If you take the same model, same repo and same task and get materially different outcomes depending on selection, rules, indexing, tools, agent mode and feedback loops...

are we still comparing models, or are we actually comparing agent environments?

Just something I felt was a valid question - What are thoughts you guys have on this??

Something I’ve been thinking about after all the recent AI agent security news:

We spent years building IAM, RBAC, SSO, audit logs, least-privilege access, etc. for human users.

Now we're giving AI agents access to the same business systems.

An agent can potentially read a knowledge base, access CRM data, interact with APIs, update records, trigger workflows, send messages, and make decisions based on what it finds.

But here’s the uncomfortable question:

Who is actually responsible for the agent?

If an employee makes a mistake, we can identify the person, revoke their access, investigate their actions, and understand the context.

With an autonomous agent, things get more complicated.

You have to ask:

• What systems can the agent access?
• What data can it see?
• What actions can it execute?
• Who authorized those actions?
• Can we see everything it did?
• What happens if the agent receives malicious instructions through an email or document?
• Can we immediately stop it?

Recent research and real-world incidents are making this less theoretical. Security researchers are increasingly treating AI agents as something closer to autonomous insiders than traditional software.

And I think this is where enterprise AI platforms need to mature.

The goal shouldn't be:

>

It should be:

>

That's one reason I'm interested in platforms like Thunai. The interesting part isn't simply having an AI chatbot. It's the ability to connect AI with enterprise knowledge, applications and workflows while thinking about how those interactions are actually controlled and automated.

I think we're moving toward a model where every enterprise AI agent needs something similar to an employee identity?

Identity → Permissions → Context → Actions → Audit trail → Human oversight

The companies that figure this out early will probably have a much easier time scaling AI safely.

Curious what others are seeing:

Are your security teams already treating AI agents as identities, or are they still being treated as “just another application”?

Right now everyone is looking to automate everything - even conversations with thier kids??? What is one overhyped AI trend for businesses you feel just breaks the process or made it not at all effective that will die out in two years.

AI is a lot of the time nothing but hype and then asks you to do the process your. Just wanted to know what AI tools you guys stopped using switching over to another option! Genuinely curious about whether my team is using any of these and what made you abandon it?

I'll paint you a scenario: The board you work for saw a headline about companies spending $39 billion on there is a surplus AI talent.

The hiring manager, however, just spent six months trying to fill one AI compliance role, and this is far from true.

But here’s the FACT: Both of them are looking at the same market, and only one of them has actually tried to hire in it.

So to clear this up for you, here's what's really driving AI hiring in 2026.

The Industries Investing the Most in AI Talent

So the million-dollar question first - which industries are paying the most? Well, the truth is that although spending is up everywhere (expectedly 36% in 2026), not everyone is fighting for the same skills - the main industries investing are:

• Healthcare
• Financial Services
• Manufacturing
• Retail & E-commerce
• Defense & Aerospace
• Energy and Utilities

https://preview.redd.it/owdkmfw2n5gh1.png?width=1143&format=png&auto=webp&s=e7ec1fdec6c8244aabad0317108459dc03f51841

But with these industries, there are quite a few noteworthy stats worth mentioning, like:

• The talent demand-to-supply ratio sits at 3.2 to 1 about 1.6 million open AI roles chasing only 518,000 qualified candidates
• The medical AI market has pulled in more than $6.1 billion in recent private investment.
• Roles there still take six to seven months to fill on average, because the talent pool needs both clinical knowledge and machine learning skill.
• 100% of healthcare payer executives plan to implement machine learning into core systems by year-end.
• 94% of financial firms now call AI and machine learning their most in-demand technical skill, against a 28% supply gap
• Wages for AI-specific roles grew 42% when compared to 2021.
• Applied implementation roles make up 88% of all AI postings in finance, versus 12% for pure research roles

The Highest-Growth AI Roles Aren't Always AI Engineers

So we covered the industries, but what are the job titles growing fastest right now. Surprisingly, they’re not the ones most people expect!

1. AI Product Managers

Not a complete shocker considering the number of AI projects receiving VC funding. But here’s why it’s so popular - an AI product manager role sits right between engineering and revenue, and companies are paying for that.

And it’s pretty clear with the numbers: the median salary for a machine learning product engineer in the US has reached $228,000!

