Inznius’AI Future in Payroll
Australia’s own Inzenius’ all-in-one Rostering-to-Payroll solution with API Integration to the Chosen HRM system is changing the world with AI.
Inzenius has been involved in Award Interpretation software for over 25 years, with its patented all-in-one Rostering-to-Payroll system. It continues to receive Australian Government R&D support for its latest AI design, which focuses on payroll compliance automation.
The Inzenius R&D team, led by founder Russell Bode, has never stopped innovating over the past 25 years, enabling the most complete, configurable automated interpretation of awards from approved employee timekeeping records.
Inzenius is specifically built for the most demanding IR and Payroll rules environment of the reported top 3 most complex in the world, including Australia, France and Bolivia.
Inzenius continues to innovate with its current AI strategy for further payroll system enhancements and innovation to deliver the most up-to-date, compliant payroll processing automation, tailored for the business and the Australian market payroll compliance regulations, like the Better-Off-Overall Test (BOOT)
Inzenius Capability Reference from Incognitus
“We found Inzenius ’integration aligned seamlessly with our SAP platform, fully meeting our specific rostering and payroll operational requirements. Here we are now with, not only a better overall SAP/Inzenius system that is now delivering up to a 70% reduction in data duplication and entry across a number of systems in payroll processing time and effort, but we have also achieved additional improved functionality overall. The support provided during the configuration and after our go-live was not only timely and efficient but also customer-focused with a detailed understanding of our specific needs”
Payroll Systems worldwide need to innovate or become redundant
The old-world payroll software is running for cover with its legacy software, as demonstrated by the share price fall. “Workday, Paycom and Paylocity have fallen 40% and 44% respectively over the same period. And Dayforce saw the writing on the wall, went private with Thoma Bravo for $12.3 billion in February 2026. Dayforce can now invest in HCM tech without the market scrutiny destroying Workday’s stock value.” Chris Harvey HR Strategist Researcher commented. The (Workday) stock specifically has dropped 43% in 12 months—a destruction of over $30 billion in value.
What support will be available for their Australian customers when they are under so much pressure, when payroll compliance is a must with high penalties if not?
Why businesses need to act now on potential payroll underpayment
From 1 January 2025, intentional underpayment of wages or entitlements can be a criminal offence. This doesn’t include honest mistakes.
An employer may commit a criminal offence where they:
- were required to pay an amount:
- to an employee, such as wages or paid leave entitlements
- on behalf of the employee, or for their benefit, such as superannuation or a salary sacrifice arrangement
AI is changing the world of HRM and Payroll
HBR Staff/Luca Onniboni/Unsplash
Article Extracts Summary.
Generative AI is dismantling the economic logic that made standardised enterprise software the default, making it fast and feasible for companies to build systems tailored to how they actually work. As a result, the core strategic question is shifting from which tools to buy to which workflows and capabilities to own, with organizations choosing among building, composing, collaborating, or buying outcomes. The companies that win will pair speed with discipline—investing in data, governance, and clear choices about where customization truly creates advantage.close
Generative AI is dissolving the economic logic that made standardized enterprise software the only practical choice for most companies. What replaces it will be shaped not just by the rapidly evolving capabilities of this new technology, but by leaders willing to ask a harder question: Which workflows do we actually need to own?
Incognitus reference
“We found Inzenius ’integration aligned seamlessly with our SAP platform, fully meeting our specific rostering and payroll operational requirements ” Grant Summers CFO Incognitus
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Until recently, organisations adapted their workflows to standardised software solutions. The workflow conventions of a vendor, such as Salesforce’s customer relationship management (CRM), SAP’s enterprise resource planning (ERP), Workday’s human capital management (HCM), or Epic’s electronic health record (EHR), became, by default, the workflow conventions of your company. You surrendered fidelity to how you actually worked in exchange for software you could work with.
That bargain is breaking faster than we anticipated. In a May 2025 article, we argued that generative AI would upend the foundational logic of enterprise software, shifting automation from workflows to outcomes, from systems that enable work to systems that perform it. We expected that transition to unfold over a decade. We were wrong—not about the direction, but about the pace.
The shift is now rapidly visible in how companies spend on IT and in how they think about work itself. Enterprise spending on generative AI applications has surged from $1.7 billion in 2023 to $37 billion in 2025, reaching a meaningful share of the global software market in less than three years. At the same time, companies are beginning to reduce reliance on traditional software-as-a-service (SaaS) tools. As a result, public SaaS valuations have compressed sharply from their 2021 peaks, with many leading companies trading 30–60% below prior peaks.
These changes point to something deeper than a technology cycle. Companies need to decide strategically which workflows they want to control themselves. Much as the first wave of enterprise software—with the emergence of outsourced IT, call centers, and cloud services—redrew organizational boundaries, this new wave of generative AI will force a renewed examination of those boundaries.
The disruption of software we anticipated is underway. The more consequential question is what generative AI solutions get built in its place, and by whom.
