acceligence

05/13/2026

🔍 Most organizations say they value curiosity. Far fewer are actually designed to support it.

In his latest article, acceligence Executive Advisor Paul Bierbusse explores how culture, structure, training, and incentives either cultivate intellectual curiosity or slowly eliminate it over time. Paul's piece goes beyond leadership theory and gets into the operational realities that shape how organizations learn, adapt, and innovate.

One of the strongest themes: organizations don’t become more adaptive by accident. They become more adaptive by design, a deliberate choice that permeates leadership decisions, culture, and behavior.

A thoughtful read for leaders navigating transformation, change, and disruption.

🔗 Link to the article in the comments below

05/12/2026

📰 acceligence news: What endures: Michael Yadgar on five leadership principles for the AI era

acceligence Executive Advisor Michael Yadgar reflects on the leadership lessons that shaped a 32-year consulting career and why they matter even more as AI reshapes the enterprise

👉 Link to the full article in the comments.

05/07/2026

🤖 For the last two years, the market narrative around AI has largely centered on model capability - bigger models, faster models, more autonomous capabilities. But inside large enterprises, especially regulated environments, the real limiting factor is increasingly something far less glamorous:

🏗️ Institutional Capability 🏢

This CIO article explores the growing dependence organizations are developing on forward-deployed engineering teams to operationalize agentic AI - and why that dependency may become one of the defining enterprise risks of the next phase of AI adoption.

💬 As acceligence CEO Justin Greis noted in the piece:

“Ending up with a system that only the vendor can operate, extend, or even fully understand is where things start to break down.”

That observation gets to the heart of what many organizations are now discovering firsthand: AI implementation is not the same thing as AI capability.

Building sustainable enterprise AI requires far more than deploying models or launching agents. It requires organizations to rethink governance, ownership, operational accountability, decision rights, security controls, institutional knowledge transfer, and long-term maintainability.

The companies that succeed in the next phase of AI adoption will not necessarily be the ones deploying the most agents the fastest. They will be the ones building architectures, operating models, and internal capabilities that allow those systems to evolve safely and independently over time.

Excellent reporting by Evan Schuman covering an important shift happening across the enterprise AI landscape.

🔗 Link to the full article in the comments below.

05/04/2026

💡 acceligence insights: Scaling AI without breaking the economics

AI adoption is entering its next phase: not less ambition, but better economics. Leaders who pair speed with cost discipline now will be positioned to scale AI confidently, protect margins, and create lasting advantage.

🔗 Read the full article here: https://hubs.la/Q04fdfff0

04/29/2026

🍎 Apple's CEO transition is really a question about how legacy tech leaders evolve for the AI era.

Our CEO, Justin Greis, shared his perspective with Evan Schuman at Computerworld on what John Ternus inherits when he takes over from Tim Cook on September 1. Ternus is a hardware veteran and a deep company insider - someone who has been in ex*****on mode his entire career. That's a real strength, and it's part of what's kept Apple's product engine running so consistently.

The interesting question is what comes next. The AI landscape has moved quickly, and Apple has a meaningful opportunity to define what intelligent integration looks like across iPhone, Watch, Mac, and iPad - calendar, contacts, photos, location, all working together in ways only Apple's ecosystem is positioned to deliver.

That's the real prize: not just AI on a device, but AI that understands the full context of how people live, work, and play.

Few companies have earned the kind of trust Apple has across so many parts of daily life. We're optimistic about what Ternus and the team will do with that foundation.

👉 Read the full article here: https://hubs.la/Q04dRx7c0

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04/27/2026

📣 acceligence press release: acceligence Founder and CEO Justin Greis named a 2026 Top Consultant by Consulting Magazine

Chicago, Illinois - April 27, 2026 - Recognition in the Cybersecurity & Risk Technology category honors more than two decades of executive advisory work and the firm's AI-powered approach to management consulting.

🔗 Read the full press release at https://hubs.la/Q04dw2fM0

04/23/2026

🗓️ acceligence event: Join acceligence COO, David Neuman, at the Global Cyber Security Initiative Annual Conference, a high-impact forum bringing together senior executives to explore the intersection of AI, cybersecurity strategy, and enterprise risk.

🔗 Event details: https://hubs.la/Q04dfrZt0

04/16/2026

AI leaders are shifting from rapid expansion to more intentional, sustainable growth. That transition is where long-term value is built.

acceligence CIO, Yuri Goryunov, captures it well, noting that OpenAI is refocusing on core products while taking a more disciplined approach to scaling and cost management.

The next wave of value won’t come from who moves fastest, but from who scales smartest.

📰 Read the Computerworld article by Evan Schuman:

Analysts and consultants see it as taking a more conservative fiscal approach as OpenAI prepares for an imminent IPO.

04/08/2026

📣 acceligence press release: Justin Greis joins NACD Board Advisory Faculty to help boards unlock the value of AI and technology

Chicago, Illinois - April 8, 2026 - Justin Greis, CEO of acceligence, has been appointed to the Board Advisory Faculty of the National Association of Corporate Directors (NACD), dedicated to empowering directors and transforming boards to be future-ready.

🔗 Read more at https://hubs.la/Q049Z4vs0

04/01/2026

🗞️ AI headlines are getting ahead of reality again.

A recent “100x less power” claim made waves - but as acceligence CIO Yuri Goryunov pointed out, it was a rule-based system outperforming a neural model on a simple, controlled task. Impressive in isolation, but far from a real-world breakthrough.

👉 What actually matters for enterprise leaders:

🗜️ Most “breakthroughs” are narrow. They work in clean, controlled settings - not in messy, real-world environments with fragmented data and ambiguity.

💰 AI economics are still shifting. Even niche advances can drive lower costs over time as platforms and providers optimize behind the scenes.

🧘‍♂️Flexibility is critical. The landscape is moving too fast to lock into one vendor or architecture without limiting your options later.

The edge won’t come from chasing headlines. It’ll come from building AI strategies that can adapt as the ground keeps shifting.

🔗 Read the full article by Evan Schuman in Computerworld: https://hubs.la/Q0499-1k0

03/31/2026

🔥 AI is a force multiplier for whatever data it’s given.

A recent piece highlights something many leaders are starting to feel in real time: the combination of AI + poor data isn’t just inefficient, it’s risky. At scale, systems don’t question the data they’re given - they act on it.

As acceligence Partner and COO, David Neuman, put it, organizations need to be far more intentional about how they manage and prioritize data - especially identifying what truly matters to retain long-term, “such as scientific data and meteorological data.”

The takeaway isn’t political - it’s operational.

If AI is going to power more decisions, then data quality becomes a leadership issue, not just an IT problem. That means:

🛀 Treating data hygiene as a strategic priority, not a cleanup project that never quite makes the roadmap. Because once AI is layered in, small inaccuracies don’t stay small for long.

🗑️ Being explicit about what data deserves long-term investment versus what should be retired. Not all data is created equal, and pretending it is creates noise that AI will confidently act on.

🏎️ Recognizing that speed without validation is a tradeoff, not a free upgrade. AI accelerates outcomes - both good and bad.

⚖️ Asking not just whether data *CAN* be used, but whether it *SHOULD* be used. Responsible AI isn’t only about accuracy - it’s about intent, context, and the downstream impact of the decisions being made.

AI is only as smart as the data it's given.

And increasingly, the real differentiator isn’t just better models, it’s better judgment (by humans).

👉 Read the full article by Evan Schuman here: https://hubs.la/Q0492ySp0