Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

Leaders mistakenly treat AI like prior tech shifts (cloud, digital). However, those were deterministic, whereas AI is probabilistic and constantly learning. Building AI on rigid, 'if-then' systems is a recipe for failure and misses the chance to create entirely new business models.

Companies run numerous disconnected AI pilots in R&D, commercial, and other silos, each with its own metrics. This fragmented approach prevents enterprise-wide impact and disconnects AI investment from C-suite goals like share price or revenue growth. The core problem is strategic, not technical.

Many firms are stuck in "pilot purgatory," launching numerous small, siloed AI tests. While individually successful, these experiments fail to integrate into the broader business system, creating an illusion of progress without delivering strategic, enterprise-level value.

Organizations that default to treating AI as an IT-led initiative risk failure. IT's focus is typically on security and risk mitigation, not growth and innovation. AI strategy must be owned by business leaders who can align its potential with customer needs, talent decisions, and overall company growth.

AI models fail in business applications because they lack the specific context of an organization's operations. Siloed data from sales, marketing, and service leads to disconnected and irrelevant AI-driven actions, making agents seem ineffective despite their power. Unified data provides the necessary 'corporate intelligence'.

Simply giving AI tools to existing departments like legal or finance yields limited productivity gains. The real unlock is to reimagine and optimize end-to-end, cross-functional processes (e.g., 'onboarding a new supplier'). This requires shifting accountability from departmental silos to process owners who can apply AI holistically.

An engineering org was effective at using AI for delegating and assessing code, but failed to tackle large problems. The missing piece was a dedicated 'planning phase' to scaffold significant work before execution. Without it, their AI-driven compounding of learnings was limited to small, incremental gains.

Housing AI strategy within IT is a critical error. The most valuable applications of AI are not technological but rather business innovations. The conversation must be led by business leaders asking what is now possible for customers and partners, with IT acting as an enabler, not the primary owner.