Human error amplifies risk in AI-Enabled systems
Security often comes down to simple human decisions, and the impact those have once AI is introduced. Take the McHire breach at McDonald’s, for example. Their platform, used for AI-supported hiring, exposed data from 64 million applicants. Why? An admin account was left with the password “123456.” That’s a human shortcut.
Here’s the problem. AI systems operate at scale. If something goes wrong, it goes wrong fast and big. The McHire flaw wasn’t caused by AI itself, but AI amplifies the consequences of bad calls like this. Once sensitive data funnels into an AI-supported pipeline, weak access control causes a problem, and it snowballs. Many executives still treat AI as a secure, isolated architecture. It’s not. It’s integrated into everything, and when everyone has access to it, security oversight becomes non-optional.
Executives need to step up governance now. Default passwords shouldn’t exist in production environments. That’s a given. But more critically, teams must assume AI platforms will be targeted because of their data weight and speed of access. You can’t secure these systems reactively. They demand proactive threat modeling, continuous monitoring, and automated audits. And all of it needs to be layered in from day one.
A platform that processes millions of candidate records should have zero tolerance for shortcuts. Even in fast-moving organizations, getting the fundamentals right needs to be higher priority than getting to market first. Because when AI enters your workflow, everything moves faster, including security failures. Better tools won’t protect bad processes. Get the processes right first.
AI adoption broadens the cybersecurity threat landscape
When you integrate AI across a business you increase surface area. AI systems need access to massive volumes of data. That data doesn’t live in one place. It flows across cloud storage, SaaS platforms, internal apps, and third-party APIs. Every connection is a potential entry point. Every data touchpoint is a potential vulnerability.
A lot of companies underestimate what this means. They deploy AI to improve efficiency or gain insights, but don’t consider the security implications. You’re not just embedding a tool, you’re embedding a system that wants to reach across every department, pull in as much data as possible, and learn from all of it. That appetite opens new attack vectors. Relying on historical security setups built for static systems won’t cover it.
The speed and dynamism of AI don’t naturally align with legacy security models. Static firewalls, siloed scanning tools, and manual patch cycles won’t cut it. If AI is real-time, security has to be real-time too. Continuous behavioral analysis, adaptive identity management, and smart automation need to become part of the standard operating model. Otherwise, the scale of risk will outpace your ability to manage it.
The trend is already visible. As more companies migrate AI operations into multicloud environments, the complexity of threat detection skyrockets. Cloud providers don’t secure your applications. That’s still your job. And AI raises the bar. The more systems your AI connects with, the more capable and attractive those pathways become for threat actors.
If you’re in the C-suite, your security posture needs to evolve in parallel with your AI strategy. They’re not separate tracks. They scale together. When AI drives the business forward, security needs to be built into it, not bolted on after. The companies that understand this will stay ahead. The ones that don’t will fall behind. Fast.
Complexity in software licensing amid mergers and acquisitions
When businesses go through mergers or acquisitions, software licensing often becomes a pain point. It’s about understanding what terms survive the deal, what gets renegotiated, and what breaks altogether. Siemens found this out the hard way after Broadcom acquired VMware and changed the terms of their licensing model. That shift created real uncertainty around continued access to essential software.
Here’s why this matters at the executive level: many critical operations still rely on long-term contracts with software providers. If those providers change hands, licensing terms can shift overnight. Suddenly, you no longer have guaranteed access to updates, patches, or support. For a company running global infrastructure on software like VMware, that becomes more than a compliance problem, it becomes a stability issue.
M&A events increasingly include major platform providers. And when licensing is not explicitly addressed in contracts during due diligence, the result is legal ambiguity, operational risk, and unplanned cost exposure. That puts CIOs and CTOs in reactive mode, which is the opposite of strategic control.
The practical takeaway: leadership teams need to treat software licensing as a core component of M&A strategy. Legal, procurement, and IT should be aligned ahead of any deal. Understand not just what’s in the contract, but what happens if the vendor is acquired, if support timelines change, or if pricing models shift. These are scenarios that can disrupt business continuity.
The Siemens situation makes the point clear. You can’t assume today’s terms will hold tomorrow under different ownership. Businesses that want to remain flexible and protected need to audit their software dependencies regularly and include escalation clauses that anticipate change. If you’re in the C-suite, ensure you’re not operating on unsecured assumptions when it comes to platforms your teams rely on. Because once you’ve already signed the deal, you’ve lost most of your leverage.
Agentic AI and digital twins revolutionize workforce decision-making
Agentic AI and digital twins are already redefining how businesses operate. These systems simulate real-time environments and make autonomous decisions based on dynamic input. That changes enterprise architecture. It also changes how people work inside it. According to Smart Answers, 15% of day-to-day decisions are expected to be made autonomously in the near future. This isn’t automation, this is AI actively managing complexity with minimal human involvement.
The initial impact will be most visible in functions like IT support. High-volume, repetitive tasks are a poor use of skilled labor. Agentic AI can manage those with better speed, accuracy, and cost efficiency. That frees people up, but it also forces a shift. As machines handle mechanics, humans need to move into roles where intuition, creativity, and judgment matter more. That means rethinking team structure, operational strategy, and hiring priorities across departments.
This is a transition phase. The companies that move early will get compound returns. They’ll reduce operational friction and increase output per employee. But embracing the shift isn’t just about implementing AI. It’s about creating a model where humans and machines don’t duplicate effort, they complement each other. That takes coordination. Leaders need to identify where autonomous decision-making adds value, and where human oversight is still critical.
Training matters here. Your workforce can’t add strategic value if they’re stuck doing tasks that AI already does better. Upskilling becomes a priority. Not as a one-time initiative but as an ongoing organizational mindset. AI is progressing fast. Your people need to stay ahead of it, technically, strategically, and operationally.
So yes, AI is taking over decision points. That’s not a threat, it’s an opportunity. But only if leadership builds the systems, incentives, and culture to align humans and AI around real outcomes. Smart companies will recognize that early and move with intent.
Key takeaways for decision-makers
- Human error scales with AI: Simple security lapses, like weak default passwords, have outsized consequences in AI-enabled systems due to their speed and data access requirements. Leaders should prioritize baseline security discipline before scaling AI platforms.
- AI expands attack surfaces: AI demands broad data access across cloud infrastructure, making traditional security models insufficient. Executives must invest in real-time threat detection and proactive cybersecurity aligned to AI’s reach.
- M&A creates software license risk: Changes in vendor ownership, like Broadcom’s acquisition of VMware, can disrupt existing licensing terms and software access. Leadership teams should reassess license agreements during any M&A to avoid operational risk.
- Agentic AI shifts workforce priorities: With 15% of daily decisions soon to be handled autonomously, low-value tasks will increasingly be offloaded to AI. Leaders must re-skill teams toward strategic, creative, and oversight roles to unlock full value from AI integration.
