tag:dlvr.it,2018-03-23:ac6861e9e2d249fc92b2895b5ff7a2e0 2026-07-05T13:29:22-07:00 tag:dlvr.it,2026-07-05:c772281af10321d388bd277624f291ae 2026-07-05T13:29:22-07:00
"Constellation Research CEO R "Ray" Wang said the never-ending stream of newly minted FDEs require buyer scrutiny. "I'm tired of looking at fake FDEs. A real FDE understands how to take the requirement and put it back into the product and actually get something done. These aren't glorified sales consultants or sales engineers. They actually are building product at the same time as they're actually going out to solve the problems at an organization, and they're not field CTOs, and they're not architects. The true FTEs are the folks that are like the consultants from the 90s, and I'm talking like folks that were literally had like 160 to 180 IQ. They knew what they were doing. Today there's so many fake FTEs." ]]> tag:dlvr.it,2026-07-05:91e260be4b9c14c405ea2c1b0fb1f301 2026-07-05T13:29:21-07:00
"CRM is not dead. The strategy is alive and well for those with the willingness to build and the patience to guide it forward. The technology is alive and well for those curious enough to explore modern paths to transformation. Gone are the static systems of record, introducing growth, engagement, experience, and revenue to a more active, more contextual, and more individualized approach. Together, with AI as an accelerator and transformational supercharger, CRM can once again take its place next to the customer and shoulder to shoulder with any brand’s CX front line. " ]]> tag:dlvr.it,2026-07-05:f5ac7d7bdb29cdc7feaddb0ff259587d 2026-07-05T13:29:20-07:00
"Finding the most capable model at the lowest cost has always been the goal. But as agentic AI evolves, a new problem is frustrating engineers: token consumption is becoming too high across AI systems. Every agent operation consumes tokens, but in very different ways. For instance, a moderately complex agent request can consume 20,000 to 60,000 tokens across its reasoning chain, but a nontrivial engineering task can burn 150,000 to 200,000 tokens per problem. Every agent operation consumes tokens, but in very different ways. Developers are realizing that selecting the right model is important, but the bigger issue is limiting unnecessary token movement throughout an agent’s workflow. That is why teams are starting to consider how to accomplish the same tasks with fewer tokens." ]]> tag:dlvr.it,2026-07-05:0fa627a60ac9c7dfe03c17ebc3197952 2026-07-05T13:29:19-07:00
"Tenet Security hijacked Claude Code in 85% of tests via a fake Sentry error — no stolen credentials, no alerts. Datadog and PagerDuty are equally exposed." ]]> tag:dlvr.it,2026-07-05:6c944ab7bf28a4bd1983d879bf469d06 2026-07-05T13:29:18-07:00
"This brings us to a critical architectural distinction: the difference between merely storing data artifacts and actively maintaining an ongoing understanding of work. Traditional software applications are exceptional at storing passive artifacts: a saved document file, a logged call note, or an archived email chain. But a massive collection of independent data artifacts does not equal true institutional knowledge. Without interpretation it remains mere data. Expecting a human executive to manually synthesize thousands of scattered communication artifacts into a coherent operational picture is a guaranteed recipe for immediate corporate burnout." ]]> tag:dlvr.it,2026-07-05:ae31d8a108e0e658979537f6b56da59c 2026-07-05T13:29:16-07:00
"If your program struggles, who in the room is solely focused on protecting the enterprise? Not the engagement, not the methodology, not the SI relationship. The outcome and the people attached to it. If the honest answer is no one, your register will keep telling you the program is improving, right up until the moment the program stops being recoverable. By then the dashboard will already be red, the SI will have filed the extended hypercare CR, the sponsor will be in front of the board, and the mitigation windows that existed when the register was last honest will have closed months back." ]]> tag:dlvr.it,2026-07-05:3aaedc6fe64f1b37b3f2131af6bcc99d 2026-07-05T12:21:16-07:00
""We're seeing from some of the data internally at least that it's actually not our engineers that are driving the token consumption. It's a lot of the non-engineers that are doing some of those behaviors [...] you were talking about," Accenture's agentic AI strategy lead Justice Kwak said in the meeting, according to the leaked audio. The pattern may not be limited to Accenture. Some providers, including GitHub, are reportedly shifting away from flat subscriptions and toward per-token pricing, while Uber is said to have capped workers' use of AI coding tools after previously urging staff to "use AI as much as possible." " ]]> tag:dlvr.it,2026-07-05:6fea50d8cbdf5fe771dcb55b7ebcb6b7 2026-07-05T12:21:15-07:00
