Seventeen card-carrying union members who ran for Congress won. But can they nudge their party in a more labor-friendly direction?

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The AFL-CIO’s Largest-Ever Midterm Program Turned Out Working-Class Voters in Key Elections

Two conversations about workers coming together: one on what AFL-CIO Secretary-Treasurer Fred Redmond says organized labor proved in Tuesday’s election and one on hospice nurses who say they won a voice by organizing. On First Friday with Fred, the monthly segment held on the first Friday of each month, Redmond says the enthusiasm of organized labor was the highest he has seen and that working people across party lines asked for a different direction. He discusses Sherrod Brown’s return to the Senate, union candidates up and down the ballot, labor’s boots-on-the-ground advantage and an election protection effort that he says fell short of its 50,000 goal but put more than 30,000 people in the field. He also says the AFL-CIO executive council was setting its lame duck priorities, including a major concern about Affordable Care Act healthcare cuts. Then, Allina Health hospice nurse and case manager Susie Smerz discusses the first contract won by hospice nurses with SEIU Healthcare Minnesota and Iowa. She says the effort was never about wages and that the contract includes protections around weekend work, pay for time over FTE and a new mileage reimbursement setting, though not holiday pay. She describes how nurses who work alone across the Twin Cities area organized, a unanimous ratification vote and the union’s work with the Attorney General’s office on a community benefits agreement tied to the Sutter Health acquisition. Visit aflcio.org for more from the AFL-CIO and seiuhealthcaremn.org for more from SEIU Healthcare Minnesota and Iowa.

Two conversations about workers coming together: one on what AFL-CIO Secretary-Treasurer Fred Redmond says organized labor proved in Tuesday’s election and one on hospice nurses who say they won a voice by organizing. On First Friday with Fred, the monthly segment held on the first Friday of each month, Redmond says the enthusiasm of organized labor was the highest he has seen and that working people across party lines asked for a different direction. He discusses Sherrod Brown’s return to the Senate, union candidates up and down the ballot, labor’s boots-on-the-ground advantage and an election protection effort that he says fell short of its 50,000 goal but put more than 30,000 people in the field. He also says the AFL-CIO executive council was setting its lame duck priorities, including a major concern about Affordable Care Act healthcare cuts. Then, Allina Health hospice nurse and case manager Susie Smerz discusses the first contract won by hospice nurses with SEIU Healthcare Minnesota and Iowa. She says the effort was never about wages and that the contract includes protections around weekend work, pay for time over FTE and a new mileage reimbursement setting, though not holiday pay. She describes how nurses who work alone across the Twin Cities area organized, a unanimous ratification vote and the union’s work with the Attorney General’s office on a community benefits agreement tied to the Sutter Health acquisition. Visit aflcio.org for more from the AFL-CIO and seiuhealthcaremn.org for more from SEIU Healthcare Minnesota and Iowa.

Union Plus benefits offered by the AFL-CIO's Union Privilege provides consumer savings, discounts, benefits, and education resources to active and retired labor union members.

The AFL-CIO’s Largest-Ever Midterm Program Turned Out Working-Class Voters in Key Elections

The AFL-CIO’s Largest-Ever Midterm Program Turned Out Working-Class Voters in Key Elections

CData's event on enterprise AI. Join us November 5: how an AI gateway grounds agents in your systems, governs every step, and improves with every interaction.

Join us on Election Day to text-bank union members during the last few hours of voting! As you know, this is a critical election year for our state with ramifications for NC workers and the whole coun...

