Pam Baker
Verified
(She/Her)
As seen in:
LinkedIn,
CNN,
CNN Underscored,
MSN Ireland,
The Verge,
Ars Technica,
Cisco,
PCMag,
PCMag UK,
O'Reilly Media
and
Covers:
AI, emerging technology, IT tech, science technologies, breaking news, business and finance
Doesn't Cover:
Lifestyle and celebrity news
Freelance journalist & author, aka a workaholic. Suffering from intense curiosity in all things. Retweets not endorsements. On Bluesky @pambaker.bsky.social
Articles by Pam Baker
SPIFFE can identify your AI agents. But can it control them?
AI agents can act at machine speed, but before they go speeding along on a successful mission or to wreak unintentional havoc, enterprises must first stop them at the gate for an identity check. Organizations must ensure they know which agent is making a request. To that end, SPIFFE — the Secure Production Identity Framework for Everyone — is an open standard that assigns verifiable cryptographic identities to software workloads, including AI agents.
Project teams get new names, but the money stays put
Many enterprises speak fluent product-speak. Product teams, product owners, value streams and outcomes over outputs -- the terms roll easily off the tongue. Org charts have been redrawn, project managers have new titles, and somebody has a slide mapping the business into product lines. It all looks and sounds good, but too often it's a mirage. The old budgets, bosses and scorecards remain in place. That's where the model starts to stall, and the first clue is usually in the money trail.
The case for the disappearing data center
For decades, data centers purposefully hid in plain sight. Most people rarely noticed them, much less questioned how much power, water or land they consumed. AI has ended that quiet arrangement with a loud public pushback. Its massive new facilities are turning previously obscure infrastructure into a visible and often noisy public target and forcing the industry to confront a difficult question: Can a data center this large ever disappear?
As AI agents take action, CIOs have to govern the outcome
AI agents can follow every access rule, pass every technical check and still produce a bad business outcome. An agent might increase throughput while worsening customer satisfaction or mark a workflow complete even though the expected change never appears in the destination system. The AI can be operating as designed while the business absorbs the damage. That problem is becoming more immediate as vendors push agents deeper into live business processes.
Software contracts can lock CIOs in long before they need to leave
A vendor change can turn a stable contract into a new risk A software vendor can change substantially during the life of a contract. An acquisition, merger, bankruptcy, business model shift or sale to private equity could alter pricing, support, product priorities and availability, security practices, or the future of the platform.
When AI has permission to act, who gives it authority to decide?
Establishing decision boundaries Setting the right boundaries is more important than many tend to think. Amir Baldiga, founder of ZOOQ, runs the company solo and lets agents handle most of the work of feeding live LinkedIn data to other agents via his API. "A boundary I get wrong shows up in my own numbers inside a week," Baldiga said. But even a delay of a few days to a week in learning that a boundary is wrong can come at a price. "My reply rate slid from about 30% to 21% before I caught it.
ERP access control debt can turn old roles into current risk
How to design an authorization architecture Clarity regarding the necessary components of an authorization model is essential to satisfy all governance requirements, now and in the future. This clarity will go far in guiding the design strategy.
Negotiating AI licensing and costs: A step-by-step guide for CIOs
Step 2: Find hidden costs "Unlike predictable per-seat software licensing, AI consumption is driven by user behavior, automated background agents and data query complexity. A sudden surge in automated workflows can exhaust annual budgets in weeks," warns Kimball. Negotiating AI licensing and costs requires a keen eye on a frequently moving target.
L’effet boomerang de l’IA : quand réembaucher est plus difficile que licencier
Être licencié du jour au lendemain, pour ceux qui ont donné des années de leur vie à une entreprise, est particulièrement douloureux. Mais c’est encore pire quand l’explication tient en deux lettres : IA. L’Intelligence artificielle a fait particulièrement mal aux emplois dans l’IT. Selon Layoffs.fyi, un site qui recense les vagues de licenciements dans la tech à l’échelle mondiale, plus de 122 000 salariés de la tech ont perdu leurs emplois en 2025, et 126 000 suivent en 2026.
Home-Based GPU Networks: Viable Supplements to AI Data Centers?
