New research out today from Rogers Cybersecure Catalyst at Toronto Metropolitan University (“the Catalyst”), funded by the Future Skills Centre through the Government of Canada’s Future Skills Program, examines how AI is changing the work traditionally done by early-career cybersecurity professionals and what that could mean for how they build foundational skills and experience.
The report, Preserving Foundational Skills in AI-Enabled Early-Career Cybersecurity Work, explores how the growing use of AI in cybersecurity could affect entry-level roles, learning opportunities, and the pathways through which new professionals develop their expertise.
The “entry-level paradox”
Employers described a shift in traditional Level 1 cybersecurity work, with tasks such as alert triage, query writing, and incident investigation increasingly automated or AI-assisted. As a result, early-career professionals are taking on more complex investigations, exercising greater judgment, and navigating business and operational context earlier in their careers. Some employers are describing this as “Level 1.5” roles. This shift is creating an “entry-level paradox” where employers want job-ready cybersecurity talent, while AI is reshaping the routine tasks through which newcomers have traditionally become job-ready.
Several employers raised concerns about a potential “loss of practice,” as AI reduces opportunities to independently perform the tasks that build technical knowledge, problem-solving skills, and professional judgment. At the same time, the research points to a broader shift in the capabilities employers value. While 64% of consultation responses identified foundational technical and IT knowledge as important, 93% of employers identified human-centred capabilities (commonly referred to as “soft skills”) as increasingly important, including communication skills (36%), stakeholder management (27%) and critical thinking (18%).
AI literacy is not a stand-alone skill. Professionals need to know how to use AI appropriately in the context of their specific tasks. And they need to have these role-specific technical AI skills in combination with communications skills, collaborative abilities, and an ability to think critically about problems.
Rethinking how cybersecurity talent is assessed and developed
These changing expectations also have implications for how cybersecurity professionals are assessed and hired. Nearly 1 in 3 employers raised concerns about assessment and hiring practices as candidates increasingly use AI during technical assessments, certifications, and interviews. Employers questioned whether traditional credentials and assessments provide enough evidence of workplace readiness, and many expressed growing interest in more applied ways of demonstrating readiness, including:
Proof of work (concrete evidence of a candidate’s applied skills and capabilities, like technical portfolios)
Simulations (practical scenarios that assess how candidates apply their skills in realistic situations)
Scenario-based assessments (assessing how candidates approach realistic challenges with and without AI)
Participants also highlighted governance, risk, and practical risk decision-making as increasingly important. Early-career professionals may need to understand not only how to identify technical risks but also how to assess, communicate, and manage those risks within a broader business context.
The research points to a need for employers, educators, and training providers to rethink what “job-ready” means, how job readiness is assessed, and how early-career professionals can build and demonstrate the capabilities needed to succeed as AI changes traditional pathways into cybersecurity. This includes creating more opportunities for hands-on learning and operational experience, using applied assessments that test reasoning and judgment with and without AI, and preparing professionals to validate AI outputs, understand risk, and apply human judgment.
The Catalyst tested these approaches through two pilot training iterations involving 55 early-career cybersecurity professionals, using hands-on simulations, a tabletop exercise, and an AI literacy lab to practice these skills in realistic scenarios.
Ultimately, the research suggests that AI is not eliminating early-career cybersecurity roles but reshaping the work, pathways, and opportunities through which the next generation of cybersecurity professionals develops experience. As those pathways evolve, employers and educators will need to ensure that AI-driven efficiency is matched by meaningful opportunities to learn, practice, and demonstrate the skills cybersecurity work still depends on.
The full report available here
AI order-to-cash platform Stuut raises $52.5M Series B after unlocking 40% more cash for enterprises
Posted in Commentary with tags Stuut on October 7, 2026 by itnerdBusinesses worldwide have $16 trillion locked up in unpaid receivables. Stuut, the AI platform that runs order-to-cash for the some of the world’s leading enterprises, is going after it.
In a world where all finance software often looks and behaves the same, Stuut offers customers the ability to automate the vast majority of their work. It runs the entire order-to-cash process, moving dollars through collections, cash application, payments, disputes, and deductions. Stuut’s customers are freeing up to 40% more cash flow, with a 47% reduction in DSO.
Now, just ten months after its Series A and amid overwhelming demand, the company is announcing a $52.5 million Series B led by Insight Partners, with participation from Andreessen Horowitz, M12, Microsoft’s Venture Fund, and Activant, bringing its total funding to $93 million.
The problem Stuut is solving
Most customers want to pay. But a missing PO, bad order data, or an invoice sent to the wrong person triggers weeks of emails, portal work, and internal chasing. By the time an invoice is overdue, one small error can drag sales, finance, operations, and multiple systems into the mess. At enterprise scale, getting paid turns into millions of tiny investigations consuming thousands of hours.
The cost can be enormous: broken order-to-cash processes can wipe out as much as 5% of a company’s revenue, as much as $1 trillion a year across the Fortune 500 alone.
How Stuut works
A missing PO can snowball into a rejected invoice, then a portal submission, then a short-pay or deduction. Stuut follows that entire chain across thousands of invoices at once, reaching customers worldwide via SMS, email, and call, logging into AP portals, reconciling cash, and taking the next action without losing context.
Every interaction makes Stuut harder to replace. It builds a living memory of each customer: how they pay, which portals they use, what breaks and how it gets fixed. That memory compounds until Stuut disappears into the background. The work keeps moving without finance teams having to manage it. When they want visibility, they can ask what happened, why it happened and exactly what Stuut did to resolve it.
This is already happening at scale. 81.7% of outbound collections activity runs without human involvement, while 95% of incoming payments are matched automatically. Stuut now extends into credit and order management, catching issues upstream before they turn into payment problems.
Importantly, enterprises don’t have to change how they work. Stuut instantly integrates into any ERP, bank account, CRM, and payment system, going live in days. It’s configured to each company’s existing processes and controls, with every action auditable and any behavior change requiring approval.
Stuut is also partnering with leading firms across working capital to give enterprises a faster way to buy, deploy and scale the platform, including Fiserv, EY, Altamont, HIG, and more.
Traction
Today, Stuut is used by over 150 customers, including Fortune 50 and Fortune 500 companies. Its customer base has grown 5x since last year and more than $3 billion has moved through the platform, with customers aggressively pulling Stuut across more of the order-to-cash lifecycle.
Some of the world’s largest companies are seeing similar results. Bishop Lifting has rolled Stuut across 45 branches for collections, disputes and cash application, cutting overdue receivables by 35%, unlocking $3 million in working capital and increasing accounts managed per employee by 50%. Honeywell runs Stuut on top of legacy SAP to reach the long tail of customer accounts and is expanding the platform into quote-to-cash. At ZoomInfo, Stuut has collected $21.2 million and reduced time to first touch by more than 90%.
Why this matters now
The work of getting paid is getting harder to do. Finance teams are handling more customers, more transactions and more systems, while more than 300,000 accountants have left the profession since 2019. And for all the software built around order-to-cash, most of the actual work still falls to people. US businesses are carrying $7.2 trillion in trade receivables and every additional day of DSO leaves roughly $150 billion tied up. DSO is closely watched by boards and, at some companies, tied directly to CFO compensation. Now that software can actually execute the work, rather than just organize it, order-to-cash is becoming one of the highest-value deployments of AI.
Looking ahead
With the company growing over 90% quarter over quarter, Stuut will use this funding to meet overwhelming customer demand and expand deeper into the financial infrastructure around every transaction, from credit and lending to the movement of funds. The long-term ambition is much bigger: make selling radically easier for some of the world’s largest enterprises. Stuut wants to carry every transaction from the moment a company decides to sell something, through every decision, document and payment in between, until the cash is in the bank.
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