Over a four-decade business career, the current velocity of change driven by artificial intelligence stands unmatched by any prior technological shift, surpassing even the rise of personal computing and the internet. Yet, a fundamental principle of executive leadership remains unchanged: grounding investments in reality and driving meaningful innovation requires listening directly to the customer.

While some technology vendors mistakenly assume that clients do not fully understand what they need from artificial intelligence, months of global executive engagement across North America, Asia, and Europe prove otherwise. Conversations with chief executive officers, chief financial officers, and chief information officers across industries ranging from retail and manufacturing to financial services reveal a unified framework of expectations that vendors, including Anaplan, must fulfill.

These discussions establish seven non-negotiable requirements:

1. An Accuracy Imperative

Large language models operate predictively through pattern recognition to interpret context and generate outputs. While powerful, these models lack the absolute mathematical precision required for standard business operations. Financial reporting, for instance, demands flawless, fully auditable accuracy. Without deterministic AI paired with LLMs to guarantee reliable, traceable calculations, predictive models fail the core demands of the enterprise.

“For all their power in generating answers, LLMs do not deliver the precision and mathematical accuracy required for standard enterprise use cases.” Charlie Gottdiener, CEO, Anaplan

2. Data Must Be Coupled With Context

Corporate data assets, from supply chain components to transaction logs, remain vital inputs for AI. However, raw data without context holds minimal value. Business context allows AI to comprehend numbers and text accurately, mapping their relationships across various organizational systems. A solitary figure lacks meaning until tied to a specific product SKU and price tag. Enterprise data without this contextual framework is entirely useless.

“A number by itself is meaningless, but when that same number is connected with a product SKU and price, it now has context. Data without enterprise context is useless.” Charlie Gottdiener, CEO, Anaplan

3. Headless AI, No Humans Clicking Buttons

Enterprise AI solutions must operate openly and integrate seamlessly within existing technological infrastructures. Programmatically exposing core capabilities allows the user interface to adapt into anything from a Slack channel to custom third-party agents. Decoupling capabilities from rigid interfaces transforms AI into an effective orchestrator of multi-source enterprise data, removing operational bottlenecks.

4. Labor Productivity And Compressed Cycle Times

Generative AI carries clear expectations: accelerate workflows, unearth unique insights, drive labor efficiency, and compress operational cycles. Although early in realizing this efficiency wave, organizations view demonstrable productivity gains as fundamental table stakes.

5. Software’s New UX Must Be Native And Intuitive

Traditional software competition relied on proprietary, visually engaging user interfaces equipped with pre-built dashboards. AI is disrupting this paradigm. Rather than juggling multiple applications, corporate users expect conversational, intuitive AI capabilities embedded natively within the software tools they already utilize.

“Users don’t want to bounce from one application to another to garner insights. Instead, they want AI to meet them where they are.” Charlie Gottdiener, CEO, Anaplan

6. A Compelling ROI And Cost Transparency

Achieving a strong return on investment is expected with AI, just like any other enterprise technology. However, unpredictable token metering complicates financial tracking. Anaplan customers consistently emphasize that a solid ROI must accompany complete visibility into token usage, offering transparent cost management and operational control.

7. Yes To Gen AI, But Never At The Expense Of Data Privacy Or Security

Data security and privacy represent absolute prerequisites for enterprise AI adoption. Organizations demand strict governance over how proprietary and sensitive information is managed throughout an AI lifecycle, protecting against unauthorized access, theft, or model training misuse. Anything less than best-in-class security controls falls short of market expectations.

As artificial intelligence adoption accelerates, the potential for enhanced decision-making and performance is immense. Companies will naturally gravitate toward technology providers who align with these seven essential standards. Because customers have clearly articulated their requirements, enterprise architecture must adapt accordingly, recognizing that compromise on any front inevitably leads to underwhelming returns.

With high stakes defining the current market landscape, settling for mediocrity is simply not an option.