tag:dlvr.it,2018-03-23:ac6861e9e2d249fc92b2895b5ff7a2e0 2026-07-19T17:37:12-07:00 tag:dlvr.it,2026-07-19:da588725010cc63cb3767f12c5f7d0bc 2026-07-19T17:37:12-07:00
"While that is a trend worth monitoring, leaders we speak with are committed to hiring and apprenticing the next generation of experts, even if their numbers are lower than before. And some organizations are holding their numbers steady. Bank of America, for instance, is bringing in nearly 4,000 summer interns and full-time campus recruits in 2026—matching last year’s hiring from a talent pool spanning more than 500 schools—while explicitly redesigning those roles around AI from day one. Its chief people officer calls the approach “intentional and long term.” ]]> tag:dlvr.it,2026-07-19:23d9f9d33c606583ea8236e0171c94e8 2026-07-19T17:37:11-07:00
"The context graph first. Building and maintaining a compiled representation of org knowledge isn’t a one-time task. Schemas change. Systems get renamed. Personnel and processes shift. Teams that do this well treat the context graph as a product with an owner, an update cadence, and health checks, rather than a setup step that runs once at deploy time. Observability second. Model-agnostic proxy layers are becoming standard: a single layer capturing full traces on every LLM call regardless of provider, enforcing per-tenant cost and rate limits, allowing model swaps without rearchitecting. Tracing for agents means capturing reasoning, not just requests: what the agent considered, which tool it selected and why, what came back, and what it did with the result. Without that, debugging is archaeology." ]]> tag:dlvr.it,2026-07-19:afa0e69cb5e64843d9ccd27fb79fc6e2 2026-07-19T17:37:11-07:00
"Tech spending is separating from economic caution. Invest in data readiness, security, governance, and infrastructure. Public frontier models alone no longer differentiate. Context, privileged data, and homegrown models win. At 74% adoption, agentic AI is now an authority question. Agents need permissions, cost controls, monitoring, and a way to undo mistakes. Enterprise AI needs balance between "brains" (CPUs) and "brawn" (GPUs), plus storage, edge compute, and observability. Talent is the hardest constraint. Experienced people who can navigate ambiguity and apply judgment matter most."]]> tag:dlvr.it,2026-07-19:5ac6e2a9ef2ae12b5207f694f25d2993 2026-07-19T17:37:11-07:00
"For the past two years, the industry optimized around yesterday’s scarce resource: raw intelligence. Capital flowed into larger clusters, bigger training runs, and the belief that the smartest model would inevitably capture the market. But tomorrow’s scarce resource isn’t intelligence. It is context, integration, and execution. For frontier labs, the strategic question is no longer simply how to build the next breakthrough. It is how to own the assets that become more valuable as intelligence becomes cheaper. For enterprises, the lesson is even simpler: stop optimizing your strategy around the model. The model is a melting ice cube. Optimize instead for a decoupled architecture, real-world workflows, and the proprietary data loops that the model illuminates." ]]> tag:dlvr.it,2026-07-19:7a96a0c891c931b5115992fa00867147 2026-07-19T16:43:17-07:00
"Some implications: Price/performance is becoming decisive. Once quality is “close enough,” the lowest-cost frontier-adjacent models take share quickly." ]]> tag:dlvr.it,2026-07-19:b413ef0b8303f5eeed937b300fe06973 2026-07-19T16:43:17-07:00
"So, as you play with shiny objects and talk about the promise of AI, remember, that your current architectures were compromised by the limitations of technologies fifty years ago. As you redefine the promise of planning with new technologies, start with first principles, redefine your relationship with data (to use all forms of data), and put some discipline around the use of lead times. Your supply chain will thank-you. And if you want to calculate the impact of your current supply chain entropy, in the appendix, I share some formulas. To monetize the affect of entropy on your supply chain, push the insights into your network design technologies to run simulations to understand the impact of entropy on cost and customer service. Then call your CFO and COO to have a different discussion. " ]]> tag:dlvr.it,2026-07-19:66e3001053da8be0c79f8a4e9701d1d7 2026-07-19T16:43:17-07:00