"Good enterprise AI usually comes from a better feedback loop, not from more data or a bigger model. I also think that’s why so many enterprise AI projects seem to plateau around the same accuracy range. Once you’ve exhausted prompt engineering and upgraded to the latest foundation model, the next gains usually don’t come from a smarter model. They come from better supervision, better routing, better feedback, and better systems." ]]> tag:dlvr.it,2026-07-05:1c400cd4ad844c89fc9afb4b6bd07797 2026-07-05T12:21:13-07:00
Enterprise month in review - is HR tech falling short? With Meg Bear and Amy Wilson https://ift.tt/UOc6Juh Yep, it's that time again. Join Jon Reed and Brian Sommer for the Enterprise Month in Review. We'll pick our underrated stories, and then, in a classic show mashup, Amy Wilson and Meg Bear of the Meg and Amy how will give their live reactions to Brian Sommer's run-down of where HR tech, AI and leadership is falling short. We'll also have some make-your-own-buzzword surprises, and of course, Brian's infamous slide deck. As always, bring your savviest and snarkiest commentary, as you help steer the show and we field your reactions in real-time. Note: for the full slide experience, catch the YouTube replay at https://www.youtube.com/watch?v=bWtobJAsYqE via Busting the omnichannel - enterprise hacks and chats https://ift.tt/5y8BdFP July 5, 2026 at 04:58AM ]]> tag:dlvr.it,2026-07-05:423a0ef7c677a625826e1af5383f4981 2026-07-05T12:21:09-07:00
Enterprise hits and misses - AI context and real-time truth gets a debate, Ford rehires humans, and buyers weigh AI reults to date https://ift.tt/qpYNZhs This week - my real-time organizational truth piece evokes AI context debates - is there project success beyond frontier model dependence? Ford rehires human engineers - but it's complicated. OpenAI volunteers for regulation (kind of), and buyers get real on AI. via Jon Reed – diginomica https://diginomica.com June 29, 2026 at 01:42PM ]]> tag:dlvr.it,2026-06-28:468fd8b78f817e0f6980c4b9af58539e 2026-06-28T16:57:19-07:00
"Despite this, risk readiness remains low. Fewer than one-third of respondents consider their geopolitical risk management capabilities mature (Exhibit 2), and just 28 percent rate them as effective at supporting decision-making. Among the most cited capability gaps were the lack of early warning systems and insufficient geopolitical intelligence gathering. Notably, few leaders view their existing risk response capabilities as either “ineffective” or “very effective,” suggesting that few companies are positioned to anticipate shocks or seize the opportunities that may emerge during geopolitical disruptions." ]]> tag:dlvr.it,2026-06-28:591aea5f74894b024edc72dcd68ff04d 2026-06-28T16:57:19-07:00
"This matters more at scale. The largest Context Graphs Postman mapped run well exceeded anything a senior engineer could hold in their head, Asthana told me. They mapped more than 1,100 APIs at a major U.S. telecom, more than 2,600 at a global telecom, and over 11,000 at one large tech company. Postman has been running the agents on its own engineering team and has shared some data with me. APIs touch 68% of the company’s pull request traffic, Asthana tells The New Stack, and Postman runs the AI Engineer across the API-related work. The most consequential catch so far, he said, was a set of downstream dependency changes that likely would have cleared review but failed in production." ]]> tag:dlvr.it,2026-06-28:ce3c01640e25f856cf9ecfb179b97782 2026-06-28T16:57:19-07:00
"“As part of our ongoing engagement with the US government, we previewed our plans and the models’ capabilities ahead of today’s launch,” OpenAI wrote in a blog post. “At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.” Anthropic, OpenAI’s close rival, had carried out a similar release programme for its Mythos model but has now pulled the technology entirely after the US government ordered the company to stop foreign nationals from accessing public versions of the model, which has powerful cyber-hacking capabilities. Anthropic had at first delayed the widespread release of Mythos voluntarily but OpenAI appears to have done so at the federal government’s behest. Stating its disquiet with the US government approach, it said: “We don’t believe this kind of government access process should become the long-term default.” ]]> tag:dlvr.it,2026-06-28:e4bb2f00ccbf0bd60a4a77e4e432cd94 2026-06-28T16:57:18-07:00
"The CSA’s Agentic Trust Framework requires that any agent promoted to a higher autonomy level must pass five gates, including demonstrated accuracy and a security audit. The EU AI Act’s Article 50 transparency obligations take effect August 2, 2026. The May 2026 Digital Omnibus pushed high-risk system obligations to December 2027, but organizations deploying agentic SOC agents on incomplete asset data face immediate operational risk that outpaces any regulatory timeline." ]]> tag:dlvr.it,2026-06-28:323387cd9e286d3afbccfa514df2f291 2026-06-28T16:57:18-07:00
