Practical thinking on AI for business — articles in this section.
Field service automation can deliver 195% ROI, but only for organizations that solve data quality and process problems before implementation. This analysis breaks down where returns actually come from—scheduling efficiency, first-time fix rates, and technician enablement—and why 30-40% of implementations see less than 60% adoption. Learn what separates top-quartile performers from companies that never hit positive ROI.
IntegrationConnecting Slack to Salesforce takes fifteen minutes. Getting value from that connection takes three to six months of deliberate process work that most organizations skip. This piece examines why Slack-CRM integrations fail within six months, how AI agents compound governance problems, and what the successful minority does differently to build integrations that actually deliver lasting productivity gains.
AI StrategyBetterment's new AI account recommender follows a familiar pattern: narrow recommendation engines branded as enterprise AI strategy. What separates the 20% that get adopted? Starting with specific friction points, instrumenting baseline behavior before launch, and building human fallbacks from day one. The hard part isn't the technology—it's change management and closing the gap between technically functional and operationally adopted.
AI StrategyWTW's acquisition of Newfront reveals a critical truth about enterprise AI transformation: putting startup founders in charge of company-wide AI deployment signals that operator experience matters more than technical knowledge. While internal AI initiatives often stall in pilot purgatory, leaders who've shipped products under real market pressure bring fundamentally different instincts. This raises an uncomfortable question for mid-market companies—do you have anyone who's actually built AI into production workflows, or are you asking people to learn while carrying existing responsibilities?
AI StrategyOpenAI's Sora shutdown highlights a critical enterprise AI risk: vendor discontinuation. Learn why operations leaders should architect for portability, treat vendor lock-in as a balance sheet risk, and build contingency plans assuming any AI platform could pivot or disappear within 18 months.
AI Strategywhy-ai-pilots-fail: The failure rate for enterprise AI initiatives is well-documented. Less discussed is what the successful 22% consistently do differently. After dozens of AI implementations, patterns emerge — and most are operational, not technical.
AI StrategyMost enterprises are debating whether AI belongs under IT or a Chief AI Officer, but governance without capability is just bureaucracy. The real question isn't who's in charge of AI—it's who's accountable for business outcomes and has the power to kill projects that aren't working. Clear AI ownership means authority to audit data quality, consolidate redundant experiments, and shut down tools that deliver nothing.
ImplementationThe most expensive mistake in AI implementation is discovering data problems after the project is already resourced and underway. Budget has been allocated, vendor agreements signed, timelines communicated…
AI Operationsmodel-drift: Every AI model in production will eventually degrade. The question is whether you find out from your monitoring system or from your customers.
Integrationai-and-erp: Integrating AI into an existing ERP system is rarely as straightforward as vendors suggest. Here's what the integration actually involves — and where projects typically break.
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