As a leader, you’ve probably spent years nudging your managers to create psychological safety. After all, organizations benefit greatly when it feels safe to challenge the boss, surface bad news early, and say the uncomfortable thing before it becomes the expensive thing. The Space Shuttle Challenger disaster of 1986 has become the canonical warning. Engineers raised concerns about the O-rings used in the solid rocket booster before launch. Their warnings were overruled by managers under schedule pressure. Seventy-three seconds after liftoff, seven astronauts were dead. The lesson still holds: When people are afraid to speak or are pressured to comply, organizations miss what they already know. AI, however, is introducing a complication most leaders haven’t accounted for. Your employees now have another place to speak, and emerging research suggests they may be more willing to challenge a machine than a human leader, while another body of research challenges our assumptions about psychological safety itself. Put those together, and you’re no longer managing psychological safety only between people. You’re managing it across people and machines, and the two may tell you very different things about your organization’s health. ## Safety needs accountability Psychological safety became popular for good reason: Teams perform better when people can admit mistakes, question assumptions, and challenge authority without fear of it following them into a performance review. But here’s the misconception: Psychological safety and accountability sit at opposite ends of the same dial. Turn safety up, the thinking goes, and standards come down. They don’t. The strongest teams have both; people feel safe enough to challenge one another and accountable enough to deliver. Researchers studying five workplace settings, from hospital nurses to knowledge workers to four years of sales data across 229 retail stores, found that the relationship between psychological safety and performance wasn’t simply linear. At high levels of psychological safety without matching accountability, performance on routine work could decline; collective accountability substantially buffered that decline. The problem wasn’t that people felt too safe; it was safety operating without another ingredient the team needed. A newer conceptual paper helps explain why. Psychological safety lowers the interpersonal risk of speaking up, which is what you want, but safety alone doesn’t create the discipline that keeps a team sharp: preparing for meetings, thinking carefully before weighing in, and taking shared norms seriously. That discipline comes from accountability. Aviation offers a useful analogy. Sterile cockpit rules eliminate nonessential conversation during critical phases of flight, not safety-critical challenge. If something is wrong, you still speak. What changes isn’t how much psychological safety is allowed. It’s how tightly decision rights, operating discipline, and accountability are enforced. Your AI pilots and early experiments need maximum candor. A junior engineer questioning a model or vendor claim should have every opportunity to be heard. Once a decision moves into production, incident response, or customer-facing execution, accountability tightens. Psychological safety doesn’t go down; accountability goes up. ## Culture won’t automatically make people trust AI Here is another set of assumptions in this area for IT leaders to challenge: that inclusive leadership, meaning leaders who stay approachable and treat employees’ input as worth acting on, builds psychological safety, that psychological safety makes people comfortable with AI, and that comfort with AI drives adoption. The evidence is messier. One recent study found that inclusive leadership did improve psychological safety, but psychological safety itself did not significantly predict trust in AI. What mattered more was an individual’s ability to tolerate uncertainty. That matters because AI adoption is an uncertainty event: Capabilities shift every few months, outputs can be confidently right and impressively wrong, and the impact on jobs, workflows, and careers remains unclear. Employees are being asked to exercise judgment around systems whose behavior is probabilistic, not deterministic. Another study of employees in South Korea found that AI adoption was associated with lower psychological safety, which in turn was associated with greater employee depression. Ethical leadership, meaning leaders who are transparent and fair about what’s changing, softened that relationship but didn’t eliminate it. Neither study is universal proof — different countries, samples, and methodologies matter — but together they raise an important question for CIOs. A healthy culture may help