This is largely because well-managed AI product cycles add an average 34.2% profit increase for the companies running them - so we put these at the top of the list.

2. AI Security Specialists

With an astounding number of data breaches resulting from NHI and stolen OAuth tokens, AI security is the next logical role for many. That said, that’s downplaying the overall budget companies are willing to spend on them.

Defense at human speed stopped being good enough this year, and according to Reddit the amount AI security specialists are paid in the US goes to show you that.

• AI SOC Orchestrators: $90K to $140K
• AI/ML Security Engineers: $150K to $220K
• AI Offensive Orchestrators: $180K to $250K+
• Quantum-AI Security Specialists: $170K to $260K

3. AI Solutions Architects

Somebody has to connect the model to the actual business, and that somebody is in short supply. For every one foundational researcher the market demands, it needs two algorithmic specialists and four applied implementers

These architects build the RAG pipelines, vector databases, and multi-agent frameworks that make a model useful on real company data

AI Consultants

The hardest part of AI adoption right now is not technical.

• 41% of executives name workforce culture and internal resistance as the top barrier to deployment
• The EU AI Act and similar rules are expected to create 340,000 new governance, risk, and compliance roles

AI Operations Engineers

Somebody has to keep the lights on and the bill predictable.

These engineers monitor token usage, latency, and agent behavior so a runaway loop doesn't quietly burn through the budget overnight

80% of the standard engineering workforce is expected to need operational upskilling by 2027 to stay relevant

Which Industries Are Most Resistant to AI Job Displacement?

The scariest headlines about total job replacement don't hold up once you look at where human judgment is actually load-bearing.

Healthcare

Bedside medicine relies on empathy, physical dexterity, and reading a patient's non-verbal cues, none of which a model can substitute for.

Here’s a no-brainer - no one want a robot to take care of them when they’re sick!

The legal and ethical weight of patient outcomes also has to sit with a licensed human, no matter how good the diagnostic model is - so healthcare professionals are one of the safest bets.

skilled Trades

The physical world does not run on clean data - it does need clean water, running taps, and most importantly, drainage for floods, storms, and sewage.

Plumbing, electrical work, and HVAC repair happen in unpredictable, hands-on environments.

Despite the fact that a technician can diagnose a problem from a photo with the help of a model, the actual repair requires human motor skill and judgment on the spot.

Due to the fact that most electricians and plumbers must be certified and make many calls on the job, this is both a regulatory and safety requirement.

Cybersecurity

While machines may fight machines, in the world of cybersecurity, people will still have to out-think people.

Automated tools generate huge volumes of alerts and can run defensive swarms at machine speed, but understanding an adversary's motive and strategic next move still takes human intuition and business context.

As a result, intelligent cybersecurity personnel are needed now more than ever.

Education

In spite of the fact that AI is capable of grading assignments and building a personalized study plan in seconds, it is not capable of recognizing when a student is quietly struggling.

Or more importantly, mentor them through it, or manage the social dynamics of a classroom. 

Teaching is challenging and requires skilled professionals, and given its constant demand for sensitivity  - it’s not going to be outsourced to AI or robots anytime soon.

Leadership & Strategy

A growing demand for human judgement, AI oversight, and governance will account for 40% of future industrial skills.

Future technical roles will require human-machine collaboration skills, but the fastest-growing non-engineering skills are cross-functional collaboration and communication.

With AI odds are, you’ll see in some ways the death of middlemen and middle management. That said, no model can negotiate a merger or rally a team through a crisis - which is why leadership is not going to full replaceable anytime soon.

This applies even while AI in customer service and workflow automation is projected to save a lot of money, time, and human effort.

The AI Skills Employers Actually Want in Each Industry

Five specific skills command the highest premiums in every sector right now.

1. Agentic workflow design:  Building multi-agent systems with tools like LangGraph and CrewAI that can act, not just chat
2. Advanced prompt engineering and context management: Grounding a model in real company data so it stops guessing
3. Governance, risk, and compliance: This refers to knowing how to build guardrails and satisfy regulators
4. Data pipeline engineering and vector databases: This is because a model is only as good as the data feeding it
5. Human-AI collaboration and change management: This is the soft skill of getting a skeptical team to actually use the tool and test what needs improvement.

