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From cybersecurity and consulting to publishing and software development, companies are embedding artificial intelligence into everyday operations to reduce friction, accelerate decision-making, and deliver better outcomes.
Artificial intelligence has moved beyond the experimental stage. Across industries, organizations are no longer treating AI as a future investment but as an operational tool that is actively changing how work gets done. From cybersecurity firms and management consultancies to data publishers and software developers, companies are integrating AI into their daily workflows to streamline processes, accelerate delivery, and improve client outcomes.
Businesses are now realizing that the greatest value lies not in AI-generated content but in AI-powered workflows that remove bottlenecks while keeping humans firmly in control.
Cybersecurity Moves to Continuous Testing
As cybercriminals gain access to increasingly sophisticated AI tools, cybersecurity firm CovertSwarm has adapted by incorporating AI into its offensive security testing operations. The company combines AI-driven reconnaissance with human expertise, creating a symbiotic approach to identifying vulnerabilities.
“AI is just like a force multiplier, isn’t it? So in the same way that we get enabled by AI in, for example, researching new topics that we might be interested in, or for example, with coding, it’s been a real accelerator. Those same accelerations apply in, you know, use cases that might not be so friendly. AI has been great for everyone, and sadly, that includes the bad guys,” said Dominika Pietrzak, RAID Leader at CovertSwarm.
To stay ahead, the company’s internal RAID team develops specialized tools that can be deployed across its consultants, enabling broader testing coverage without increasing headcount.
“It really feels to us like a symbiotic relationship, and it’s working together that really has brought the best results from our perspective,” Pietrzak added.
That approach is also changing client expectations. Rather than relying on annual penetration tests, organizations are increasingly embracing ongoing assessments.
“Just because you do a pen test in February, you’ve pushed new code in March or the week after, like that pen test is outdated straight away. So we’re continuously testing and using the latest tools to do that. That’s really what’s going to help you stay ahead of the threat, I think,” Pietrzak added.
Turning AI Into a Habit
While many organizations focus on technology, management consulting firm SHA/RP argues that successful AI adoption depends on behavior change.
Founder Raphael Peyret believes AI must fit naturally into established routines if companies expect employees to embrace it.
“We think that it’s a technology problem, and we forget that actually the technology part is the easy part. You’re going to create a center of excellence. You’re going to give it to IT, right? The problem with that is as long as it stays there, some other random guy comes and tells you how to do your job better. And you’re like, you have no clue,” Peyret said.
His own workflow relies on structured AI-powered routines that organize information at the start and end of each day.
“The reason why I focus on routines is if you look at the science between habit forming, right; you need to anchor habits on something that you know is going to happen. Because I have this as kind of a framework, even if I mess up one day, like the next, you get back to it, and you’re back on the rails,” he explained.
When productivity improvements are substantial, adoption follows naturally.
“The gains were so ginormous for them because otherwise they would go and look through documentation themselves; they would send a message to a sales engineer who wouldn’t know half the time. The benefits are so significant that there is a change of behavior,” Peyret added.
Scaling Workflows, Not Content
For Thomas Rewwer, founder of AmericaByNumbers, AI’s most valuable contribution lies in managing large-scale workflows rather than producing written content.
“Don’t measure AI on how many emails and texts that it writes. That’s a small win, but the real win is that AI runs the whole workflow. Normally, users need people to do that,” Rewwer said.
His platform processes vast amounts of government data, enabling a largely automated system to publish information that would traditionally have required a sizable editorial operation.
“It’s like a superpower. It’s crazy. Colleagues that use AI are 10 times faster,” he noted.
To maintain accuracy, Rewwer combines automated validation with human oversight.
“I need samples. I have half a million pages; it’s too much to check all, but I can do samples over here, over here, over here, and take a look: okay, it’s right, it’s the correct data, and everything is the right way,” he added.
Keeping AI-Generated Code on Track
The rapid adoption of AI coding assistants has created a new challenge for software teams: ensuring generated code aligns with architectural standards.
Theo Valmis, founder of MnemeHQ, said development teams are increasingly overwhelmed by the volume of AI-generated output.
“With all this AI-generated code, the review of it is still, to an extent, manual. Developers now end up outputting a lot of code as teams, and they cannot physically review that code. Either they will cut corners, or they are frustrated when they’re trying to adapt to their environment,” Valmis said.
His company addresses the issue by validating code against architectural requirements before it reaches the review stage.
“It checks before you submit the code for review, right? If it follows the architectural constraints. It doesn’t allow architectural drift,” Valmis explained.
He also believes AI is lowering both cost and knowledge barriers for organizations.
“Now you can have, like, an agency working for you, knowing all the programming languages, all the information that’s out there, at the tip of your hands for $100 per month instead of $5,000, $10,000. One is capital, and the other is a knowledge barrier, because if you’re into it, it educates you, it makes you better, it gives you so many opportunities to evolve,” Valmis added.
From Assistants to Autonomous Agents
At Palo Alto Networks, AI’s impact is measured by time saved. According to Senior Engineering Manager Mona Rajhans, even small efficiency gains compound quickly across daily operations.
“It’s not uncommon to see tasks that previously took 30 to 60 minutes have now been reduced to 5 to 10 minutes or even less with AI-assisted workflows, but the bigger impact is cumulative. When dozens of micro-frictions disappear throughout the day, overall operational velocity improves significantly,” Rajhans said.
She emphasized the importance of gradual adoption.
“Train your users one step at a time, because at the end of the day, your end customers, the ones who are paying you, are humans; they’re not machines,” Rajhans stated.
Looking ahead, she sees AI evolving beyond assistance and toward autonomous action.
“I feel like we’re moving from passive co-pilots towards autonomous workflow systems. The next-gen AI is increasingly observing our workflow state, it’s reasoning across systems, and it’s coordinating actions. It maintains memory. And it proactively recommends or executes next steps. So I feel like we’re now getting into AI agents instead of AI assistants,” Rajhans added.
A Common Thread Across Industries
Despite operating in different sectors, AI delivers its greatest value when it improves workflows rather than simply generating content. Whether through continuous cybersecurity testing, habit-based consulting systems, large-scale data publishing, code governance, or operational automation, the most successful implementations target specific points of friction while preserving human oversight.
As organizations continue to refine their strategies, AI is increasingly becoming a workflow multiplier, helping companies make better decisions and create lasting advantages for their clients.
