{"id":13082,"date":"2026-02-02T09:00:00","date_gmt":"2026-02-02T17:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=13082"},"modified":"2026-02-21T01:47:41","modified_gmt":"2026-02-20T14:47:41","slug":"how-enterprise-ai-will-overcome-last-mile","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/au\/news\/stories\/how-enterprise-ai-will-overcome-last-mile\/","title":{"rendered":"The Last-Mile Challenge Is Real. Here\u2019s How Enterprise AI Will Overcome It"},"content":{"rendered":"\n

From vacation planning to homework help, the adoption of AI<\/a> by consumers has happened at breakneck speed, while enterprises often still struggle<\/a> to go from a first pilot to broad deployment. <\/p>\n\n\n\n

That last-mile challenge will be familiar to anyone who remembers the rollouts of email, broadband, the cloud, and other technology innovations. It\u2019s the gap between staging impressive demos and making those systems work reliably in real-world operations, as early potential rams into the countless challenges that lie between exciting prototype and adoption at scale. Enterprise AI is just the latest innovation to fall into this chasm. <\/p>\n\n\n\n

But the good news is, by historical standards, companies have the opportunity to cross the last mile faster than ever when it comes to enterprise AI<\/a>. The ones that can embrace change and comprehend what it takes are the ones that will succeed. <\/p>\n\n\n\n

The Traditional Adoption Curve<\/strong><\/h2>\n\n\n\n

In the digital era, transformative technologies have typically followed a five- to 10-year adoption curve. Consumer products often move a little faster \u2014\u00a0U.S. smartphones, for instance, reached 50% penetration just five years after the iPhone was released. But enterprise infrastructure often moves more slowly. That\u2019s because full adoption requires internal consensus and process redesign. Cloud computing, for example, took roughly a decade to achieve mainstream acceptance after Salesforce pioneered software as a service<\/a> (SaaS) in 1999. Even then, IT departments struggled to trust their data to servers they didn’t control.<\/p>\n\n\n\n

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Agentic AI isn\u2019t just a technology implementation challenge; it\u2019s a change-management challenge as well.<\/p>\n<\/blockquote>\n\n\n\n

Diffusion constraints also include hardware costs, infrastructure requirements, or distribution challenges. The first mainframes, for example, were room-sized monsters requiring raised floors for cable management, industrial air conditioning, and a dedicated staff. While broadband services were available in the 1990s, widespread adoption depended on physical wiring, routers, and carrier availability. Email took several decades to travel from Ray Tomlinson\u2019s 1971 ARPANET experiment to standard business infrastructure in the mid- and late 1990s. <\/p>\n\n\n\n

Consumer AI has rocketed ahead \u2014 OpenAI\u2019s ChatGPT reached 100 million personal users within two months of its 2023 launch<\/a> \u2014 but enterprise AI is taking longer to achieve liftoff beyond the prototype stage. That has led to a disconnect.<\/p>\n\n\n\n

\u201cAI doesn\u2019t conform to the traditional tech adoption pattern,\u201d said Jim Cavalieri, a 25-year Salesforce veteran and advisor to the chair and CEO of the company, \u201cbecause it’s spreading with consumer-tech speed while demanding enterprise-level organizational change.\u201d<\/p>\n\n\n\n

Just think of all the elements agents need to understand a business, perform consistently, and become key elements of a daily workstream. They need context \u2014 from a CRM<\/a>, service platform, data warehouse, and collaboration tools \u2014\u00a0so they can all operate from a single source of truth. They need metadata to understand the relationships between these systems, the business logic that governs data flow, and specific role-based permissions that control access. They need to be accessible in the place where work is happening<\/a>. Furthermore, agentic AI isn\u2019t just a technology implementation challenge; it\u2019s a change-management challenge as well. Agentic AI<\/a> reshapes how knowledge workers think, create, and make decisions, so it triggers organizational, cultural, and incentive-based resistance.<\/p>\n\n\n\n

Challenging as those hurdles may be, they are where true business value lies. “The trapped value is always in latency,” said Geoffrey Moore, author of \u201cCrossing the Chasm\u201d and a Salesforce advisor. “Where in our current operating model is the bottleneck that constrains our overall performance? When we know where the bottleneck is, we know where the trapped value is.” <\/p>\n\n\n\n