people absorb the disruption AI creates without necessarily making them trust the technology itself. Those are two different problems. One is organizational: Do employees believe they can question what is happening around them? The other is psychological and technical: Can they work productively when the tool is probabilistic, and do they know when to trust it, challenge it, verify it, or ignore it? That requires more than another culture initiative. It requires AI literacy, judgment, and comfort operating under uncertainty. ## Your people may be honest. Just not with you. A 2026 series of scenario-based experiments with participants in China found that employees were more willing to voice concerns to an algorithmic leader than a human leader during cognitive tasks. Perceived fairness and psychological safety helped explain the difference. That doesn’t mean employees want robots for bosses — the effect didn’t hold in the same way for emotionally charged situations — but for analytical work the finding is worth paying attention to. One possible explanation feels remarkably human: An algorithm can’t embarrass you at the next staff meeting, downgrade your reputation with the executive team, or remember that you challenged its favorite project six months ago. Now think about what happens as generative and agentic AI become embedded in everyday work. Somewhere in your company, an engineer may tell an AI assistant that the proposed architecture is flawed, explain why, work through three alternatives with the model, then walk into a meeting an hour later and nod along while a senior leader advocates for the original design. The concern existed. The organization just never received it. AI didn’t suppress the dissent; your management system did. You’ve created a psychologically safe chatbot sitting inside a psychologically unsafe org chart, and your AI adoption dashboard will never show you the difference. Both employees look like successful AI users, one using AI to accelerate work, the other using it as the only place they feel comfortable saying what they really think. Usage looks identical. Organizational health doesn’t. We spend a lot of time asking whether employees trust AI. We may need to spend just as much time asking whether they trust people enough to repeat what they already told it. ## What only you can fix First, keep psychological safety constant and let accountability flex by phase. Push for aggressive challenge during strategy sessions, architecture discussions, AI pilots, and early experiments, and make disagreement cheap before decisions get expensive. Once execution starts, tighten decision rights and expectations without closing the channel for legitimate safety, ethical, or operational concerns. Second, stop measuring psychological safety only through sentiment. Ask whether dissent changes decisions: what major initiative was redesigned because someone challenged an assumption; what AI pilot was narrowed, delayed, or killed because someone surfaced a risk early; what executive decision changed because somebody several layers down challenged the prevailing view. If nobody can remember one, another engagement survey isn’t going to tell you much. Third, build a path for concerns raised to AI to reach the human decision system. That doesn’t mean reading employees’ private AI conversations; doing that would create exactly the surveillance problem that destroys candor. Instead, design mechanisms that let employees elevate a concern without reconstructing it from scratch: an anonymized escalation channel, a prompt-to-retrospective workflow, a mechanism to flag questionable AI outputs, or a structured path from an AI conversation into an architecture or investment review. The design question is simple: Can an employee move a concern discovered in an AI conversation into the organization’s decision process with one deliberate action? Increasingly, that’s where the concern may show up first. None of this argues against psychological safety. It argues against assuming psychological safety works in isolation, or that human trust and AI trust are the same problem. AI is creating a second conversation inside your organization, one that happens in meetings, reviews, and Teams calls, and one that happens quietly between an employee and a machine. The real question isn’t whether your employees trust the AI. It’s whether AI has quietly become the place where they say the things your organization most needs to hear. **_Second Opinion_** _offers technology leaders an evidence-based perspective on their most consequential decisions. Khalid Kark brings 25 years as a trusted advisor, researcher, and operator. In an enterprise technology market crowded with noise, this column separates signal from spectacle._