A very physical problem is blowing up the AI boom. Communities overwhelmingly do not want the massive data centers required to support it. At the same time, AI’s demand for computing power continues to soar, forcing the industry to search for alternatives to ever-larger centralized campuses. One emerging idea is to distribute that computing load across thousands, or even potentially millions, of homes. But are home-based distributed data centers technically viable?
L’informatique quantique, planche de salut énergétique de l’IA ?
La multiplication des projets de datacenters dédiés à l’IA fait craindre que les réseaux électriques, déjà sous tension, saturent. Certaines constructions ont d’ailleurs été ralenties, voire abandonnées pour cette raison. Les géants de l’IT explorent différentes pistes pour tenter de gérer ce problème d’énergie. Et parmi elles, l’informatique quantique commence à connaître une certaine exposition médiatique. L’idée est de faire du compute hybride, quantique-classique.
The AI boomerang: Why rehiring is harder than letting go
After cutting staff in the hopes AI could replace their labor, many companies are now reversing course. But winning back spurned talent is its own challenge. Being laid off with no notice is a gut punch. For people who gave a company years of their professional life, it's especially painful. Then comes the worse discovery: AI has gutted the whole industry, not just their former employer, and the openings that were once plentiful have dried up and blown away. A tech career no longer seems so stable.
Can quantum take the load off AI's power problem?
Picture this: Giant AI data centers keep multiplying, pushing already strained power grids into a full-blown energy crisis. The strain on the grid forces Big Tech to slow the pace of new AI data center construction. But some quantum computing researchers are saying there is another option. They argue that the hybrid quantum-classical approach offers a narrow but real escape valve by offloading the most energy-brutal computations to processors that barely sip power.
Your AI agents won't fail. Your processes will
It's a common belief that AI agents can automate almost anything. But that unrealistic assumption can set enterprises on equally unrealistic trajectories; Gartner predicts that 40% of agentic AI projects will collapse by next year. The prevailing consensus is that the model is almost never why these projects fail.
How AI leaders can build an AI feature inventory
If shadow AI is defined as AI tools employees use without company approval, it presents only one challenge to creating a comprehensive AI feature inventory. Much of the hidden AI is embedded in enterprise software that the organization has already approved for employee use. The greatest risk is "false confidence" in the software the organization has already vetted and trusts, said Richa Taldar, staff product manager of AI Agents and MarTech at Walmart.
How AI leaders can build an AI feature inventory
How to get started Start by choosing a dynamic, rather than static, form to house the organization's inventory, making it easier to update. The organization could also automate portions of the update process, but governance should not be fully automated. Human owners must still determine materiality, validate business use and make decisions about controls, according to Jessica Block, chief AI officer at RGP, a global professional services firm.
How CIOs can tell real AI agents from 'agent washing'
Many AI products labeled as AI agents are little more than dressed-up chatbots or repackaged assistants, a practice called "agent washing." In a report published in May, Gartner warned IT leaders should be careful not to "mistake vendor positioning for true autonomy." But sorting the real from the fake is often easier said than done. Real agentic capability is expensive to build.
8 tips for a successful ERP upgrade project
ERP upgrades are a bit like major surgery on a moving patient. Nobody looks forward to it, yet delaying the operation too long creates risks of its own. For CIOs, the challenge is no longer just replacing aging systems. Modern ERP upgrades have become high-stakes operational transformations that touch finance, supply chains, customer experience, compliance, analytics and, increasingly, AI-driven decision-making.
Top change management tips for ERP implementation success
6 keys to ERP change management Here are six tips from the experts for using change management for ERP implementation success. Change management, especially for something as major as an ERP implementation, must be deliberate and carefully executed to be effective.
How software contract structure can maximize long-term value
By the time a CIO signs an enterprise software contract, the hard part feels over: The vendor is chosen, the price is set, and procurement moves on to the next deal. But the signature is where the costs actually begin and, increasingly, where they go sideways. The catch is in the contract structure rather than the initially agreed-upon costs.
How to decide which AI features are worth turning on
For the past two years, every CIO's mission has been to find and adopt AI as rapidly as possible. But now a seemingly endless stream of AI features arrives through software updates, license renewals and bundled product releases. AI assistants, copilots and agents arrive bundled into the next ERP release, switched on by a CRM update and repriced into a collaboration license that has already been signed.