74% of companies say they cannot keep up with demand for this exact hybrid skill set, and 59% of the global workforce.

Roughly 120 million people will need real reskilling by the end of the decade.

I spent the last 18 months implementing AI-powered customer experience automation for clients across different industries and one thing has become very clear. There is a huge gap between what AI vendors promise and what actually works in production. I keep seeing the same advice repeated online, and much of it sounds great in theory but falls apart once real customers start using it. After plenty of trial and error here is what I have learned.

The biggest mistake companies make is trying to build one massive conversational AI bot that can answer every possible customer question. Almost every client asks for this at the beginning, but it rarely delivers. The bot ends up giving vague, repetitive responses, gets stuck in loops, and slowly erodes customer trust. A much better approach is to start small. Automate your 10 to 20 most common, low-risk questions first, prove they work consistently, and then expand over time.

Another common mistake is removing humans from the process entirely. Every AI chatbot should have an obvious escalation path. If a customer has gone back and forth with the bot for two or three messages without getting closer to a solution, it should immediately hand the conversation to a human agent. A chatbot that traps customers in endless conversations creates more frustration than having no chatbot at all.

Billing disputes are another area where AI often struggles. Once you introduce multiple exceptions, account-specific policies, late fees, suspensions, refunds, or manual adjustments, the logic becomes far too complicated. More importantly, customers dealing with money-related issues usually want reassurance from another person. Even if AI can technically process some of these cases, the customer experience is often better when a human takes over.

On the other hand, some AI use cases consistently deliver excellent results. Password reset automation connected directly to your Identity and Access Management (IAM) platform is one of them. Because the workflow is deterministic rather than relying on language generation, we're seeing success rates close to 98% with virtually no hallucinations. This has become one of the safest and highest ROI automation projects.

Order status inquiries are another easy win. By connecting AI to their Order Management System (OMS) via APIs, businesses can automatically answer most "Where is my order?" questions. Many organisations achieve deflection rates between 70% and 90% when the integration is implemented correctly. As a bonus, these interactions create natural opportunities for cross-selling and upselling without disrupting the customer experience.

One of the biggest surprises has been AI-powered ticket routing and triage. Instead of replacing support agents AI analyzes incoming requests, identifies customer intent, determines priority, and sends tickets to the right queue before a human ever sees them. For one client, this reduced misrouted tickets by more than 60%, significantly improving response times and reducing internal workload.

Agent-assist features have also delivered far more value than expected. When an AI chatbot transfers a conversation to a human, it can automatically generate a concise summary of the interaction. Instead of reading through dozens of chat messages, the support agent immediately understands the customer's issue and previous troubleshooting steps. This reduces average handling time, speeds up resolutions, and is one of the few AI features that support teams genuinely appreciate.

There are also several projects that can consume enormous budgets without delivering proportional value. One example is applying AI quality assurance scoring to every single customer conversation. While this makes sense in highly regulated industries such as banking, healthcare, or insurance where compliance is critical it's often excessive for smaller support teams. The infrastructure, storage, and processing costs add up quickly without providing meaningful business benefits.

Voice AI for real-time appointment scheduling is another area that requires realistic expectations. Natural conversations demand extremely low latency, accurate speech recognition, and smooth dialogue management. Achieving that level of performance is technically challenging and significantly more expensive than many companies anticipate. Without the proper investment, the experience quickly starts to feel robotic and frustrating.

Perhaps the biggest misconception is that AI can replace skilled humans during negotiation, customer retention, or other high-value conversations. These interactions require empathy, judgment, and the ability to adapt to emotional context. Current AI systems still struggle in these situations, and customer satisfaction scores reflect that reality. Rather than spending months trying to automate these conversations, businesses are usually better off routing them directly to experienced human agents.

The biggest lesson from all of this is surprisingly simple. AI performs best when it's handling structured, repetitive, and predictable tasks. Humans perform best when conversations involve emotions, exceptions, financial decisions, or complex judgment calls. The companies seeing the strongest results aren't trying to eliminate human support they are using AI to remove repetitive work so their teams can focus on the conversations that truly need a human touch. Fully autonomous customer support may eventually become reality but for today's messy, real-world scenarios, the most effective approach is still AI and humans working together.