Throughout a four-decade career in business, the current velocity of artificial intelligence adoption stands unmatched, eclipsing the massive paradigm shifts seen during both the personal computer revolution and the dawn of the internet. Yet, despite this unprecedented speed, a fundamental rule for corporate executives driving digital transformation remains untouched: grounding investments in reality requires sourcing the roadmap directly from the end user.

While some technology suppliers mistakenly cling to the notion that clients are unaware of their own AI needs, six months of intensive listening tours across North Asia, Europe, and North America prove otherwise. Conversations with chief executive officers, chief financial officers, and chief information officers across major industries—ranging from manufacturing and retail to financial services—reveal a unified corporate wishlist. These leaders have outlined exactly what they expect from vendors like Anaplan.

1. An Accuracy Imperative

Large language models operate predictively through statistical pattern recognition, expertly parsing context and managing ambiguity. Despite their generative prowess, these models lack the mathematical precision required for routine corporate operations. Financial reporting, for instance, demands flawless exactness alongside complete traceability and auditability. While LLMs drive general productivity, failing to pair them with deterministic AI for fully reproducible and dependable calculations breaks the core accuracy mandate.

“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

A corporation's vast stores of internal information—spanning customer transaction histories to intricate supply chain components—serve as vital fuel for artificial intelligence. However, raw data absent context holds virtually zero practical value. Enterprise context empowers AI to comprehend the true significance of raw figures, mapping how numbers relate to specific systems, organizational frameworks, and origins. An isolated number means nothing, but that same digit paired with a specific price and product SKU gains immediate meaning. Uncontextualized data 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

Modern AI solutions must remain open and integrate seamlessly within existing corporate technical architectures. Practically speaking, this requires technology investments to expose fundamental capabilities programmatically, allowing user interfaces to range from custom third-party agents to standard messaging platforms. De-coupling system functions from rigid interfaces allows artificial intelligence to orchestrate information fluidly across disparate business systems, removing operational bottlenecks and driving technical agility.

4. Labor Productivity And Compressed Cycle Times

Generative AI carries an unmistakable mandate: accelerate workflows, unearth deeper insights, drive efficiency, and compress operational cycle times. Although the broader quest for peak operational efficiency is still in its early stages, the expected productivity returns are remarkably high. Delivering demonstrable efficiency gains has officially become a baseline requirement.

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

For generations, software developers built competitive advantages on proprietary user interfaces characterized by visually dynamic dashboards and pre-built summaries. Artificial intelligence is completely dismantling this model. Modern enterprises increasingly demand AI capabilities embedded directly within the software they already use daily. Users reject the friction of jumping between disparate applications for insights, preferring instead an intuitive, conversational AI experience that meets them right inside their active workflows.

“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

Just like any major corporate investment, artificial intelligence faces intense pressure to deliver a tangible return on investment. Yet, calculating that financial return becomes exceptionally difficult amid confusing and unpredictable token-based metering. A recurring concern raised by enterprise clients involves pairing strong returns with absolute clarity regarding token expenses. Organizations require total visibility into usage metrics to maintain strict cost control and effective budget management.

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

Data protection and digital security remain completely non-negotiable for corporate artificial intelligence deployments. This requires absolute authority over how sensitive, proprietary, and confidential corporate details are managed throughout the entire operational lifecycle of any AI framework. Businesses are entirely justified in protecting their proprietary advantages and customer information from being improperly utilized in third-party model training. Furthermore, this information demands top-tier protective technologies, including robust access management, comprehensive encryption, and secure infrastructure. Anything less than elite security protocols falls drastically short of corporate expectations.

As artificial intelligence continues its breakneck evolution, the potential to elevate performance, decision-making, and output remains staggering. Organizations rolling out these capabilities will inevitably favor technology partners who satisfy every single item on this critical checklist. Enterprise clients have clearly articulated their demands, and vendors ignore them at their peril. Compromising on any of these foundational requirements will ultimately guarantee underwhelming financial returns and subpar operational results.

When so much hangs in the balance, simply being good enough will never suffice.