Tips for successfully exiting AI vendor contracts
Deciding to cut an underperforming AI tool is the easy part. Getting out of the contract can be painful for CIOs. Vendors made onboarding easy with generous trials, quick setup and AI included at every level. However, these same vendors often make leaving costly and slow.
Why ERP initiatives struggle to deliver business value
According to a 2024 Gartner prediction, more than 70% of recent ERP initiatives will fail to fully meet their original business case goals by 2027, and some will fail outright. While comprehensive data on ERP outcomes is limited, there's little doubt that many organizations struggle to translate successful ERP deployments into the business value used to justify them.
Time for an AI exit strategy: How CIOs are cutting AI waste
Enterprises spent two years shoving AI pilots into production. Now the bill is coming due, and for many CIOs it doesn't add up. Poorly governed copilots, redundant SaaS AI features bolted onto every tool in the stack, half-secured chatbots and automations that barely move the needle are quietly inflating cloud, licensing and labor costs well past budget. The ROI that was supposed to justify all of it? Largely missing in action. For a growing number of IT leaders, it's past time to cull.
Hybrid architecture as a permanent enterprise model
In the cloud-first age, hybrid cloud was never meant to be permanent. It was intended as a temporary step on a controlled march to the cloud. It was designed as a practical solution for organized cloud migrations and as a reassuring step for companies that were hesitant to abandon their data centers entirely.
SlackやSalesforceで「名ばかりの担当者」が増殖？ 説明責任が消滅する理由 ： 気付いたら責任者がいない状態に
ハイブリッドワークは従業員の勤務場所で定義するものではない。どのように業務をこなし、誰が責任を持ち、問題が発生したときにどう対処するかを決める運用モデルだ。企業はこのうち最初の観点については慎重に判断するが、残りの2つは放置しがちだ。その結果、日々の業務やプロジェクトの成否に対する説明責任が曖昧なままになっている。 ログ分析システムベンダーSplunkでAI（人工知能）技術の元プロダクトマネジャーであるサヤリ・パティル氏は、次のように指摘する。「オフィス内外のメンバーが混在する分散型の企業で最も目立つのは、担当の曖昧さだ。これは経営層が割り当てた仕事と、システム上の記録の間にあるずれが原因で生じる」 この状況の放置は危険だが、そうなってしまう背景は理解できる。システムはあくまで、「ある処理が終わったら次の工程にタスクを送る」といった作業の手順を機械的にリレーするために設計されているだけで、その業務の最終的な結果に責任を持つ「真のオーナー」を指名する仕組みにはなっていないからだ。...
The invisible labor crisis inside IT: AI work the org chart can't see
The loudest conversations about AI and jobs focus on what disappears. Inside IT, the more immediate problem is what quietly multiplies. As AI capabilities spread across the stack, they splinter work into new, poorly defined skill demands: prompt engineering here, orchestration there and model evaluation somewhere in between. None of it aligns neatly with existing roles, reporting lines, or hiring frameworks. The result is an accumulation of invisible labor.
Who owns the outcome? Accountability in the hybrid enterprise
Hybrid work is not defined by where employees sit. It is an operating model decision about how work gets done, who owns it and what happens when it breaks. Most organizations make the first call deliberately; many never make the other two. As a result, accountability for success -- or failure -- is still a mystery at many companies. "The most common breakdown I see in distributed engineering organizations isn't missing ownership; it's ambiguous ownership. Two people think they own something.
Nobody told legal about your RAG pipeline -- why that's a problem
Every regulated enterprise running an AI system is sitting on a discovery liability it can't see. Retrieval-augmented generation, commonly referred to as RAG, is the architecture that lets large language models (LLMs) pull from internal document repositories before generating a response. Yet legal teams are rarely aware of the liabilities that lurk there. How did RAG become such a universal blind spot?
The hidden high cost of training AI on AI
Today's AI models are falling victim to a dangerous vulnerability: data poisoning. But the data poisoning crisis isn't caused solely -- or even mostly -- by hackers or adversaries. It's self-inflicted. As enterprises race to deploy AI across workflows, they are quietly and quickly flooding their internal databases with AI-generated summaries, emails, code and reports.
How AI in enterprise software redefines work quality
For decades, enterprises measured work quality by outputs they could trace, review and attribute. Documents were drafted and reviewed, decisions were recorded, and processes were followed by identifiable people accountable for the result. AI embedded in enterprise software is dismantling that model faster than governance frameworks can respond. The outputs are arriving cleaner, faster and in higher volume.