"In a new book, Chilling Effects: Repression, Conformity, and Power in the Digital Age, Jon Penney explains how surveillance, technology and power can be weaponized to influence behavior at scale. Surveillance, personalization, uncertainty and authority are all key mechanisms to increase the scale and impact of chilling effects. They cause people to self-censor their words and actions, to become more conformist and compliant and thus easier to manage and control. And the effects are additive: the more mechanisms employed, and the more powerful the form, the greater the chill." ]]> tag:dlvr.it,2026-07-19:eddea4a584859313844e1f59627e8946 2026-07-19T16:43:17-07:00
Workday Elevate - what kind of AI do you want? Customers share AI success tactics - and how employees bought in https://ift.tt/O06lK5E One big thing has been missing from the keynote stage this year - customers going in-depth on AI success, including the rough edges of change. Workday Elevate was different. Here's how Pfizer, Visa, and Cushman & Wakefield notched early agentic AI wins, and turned employees from naysayers to advocates. via Jon Reed – diginomica https://diginomica.com July 8, 2026 at 08:50AM ]]> tag:dlvr.it,2026-07-19:77b9ae6a3921977d7166f2506b92bd54 2026-07-19T13:33:41-07:00
"We know that enterprise data quality is a problem - and the biggest obstacle to successful AI. But the truth can quickly become a static phrase. What does the data really show? What are the savvy companies doing? How deep does this organizational problem go? Recently, a monster piece of independent research, co-authored by diginomica's Alyx Macqueen and Maureen Blandford for Serendipitus, investigated this problem, and surfaced surprising insights. So let's get Blandford and Macqueen in the proverbial hot seat, and find out about their method, what they learned, and the issues that this research sparked. As always, this is a live, interactive event where your questions and commentary are art of the mix. Maureen and Alyx have prepared their top surprises and fresh views for this program, you won't want to miss this one." ]]> tag:dlvr.it,2026-07-19:98943979d833a303616cbc358e6d0fb2 2026-07-19T13:33:41-07:00
"Enterprise buyers are currently being bombarded with vendor pitches promising that agentic AI will magically solve their customer experience woes. If you are a buyer and concerned about ethical AI use, here is your survival guide to avoid making an expensive, possibly brand-damaging mistake: Prioritize Architectural Integrity Over Hype: Do not be seduced by an agent's ability to generate natural-sounding excuses. Demand to see the integration map. If the agent cannot access your back-office CRM and ERP data securely and deterministically, it is not an agent; it is a glorified chatbot with a larger vocabulary." ]]> tag:dlvr.it,2026-07-19:0a74cf53c38a6256a4f927b6802def02 2026-07-19T13:33:41-07:00
Enterprise hits and misses - fun times with GEO and AEO, brands bring AI in-house, and the (expensive) problem of agentic loops https://ift.tt/WDs3SGt This week - AEO and GEO are here, but what road should brands take? Tokenomics are a thing, but are agentic loops the real cost problem? Let's debate. More enterprises like Cisco are bringing AI infrastructure in-house, and Meta tries to single-handedly claim the whiffs section. via Jon Reed – diginomica https://diginomica.com July 13, 2026 at 01:47PM ]]> tag:dlvr.it,2026-07-12:527196f08477cf1d3ee2ce7974b5dd3c 2026-07-12T18:00:50-07:00
"The next wave of value from AI will flow to those leaders who redesign business models, eliminate friction, and build organizations that learn faster than the competition. " ]]> tag:dlvr.it,2026-07-12:1db0775d450e82564faa80ee8eeeae15 2026-07-12T18:00:50-07:00
"MCP doesn’t replace APIs. It creates a standardized layer through which AI agents can access the context they need from multiple tools and vendors. In incident management, cross-tool access is crucial. Responders need a connected view across alerts, changes, communications, service ownership, and customer impact. “MCP doesn’t replace APIs. It creates a standardized layer through which AI agents can access the context they need from multiple tools and vendors.” ]]> tag:dlvr.it,2026-07-12:80234db3beaf35467749a399fecfa55c 2026-07-12T14:53:51-07:00