"AI value has replaced the AI sizzle reel. Winning AI use cases are driven by processes and removing friction inside of the enterprise. Think supply chain, forecasting, scheduling, maintenance, inventory and all of those backend workflows. Start with the operational pain points and deliver outcomes. Customer facing AI still matters and garners its share of attention, but the returns so far have been about the workflows behind the scenes where AI can remove cost, time, errors and friction." ]]> tag:dlvr.it,2026-06-28:1c442ab27be6988096307beffacfa3e5 2026-06-28T16:57:17-07:00
"Wall Street has drawn a remarkably clear line between winners and losers, with AI-native companies representing the future and traditional IT services firms increasingly seen as part of the past. One side now commands price-to-sales multiples of 60x to 100x, while the other is steadily drifting towards 1x as investors bet that AI will replace the labor-intensive services model that has dominated enterprise technology for the last three decades. The problem with that narrative is that it only works if enterprises can actually deploy AI at scale, and today they simply cannot. Until organizations resolve their technology, data, process, and talent debt, AI will remain trapped in pilots and proofs of concept rather than fundamentally changing how businesses operate, which means the AI balloon won’t burst because the models fail, but because enterprise adoption never catches up with the expectations already baked into today’s valuations." ]]> tag:dlvr.it,2026-06-28:2f74fa833893d0352f6d6297bb4418b8 2026-06-28T16:57:17-07:00
"For U.S.-headquartered enterprises: The regulatory line has been drawn at the computational threshold of 10 to the power of 26 floating-point operations, the statutory boundary establishing a system as a frontier model under California’s Transparency in Frontier Artificial Intelligence Act (SB 53). Developers generating more than 500 million dollars in annual gross revenue face the most intensive obligations under the Act: they must publish annual catastrophic risk frameworks and report critical safety incidents to state emergency agencies within 15 days of discovery, shortened to 24 hours if the incident poses an imminent risk of death or serious physical injury." ]]> tag:dlvr.it,2026-06-28:acfdd44f3c9af74aa7af76d30e9b7258 2026-06-28T16:57:17-07:00
"The concept is simple to state. Your orchestration layer accepts any capability format, normalizes it, and hands the model a unified tool catalog. MCP server, OpenAPI spec, GraphQL endpoint, REST API, shell script: the harness ingests all of it. The agent never knows or cares what’s executing underneath. Building it well is a serious engineering investment, and I want to be honest about that because most writing on this topic glosses over the cost. If your team is early-stage and running a handful of integrations, the wrong move is to build this now. Use whatever is fastest. But if you are building foundational enterprise infrastructure, this is not a premature optimization. Even if frontier model context windows become effectively infinite or native tool execution improves, a regulated enterprise can never allow an LLM to directly execute unaudited, non-rate-limited system calls. This abstraction layer isn’t for the model. It’s for the engineers who have to govern the platform. You pay for the abstraction either way. The question is whether you design it or inherit it. There are five concrete engineering problems the harness has to solve. Each one has a failure mode that bites in production and doesn’t announce itself clearly in a local development environment." ]]> tag:dlvr.it,2026-06-28:ec63989aff42a031cb2a769af0397e28 2026-06-28T16:57:16-07:00
"The 350 returning engineers were tasked with mentoring junior staff, rebuilding the data pipelines that feed Ford’s AI training, and refining the automated systems they were originally supposed to be replaced by. Ford also created a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases and revalidate software changes late in development." ]]> tag:dlvr.it,2026-06-28:156abe21a6db3297342f40acf25a2961 2026-06-28T15:49:35-07:00
"Beyond the technical bypasses, Pliny also leaked Fable 5’s ~120,000-character system prompt to GitHub, exposing the internal framing and safety instructions Anthropic uses to govern the model’s behavior at the base level. The incident reignites the longstanding tension between AI capability and safety containment. Anthropic’s classifier architecture routing flagged requests to a weaker fallback model rather than refusing outright was designed to reduce friction for legitimate users." ]]>