Where collaboration overload signals burnout risk
Collaboration has quietly crossed a threshold from a productivity driver into a systemic liability. In today's enterprise, the issue is not a lack of teamwork; it is the relentless, unbroken demand for it. Meetings stack onto messages, messages trigger more meetings and work fragments into an endless loop of coordination.
Your AI vendor is now a single point of failure
Traditional vendor lock-in was manageable, if not ideal. Today's AI model dependency presents a different challenge, and yet most companies treat AI vendor lock-in as if it were business as usual. This is a mistake. While nothing about AI is business as usual, model centralization is even less so. It's a critical business risk, but one that is largely unacknowledged, so mitigation measures typically don't exist.
Collaboration-Plattformen als Kostenfaktor kontrollieren
„Collaboration-Plattformen beeinflussen direkt, wie Menschen ihre Zeit verbringen, was in jedem Unternehmen den größten Kostenfaktor darstellt. Zudem stehen sie im Zentrum von Lizenzierung, Sicherheit und Zugriff, sodass dort getroffene Entscheidungen die Kosten sehr schnell in die Höhe oder Tiefe treiben können.
How techno-nationalism is complicating IT resilience and supply chains for CIOs
The growth of techno-nationalism, meaning the use of technology policy to advance national economic and security interests, has had a series of knock-on effects for the CIO. Especially for CIOs orchestrating enterprise IT on a global scale, techno-nationalism impacts more than just architectural decision-making; it also creates operational challenges for IT organizations, particularly in supply chains, resilience planning, and regulatory compliance.
Tech nationalism is reshaping CIO infrastructure strategy
The use of technology policy to advance national economic and security interests, also known as tech nationalism, is no longer a distant geopolitical trend. It is becoming a direct operating constraint for CIOs -- and it's far more involved than many CIOs realize. Vendors aren’t much help in clearing the confusion, as they tend to blur three key concepts that CIOs need to separate, said Collin Hogue-Spears of Black Duck -- data residency, data localization and data sovereignty.
The CIO's new mandate: Redesign work itself
Back when CIOs were first pushing for a center seat in the C-suite, the division of labor looked different. At that time, the responsibility of reshaping the organization's design wasn't part of the CIO mandate. Now it is. The shift came right after AI catapulted IT ahead -- and just before IT broke down. "The reality is that IT no longer happens in the IT department," said Frederik Hagstoem, CTO at Emergn, a global technology consultancy.
情シスは“PC管理部門”から“労働コスト管理部門”へ 新たな役割とその中身は：労働コストを情シスが管理できる時代に
企業のITコストの構造が変化している。かつてIT投資の中心は、サーバやネットワーク機器といったインフラだった。しかし近年は、SaaSや人工知能（AI）ツール、コミュニケーションツールなど、従業員が日常業務で利用するソフトウェア環境がコストの大きな割合を占めるようになっている。 この変化は、情報システム部門（以下、情シス）の役割にも影響を与えている。 ITコストの変化と情シスの役割 SaaSやAIツールの普及によって、従業員のPC環境は“見えにくいコスト”が生まれやすい場所になっている。例えば、次のような要素が生産性の低下やコスト増加につながる。 ツール間の切り替えによる待機時間 機能が重複するツールの併用 システム連携が不十分なことによる手動データ移行 認証やアクセス処理の待機時間 ツール操作の複雑さによる業務遅延 こうした要素は、財務データには表れにくい。しかし実際には、従業員の作業時間を消費し、企業の労働コストを押し上げる要因になっている。...
How end-user computing is becoming a cost-control system
End-user computing (EUC) is no longer just the desktop support layer in IT's tech stack. It has quietly evolved into the execution layer where cost signals become measurable. Today, it governs how labor is allocated, how work flows through the enterprise and where operational costs are incurred. For CIOs and CFOs alike, EUC is increasingly viewed not as an overhead function, but as a cost-control platform shaping the economics of modern work. But that change in perspective did not happen overnight.
How collaboration platforms are becoming cost-control systems
Collaboration platforms are rapidly becoming consequential cost centers. That can be surprising, considering how inexpensive messaging or meetings seem in isolation. But the combined costs in these systems increasingly shape financial decisions that affect labor, time and software spend. The financial implications can be hard to ignore. "Collaboration platforms directly shape how people spend time, which is the biggest cost in any organization.
