{"id":3841,"date":"2026-07-20T22:15:30","date_gmt":"2026-07-20T22:15:30","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=3841"},"modified":"2026-07-20T22:15:30","modified_gmt":"2026-07-20T22:15:30","slug":"salesforce-vp-on-the-leaky-ai-pipeline-why-cheaper-tokens-wont-fix-enterprise-ai","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=3841","title":{"rendered":"Salesforce VP on the leaky AI pipeline: why cheaper tokens won\u2019t fix enterprise AI"},"content":{"rendered":"
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But this year, the conversation changed. In nearly every executive review, the question is no longer \u201cHow do we get more AI?\u201d or \u201cWhich model is smartest?\u201d It\u2019s \u201cHow do I generate impactful outcomes, while keeping AI costs under control?\u201d Different companies have different answers. Uber tightened internal AI spending after consumption ran far ahead of plan. Anthropic, OpenAI and Google have aggressively cut input token prices and rolled out prompt caching.\u00a0 And Cursor, with Composer 2.5, now considers cost an important factor in model selection \u2014 not just performance.\u00a0<\/p>\n

Read more Christopher Nolan was rejected from film school as a teen. Today, the Oscar-winning director behind\u00a0 \u2018The Odyssey\u2019 is worth $250 million<\/a><\/p>\n

Cutting token and model costs are one solution, but they ignore the more profound issue: Most companies are not architected to use tokens efficiently. The modern AI pipeline behaves more like a sieve \u2014 leaking tokens, and dollars, at every phase of execution. Pouring cheaper tokens through a leaky foundation isn\u2019t a sustainable solution. To truly generate great, cost-effective outcomes, companies need an architectural foundation that closes those leaks. Here\u2019s how.<\/p>\n

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Figure 1 \u2014 The Leaky AI Pipeline: where tokens, and money, are lost every day.<\/em><\/p>\n

Token optimization requires more than smart prompting. It takes an architecture designed to address the five predictable failure points that show up again and again:<\/p>\n

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  1. : Context window bloat is the first leak. The more information you stuff into a prompt, the more you pay and the more the model drifts. The fix comes in adding context before<\/em> the prompt stage \u2014 with a data foundation that agents can rely upon to always provide the most reliable version of the truth. Master data management uses deterministic matching, merging, and deduplication to unify overlapping or conflicting sets of data in one golden record, ensuring that agents always have access to the right customer, the right account, the right policy, the right transaction and the right recent activity \u2014 not through the prompt, but built into the infrastructure of every single interaction.<\/li>\n
  2. . Without clear governance, agents roam aimlessly through data warehouses looking for credible information, then check the results after the fact \u2014 wasting time and tokens, and generating inconsistent actions. Establishing a data catalog, lineage, permissions and quality signals makes governance a routing signal instead of a checkpoint, directing the agent to certified data on the first hop.<\/li>\n
  3. Algorithmic brute force \u2014 sending every task to a frontier model \u2014 is the most expensive habit in the building. Cost-routing work through the right model, instead of\u00a0 the biggest one, is one of the best ways to increase efficiency. Cost-route the work. Treat models as a portfolio: small and efficient for lookups and classification, tuned mid-tier for domain workflows, frontier reserved for novel reasoning and sensitive judgment.<\/li>\n
  4. Every stateless agent starts each interaction from scratch. It has to reload context, reprocess history and rediscover exceptions that were already resolved. Structured memory turns each new interaction into an incremental one. The agent keeps and retrieves the useful state when needed \u2014 relevant facts, prior decisions, user preferences and open tasks \u2014 instead of starting from zero. That reduces token consumption, improves consistency, and lowers cost per interaction.<\/li>\n
  5. Too many enterprise agents process recurring requests as if they are seeing them for the first time. For example, re-reading the same context, re-running the same retrieval, and re-generating answers that could have been cached, embedded or pre-computed. By pairing prompt caching, embedding reuse and pre-computed outputs with the semantic structure already present in enterprise data flows, companies can make repeat requests progressively cheaper, faster and more reliable<\/li>\n<\/ol>\n
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    Token optimization is the difference between inefficient experiments and productivity-driving foundations of the business.\u00a0<\/p>\n

    A large enterprise running AI agents for sales or service operations might process thousands of agent interactions daily. Each interaction that passes raw, uncurated data into an LLM prompt consumes 5\u201310x more tokens than necessary. At $10\u201315 per million tokens, that compounds fast across a fleet of agents. Enterprises that invest in Master Data Management and data governance create a natural advantage \u2014 drawing on pre-curated, semantically enriched, quality-scored data rather than raw noise.\u00a0<\/p>\n

    Read more FDA walks back positive lab test while lettuce recall continues<\/a><\/p>\n

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    I\u2019ve seen this advantage firsthand with the customers I work with every day. I see this firsthand in my work with enterprise customers. When Informatica\u2019s master data management integrates with Data 360, Salesforce\u2019s Customer Data Platform, it ensures every AI agent is grounded in trusted customer and business context.\u00a0 Together, the\u00a0 architecture turns un-governed AI consumption into governed, measurable business value.\u00a0<\/p>\n

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    Every company can buy more tokens. Very few know how to extract more value from fewer of them. The next phase of enterprise AI will not be defined by who consumes the most tokens, runs the largest models, or fills the biggest context windows. It will be defined by who can turn AI consumption into governed, measurable business value.<\/p>\n

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    The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of <\/em>Fortune.<\/em><\/p>\n

    Read more Spain\u2019s Ferran Torres scored the winning goal in the World Cup\u2014but just four years ago, he was in therapy and stuck in a \u2018bottomless pit\u2019<\/a><\/p>\n

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    Enterprises waste 5x-10x more tokens than needed on raw data, and switching models alone won’t fix the leak.<\/p>\n","protected":false},"author":1,"featured_media":3840,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[211],"tags":[],"class_list":["post-3841","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agents"],"yoast_head":"\nSalesforce VP on the leaky AI pipeline: why cheaper tokens won\u2019t fix enterprise AI - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=3841\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Salesforce VP on the leaky AI pipeline: why cheaper tokens won\u2019t fix enterprise AI - 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