The most expensive quantum mistake a CIO could make today may not be investing too early. It may be investing in the wrong capabilities, for the wrong reasons, without understanding the architectural and economic consequences. Over decades of leading enterprise technology, architecture, modernization, and innovation in complex, highly regulated environments, I have seen a recurring pattern: technological enthusiasm advances faster than institutional readiness. Capital gets committed. Platforms become entrenched. Dependencies multiply. Business value remains difficult to demonstrate. Quantum computing risks repeating that pattern, but with an important distinction: The cybersecurity consequences demand action today, while the timing and economics of broader commercial quantum advantage remain uncertain. That is the strategic tension CIOs must resolve. Quantum readiness and preparedness is not a race to acquire qubits. It is a discipline of protecting enterprise trust, preserving strategic options and directing scarce capital toward demonstrable value. The fundamental question is no longer whether quantum computing deserves executive attention. It is whether CIOs and enterprises can distinguish what must be protected now, what deserves experimentation and what should remain an option rather than a financial commitment. ## Two quantum clocks. One enterprise balance sheet Quantum presents two fundamentally different timelines to a CIO. The first is the security clock. In August 2024, the National Institute of Standards and Technology (NIST) published its first three finalized post-quantum cryptography standards, establishing practical mechanisms for protecting information against anticipated quantum-enabled threats. This is critical because cryptography is not an isolated cybersecurity component. It underpins identities, financial transactions, digital signatures, software integrity, communications, connected infrastructure and trusted business relationships. A sufficiently capable future quantum computer could compromise certain widely deployed public-key cryptographic methods. The October 2026 U.S. Government Accountability Office (GAO) report, Quantum Computing: Federal Actions Needed to Prepare for Emerging Cyber Threat, reinforces the urgency. None of the 24 federal agencies examined had fully addressed all three assessed preparatory practices for migrating vulnerable cryptography. The lesson extends beyond government: you cannot migrate cryptographic dependencies you have not discovered. The second is the commercial advantage clock. Quantum computing may eventually offer meaningful advantages in selected areas of chemistry, materials science, simulation and optimization. But promising research does not automatically translate into repeatable enterprise economics. IBM’s published quantum technology roadmap targets a fault-tolerant quantum system in 2029. That is an important technological ambition, not a guaranteed date for widespread commercial advantage. These two clocks should not share the same investment assumptions. Security readiness is justified by exposure and risk tolerance. Commercial adoption must be justified by demonstrated advantage and economic returns. Confusing the two creates either dangerous underinvestment or speculative overspending for the CIOs. ## Architecture before acquisition The enterprise architecture challenge is more consequential than the quantum procurement question. For CIOs, post-quantum preparation begins with initial understanding of where vulnerable cryptography exists across their enterprise, how it supports critical business services and whether those dependencies can be changed without destabilizing operations. For example, consider a globally distributed financial institution. Cryptographic dependencies may span payment platforms, customer identities, authentication services, certificates, mainframes, cloud environments, third-party APIs and decades of retained information. Replacing an algorithm is not necessarily equivalent to replacing a library. Some changes affect protocols, hardware security modules, certificate lifecycles, application compatibility, vendor contracts and operational resilience. This demands cryptographic agility: the ability to replace or adapt cryptographic mechanisms without rebuilding the enterprise. The same architectural discipline applies to future quantum workloads. Enterprises should favor modular integration, well-defined interfaces, workload portability and interoperability across classical computing, high-performance computing, AI and emerging quantum services. Quantum will not make classical computing obsolete. The more credible architectural direction is selective integration where a quantum capability demonstrably improves a specific computational outcome. The architectural objective therefore is not quantum dependence. It is quantum optionality. An enterprise should be able to adopt a useful breakthrough without surrendering control of its technology economics or becoming captive to a single provider. ## The missing equation: Quantum workload economics The quantum conversation becomes strategically meaningful when CIOs stop asking what a quantum computer can theoretically perform and start asking what business outcome it can improve. The OECD’s 2026 research, Building Business Readiness for Quantum Computing, identifies barriers including technological immaturity, uncertainty surrounding business applications, access and training costs, and shortages of specialized expertise. These are not simply engineering obstacles. They are capital allocation constraints. This means that a quantum experiment may demonstrate technical progress while failing to establish commercial value. For example, a proposed optimization workload should be evaluated against competitive classical algorithms, realistic datasets, operational constraints and the full cost of execution. The comparison must include more than processing time. It should consider problem preparation, data movement, orchestration, error mitigation or correction, integration, specialized talent, assurance and recurring operating expenses. Even an impressive laboratory result may offer little economic benefit if the end-to-end enterprise workflow becomes slower, costlier or less dependable. I would therefore introduce a more demanding investment metric: Cost per Trusted Quantum-Enabled Business Outcome. This proposed measure evaluates the total attributable cost of achieving a validated business result, including the controls necessary to make that result operationally acceptable. The denominator is not the number of quantum circuits executed. It is the number of useful, verified business outcomes delivered. Where no defensible quantum advantage exists, the responsible decision may be to continue using classical computing. That is not technological conservatism. It is capital discipline. ## Introducing the quantum capital discipline framework I propose a six-dimensional executive framework for separating quantum preparedness from quantum speculation. 1. **Exposure: What must we protect?****** Identify vulnerable cryptographic dependencies, long-lived sensitive information, critical services and contractual or regulatory obligations. Prioritize remediation by business consequence, not technology fashion. 2. **Relevance: What problem warrants quantum?****** Establish a specific business problem whose computational characteristics plausibly justify quantum investigation. Scientific curiosity alone is not an enterprise investment thesis. 3. **Advantage: What evidence proves improvement?****** Demand reproducible comparisons against strong classical alternatives. Evaluate solution quality, performance, operational constraints and independent validation. 4. **Architecture: Can we adapt without lock-in?****** Assess portability, integration complexity, vendor concentration, reversibility and the ability to change technology choices as the market evolves. 5. **Economics: What is the full cost of value?****** Measure total lifecycle costs, opportunity costs, talent requirements and credible returns. Distinguish research expenditure from production investment. 6. **Authority: Who decides to advance, defer or stop?** Assign accountable decision makers, measurable milestones, investment thresholds and explicit exit criteria. Together, these dimensions form the Quantum Capital Discipline Framework. Its governing principle is straightforward: Capital follows evidence. Architecture preserves options. Governance owns consequences. Importantly, this framework does not subject essential cryptographic remediation to the same commercial-return requirements as speculative quantum workloads. Security obligations and innovation opportunities require different investment justifications. ## What boards should demand from their CIOs Quantum strategy should enter Board discussions through enterprise risk, capital allocation, resilience and competitive positioning — not promotional demonstrations. I would encourage directors and executive committees to demand answers to five key questions: 1. Which critical business services and information assets face material quantum-related cryptographic exposure? 2. Can we modernize vulnerable dependencies without unacceptable operational disruption? 3. Which proposed quantum applications outperform credible classical alternatives? 4. What evidence would justify expanding, delaying or terminating quantum investments? 5. Who is accountable for the financial, security and architectural consequences? These five questions create a practical governance boundary between preparedness and speculation. They also expose a broader institutional weakness: enterprises often approve emerging technology investments without establishing the conditions under which those investments should be reconsidered. Quantum requires a different approach. Investment decisions should be revisited when technical capabilities, benchmark results, standards, security exposure or business economics materially change. Boards should insist on measurable progress but avoid manufacturing artificial deadlines for breakthroughs nobody can reliably predict. ## The CIO’s next mandate: Preserve the freedom to act Quantum computing will not transform every industry simultaneously, deliver equal advantage across workloads or reward every early investor. Some enterprises may eventually realize significant value from specialized quantum applications. Others may encounter quantum’s most immediate consequences through cryptographic modernization, technology dependencies and the protection of long-lived information. Both outcomes demand leadership today. The CIO’s responsibility is not to predict which quantum architecture will prevail or which vendor will dominate. It is to ensure that today’s decisions do not compromise tomorrow’s ability to compete, adapt and protect enterprise trust. That requires investment discipline, architectural foresight, credible experimentation and accountable governance. The next quantum advantage may belong not to the enterprise that invests first or spends the most, but to the one that knows precisely when to act, where to invest and when to walk away. Quantum readiness is not measured by the technology an enterprise owns. It is measured by the strategic freedom it retains, the risks it responsibly reduces and the business value it can demonstrably create. That is the difference between quantum enthusiasm and quantum leadership. Quantum leadership is not about predicting when the future arrives. It is about architecting the freedom to embrace it, exercising the discipline to invest in it and preserving the authority to walk away when promise fails to become value.

Join us calling union members and their families to learn about the issues they're facing in an increasingly costly state and educate them about the pro-working-family candidates that the Florida's un...

A router on its own isn't a complete solution to the tokenomics crisis that is on every enterprise CIO's mind. And for regulated and sovereign teams it can leave the biggest problem untouched entirely. What you need is local inference.