Why CIOs abandon traditional service management
Rigid IT frameworks used to be IT's best way to restore order to chaos. But the traditional ticket-based, request-approval-wait approach doesn't work anymore. Business demands have grown beyond the bounds of ITIL, leaving IT scrambling to catch up. Unfortunately, catching up is neither sufficient nor desirable. Tickets introduce "wait states," said Ryan Scott, technical editor and developer at consulting firm School Aid Specialists.
Why CX platforms are becoming cost-cutting tools
CX platforms traditionally aim to improve the customer experience, but "the biggest inefficiency is the people who are using them to 'help' customers," said Anastasia Vladychynska, international customer experience consultant, MBA professor and speaker. The prevailing thinking is not about helping customers, but about closing the task, she said.
How HR platforms reshape enterprise cost structures
As data fragmentation continues to obscure labor costs, HR platforms can no longer serve just as systems of record. Now they are evolving into layers of labor-cost intelligence. By Pam Baker Published: 26 Feb 2026 Labor costs remain stubbornly opaque in data-rich enterprises due to persistent data fragmentation, complexity and governance breakdown. In other words, too much data remains trapped in siloes, making it difficult or impossible for modern HR systems to access.
How ERP and supply chain platforms protect profits
Working capital, forecast accuracy and supplier exposure are governed as much by ERP and supply chain design as finance policy, making supply chain platforms key to margin control. By Pam Baker Published: 25 Feb 2026 ERP and supply chain platforms are traditionally used to help control cyclical volatility.
How CIOs can fix AI's broken learning loop
Given how many AI projects fail between pilot and production, it seems there must be a common flaw in the rollout process. To resolve this, CIOs are looking to redesign how organizations test, fail, and operationalize technology. Those are not easy tasks, but it helps to understand which part of the system broke first. "Quarterly reporting and rigid KPIs assume progress is linear and outcomes happen at a defined point in time.
Caught between state, federal laws
The burdens of AI compliance, risk management and governance have grown overnight for CIOs, who must wrestle with legal mandates that shift faster than operational guidance. The federal vs. state tug-of-war over conflicting AI laws is widening the gap between what organizations are expected to deploy and what they can confidently defend. The risk is immediate: what ships today may become tomorrow's liability.
4 supply chain trends for COOs in 2026
The trend of nearshoring will remain a major topic for COOs and other supply chain executives in 2026. Learn other trends to be aware of this year. By Pam Baker Published: 28 Jan 2026 Volatility is nothing new for executives who work on supply chains, and 2026 trends for the supply chain will attempt to relieve these pressures for companies and result in AI being further embedded in day-to-day supply chain activities.
2026 CIO trend: From seat at the table to the AI hot seat
CIOs spent years fighting for influence in the C-suite. AI secured CIOs a seat at the table and then flipped it to a hot seat. CIOs are increasingly held accountable for AI infrastructure and risk, even as business units choose the use cases. "That's a recipe for getting burned," said Vitaly Golomb, managing partner at boutique investment bank and advisory firm Mavka Capital, and the author of "Accelerated Startup." Getting burned became the trend. How did it happen?
13 unexpected, under-the-radar predictions for 2026
Surprise! Surprise! Many of these may soon rock and shock your world. Pam Baker, Contributing Writer Beyond the headlines, a quieter set of forces is at work in the background, already reshaping power, risk and economics. In the coming year, low-grade "work-slop" will quietly flood organizations, cybercrime will professionalize into multinational franchises, quantum computing will jump from lab pilots to policy mandates, and machine identities will outnumber humans by orders of magnitude.
Europe’s AI Challenge Runs Deeper Than Regulation
Artificial Intelligence & Machine Learning , General Data Protection Regulation (GDPR) , Next-Generation Technologies & Secure Development Europe Faces Barriers No Legal Rewrite Can Fix Pam Baker • December 19, 2025 Image: Shutterstock An assumption propelling a proposed recalibration of European tech regulation is that red tape is holding back the trading bloc from gaining ground in a global race for artificial intelligence dominated by the United States and China.
Show More
loading
Actions
Get in touch with Pam
Contact Pam, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack