sees another major transformation unfolding inside physical operations. “The cost of computer vision is dropping,” he said. “What you’re going to see is more real-time diagnostics capability inside the warehouse and manufacturing floors.”

Warehouses historically relied on visual supervision and periodic audits. “The factory manager sits in a little office off to the side with a little window,” he noted. “Those days are going away.”

Computer vision acts as a diagnostic layer identifying problems in real time. That has implications for safety, insurance rates, and productivity.

The rise of vertical AI

In the investment community, Anthos says the biggest opportunity isn’t foundational large language models, but vertical AI applications.

“There’s a lot of interest in vertical AI right now,” he said. “You can unlock a lot of value and sell a company with very little capital” if it is building vertical AI. He cited an example of a five-person company managing aircraft parts supply with AI for Iberia Airways.

The system, which can be installed in just a day, monitors procurement communications by analyzing email patterns rather than requiring deep ERP integrations. “They sniff email … they attach to your email system and watch traffic between buyers and suppliers,” he said.  

The impact? Anthos said the solution reduced delays by 25%.

Such capital-efficient, domain-specific solutions represent a departure from the traditional “big consulting, big deployment” model. Anthos believes integration barriers have fallen dramatically thanks to API architectures and AI mediation layers.

AI is eating SaaS

Anthos also argued that legacy SaaS platforms are vulnerable because “AI is eating software.”

In essence, he said many providers are adding AI on top of current solutions rather than building natively around AI. The result is that companies are not getting “truly agentic AI [solutions],” he said.

In contrast, AI-native systems operate flexibly, capable of moving across datasets and functions autonomously. He predicts increased pressure on traditional ERP processes such as procurement, freight audit, and CRM workflows where AI can automate root-cause analysis and decision support.

“Freight audit—AI is destroying the freight audit business,” he said.

Instead of merely flagging discrepancies, AI identifies patterns and suggests preventive actions. That shift from reporting “what is” to diagnosing “why it is” is critical, he said.

“I don’t want to know the invoice is 90 days late,” he said. “I want to know why.”

Agentic friction: Cultural and political barriers

Yet while technology advances quickly, corporate culture lags. “Boards are pounding CEOs; what are you doing about AI?” Anthos said. But, executives often respond by installing AI tools reactively rather than strategically. Many projects fail because they don’t address real pain points.

More importantly, agentic systems disrupt political structures. “Mid-level managers don’t like that,” Anthos said of AI surfacing root causes across silos. Autonomous analysis reduces information gatekeeping and challenges hierarchies built around controlling access to data.

A flood of applications

The scale of change may be overwhelming, and quickly.

“There’s a projection that there will be 1.4 million native AI applications by 2030,” Anthos said.

That raises questions about consolidation, sustainability and enterprise readiness. How many freight audit AI systems does the market need, for instance? Or how many procurement agents are really needed?

Investors like Anthos are increasingly looking for capital efficiency and defensible domain expertise. “We’re looking for a team that will break a brick wall down,” Anthos said.

Ultimately, Anthos believes the future of supply chain AI lies in reasoning rather than reporting. “It’s the why and the root cause analysis where there’s real opportunity,” he said.

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