"In almost every enterprise AI conversation right now, someone eventually says the same thing: “Tokens are basically free.” I understand why people say it. If the expensive part of building with AI was inference, then cheaper tokens should unlock everything. But that assumption hides a bigger problem. The real cost of enterprise AI was never just the tokens. It was the messy human work required to turn a probabilistic answer into something a business is willing to bet on. I’ve started seeing the same pattern play out in enterprise agent deployments. A team lets an advanced coding agent loose on a sprawling, decades-old monolithic service, targeting a move to a modern microservices architecture. The agent spends hours autonomously iterating on a large pull request, running local test suites, and fixing its own syntax errors. When the run finishes, the compute bill is often the smallest part of the exercise." ]]> tag:dlvr.it,2026-07-12:886643007c1fe99cb7c5922cbabae3e5 2026-07-12T14:53:51-07:00
"And now for something completely different! Let's have a live look at Analyst Relations disruptions - via a feedback session on AI-generated content on how AR needs to change. I generated this content from my own research and analysis - but did AI get it right? What went wrong? How can all of us - including analysts - serve customers better when they need an open, year round dialogue like never before? Joining me will be independent analysts Bonnie Tinder an d Josh Greenbaum. I'll get their unscripted reactions to this collateral, some of which is quite edgy, but does it reflect our views? Where did AI go wrong, and where can data and analytics help us make changes in our industry? Join us and let's hear your reactions also." ]]> tag:dlvr.it,2026-07-12:2f2abe341403e0e1adaadbe6582bf08f 2026-07-12T14:53:51-07:00
Enterprises are managing costs and that means model routing. The rise of good enough models has started to commoditize LLMs. The economics of LLMs aren't going to hold up just as Anthropic and OpenAI chase IPOs and valuations topping $1 trillion. Something will give, but it's a question of when. ]]> tag:dlvr.it,2026-07-12:4636fcfb45f4d921f345b4a1677988ea 2026-07-12T14:02:14-07:00
"Cisco’s system automatically chooses the right AI model for each task. “It knows which tool is most effective and most efficient,” Patterson said. Much of the AI’s infrastructure is built on-site, giving Cisco more say over costs. “We feel like that’s the most efficient way is to build our own AI stacks, which will go out and query the different models based on the particular use case,” Patterson said." ]]> tag:dlvr.it,2026-07-12:f7efe9bc5a09c47e414e7b01191c0e9a 2026-07-12T14:02:14-07:00
"New VentureBeat survey data: 69% of enterprises share AI agent credentials, letting one compromised agent inherit the access of every workflow it touches." ]]> tag:dlvr.it,2026-07-12:44857450ab47a9fafc61307c3bddcf0d 2026-07-12T14:02:13-07:00
Mind the maintenance gap! Christian Pedersen of IFS on how redefining innovation can change industries - and reduce energy consumption https://ift.tt/jdkAygw Too often, innovation is about chasing shiny new toys. But Christian Pedersen has a different take: he starts by uncovering customer pain points - and overlooked inefficiencies. With the IFS Zero news on deck, I talked with Pedersen about why we need to think differently about industrial change, AI, and energy consumption. via Jon Reed – diginomica https://diginomica.com July 1, 2026 at 01:38AM ]]> tag:dlvr.it,2026-07-12:825c87fdca601588ce624dc5e5b9434f 2026-07-12T12:53:32-07:00
"When Europe 2031 was published in June, it set off an immediate debate in Brussels and beyond. The fictional near-future scenario authored by a group of AI researchers argues that Europe risks economic and political irrelevance if it fails to compete at the frontier of AI development. Within days of its release, the United States government ordered Anthropic to restrict access to its most advanced AI models for non-US users—an event the report had projected for 2029, underscoring the potential threat. In this episode, I’m joined by Europe 2031 co-author Maximilian Negele and AI Now Institute advisor Frederike Kaltheuner, an author of a three-part series for Tech Policy Press that argues Europe's dependency problem runs deeper than frontier model access, to debate what Europe is actually getting wrong, and what it would take to change course." ]]>