The Scale, Scope, and Speed of Change
Before we discuss how the software solutions landscape and organizational boundaries might evolve, it is helpful to keep in mind a few trends that presage the urgency with which leaders must respond.
First, generative AI spending has moved with unusual speed. Enterprise investment in generative AI applications has risen more than twentyfold in two years. That level of penetration took SaaS nearly a decade to achieve.
Second, how software is created is changing. An estimated 40% of code is now AI-generated, and the vast majority of developers use AI coding tools in their daily work. What once required months of engineering effort can increasingly be produced in days.
Third, early substitution effects are already visible inside organizations. In surveys, more than a third of companies report replacing at least one SaaS tool with a custom-built generative AI alternative, while a majority expect to build more such tools in the coming year. In some early-adopting mid-market companies, spending on categories such as project management software has declined materially even as AI investment has surged.
The implication is subtle but important. When software can be shaped around how a company actually works, rather than the other way around, the question of what to standardise and what to differentiate becomes a strategic choice rather than a cost-imposed constraint.
That shift, more than any individual tool or platform, is what makes this moment consequential.
Four Models Taking Shape
As this transition unfolds, a small number of distinct approaches are beginning to emerge. Each reflects a different answer to the same underlying question: Where should the boundary lie between what an organization builds for itself and what it relies on others to provide?
Build
Some companies will choose to construct their own systems directly on top of foundational models. These systems are not organized around the vendor’s workflow conventions, but around the specific jobs the organization is trying to perform.
A logistics company, for example, might build a system designed not simply to track shipments, but to continuously optimize delivery time and cost based on its own data. Over time, that system becomes a repository of institutional knowledge that is difficult for competitors to replicate. The tradeoff is clear: greater control and differentiation in exchange for ongoing technical complexity.
A useful check is whether the system encodes truly distinctive data or decision logic; if not, building, even though it initially seems easy, may be overreaching, and composing or collaborating could deliver similar value with far less complexity.
Compose
A second path is emerging for organizations that want flexibility without full ownership. In this model, vendors provide scaffolding, templates, and primitives that business users can configure to their needs.
For example, a sales organization might use a composable platform such as Salesforce Headless 360 to tailor lead scoring, outreach sequencing, and pipeline reviews to its own motion, without building a CRM from scratch. The direction of customization reverses. Software adapts to the company, rather than the company adapting to the software. This approach lowers the burden of building from scratch, but it also means that some constraints remain, particularly in how far customization can extend into core functions.
Collaborate
A third model shifts from buying enterprise software to collaborating with providers to build tailored solutions. Providers work with clients—using forward-deployed engineers—to define the job clearly, then build bespoke systems designed around that outcome.
What once required long and expensive implementation cycles can increasingly be done in weeks. A specialized team might help a manufacturer redesign its procurement process and build a system around it in a fraction of the time a traditional ERP deployment (which also involves the use of consulting services for configuration and minor customization) would require.
The advantage is speed and alignment; the risk is dependence on external expertise. Over time, companies should assess whether repeated reliance on external partners is masking an opportunity to internalize a strategically important capability, or conversely, whether what is being built lacks uniqueness and could be bought as an outcome.
Buy Outcomes
The most radical model moves beyond software altogether. Instead of purchasing tools, companies procure the outcome itself. An organization might no longer license accounting software and operate it internally, but instead contract for accurate financials, compliance, and reporting as a service.
In this model, vendors do not provide tools; they take responsibility for results. This can significantly simplify operations, but it also shifts control and requires a high degree of trust in the provider. This model works best when the outcome is non-differentiating; if performance in that domain begins to shape competitive advantage, firms may need to reclaim control by building, composing, or collaborating more directly. Another manifestation of this model is the recently announced outcome based pricing by Adobe for its CX Enterprise suite and agentic AI tools. Adobe will deploy its AI agents directly with the customer, but rather than charging per seat or for consuming AI tokens, it will charge for outcomes such as the number of successful ad campaigns completed by its AI agents.
These models are not mutually exclusive. Most organizations will employ some combination of all four. The critical point is that the choice among them is no longer dictated primarily by cost or feasibility. It is a strategic decision about where differentiation matters and where it does not.
The Strategic Question This Forces
The deeper significance of this shift is not about software. It is about competitive advantage and, more fundamentally, about what the firm itself is for.
When bespoke systems were prohibitively expensive, the question of which capabilities to own was largely settled by default. You ran your own payroll, your own customer support, your own demand forecasting, not because these were sources of differentiation, but because the available tools required you to. The standardized systems that enabled those functions made one company’s implementation look much like another’s.
That is no longer true. When any function can be deeply customized, or handed to an external provider to deliver as an outcome, every organization faces a strategic choice it has not had to confront in a generation: Which jobs do we truly want to own?
Some of those jobs will be drawn deeper inside the organization and rebuilt as proprietary capability. Others will move outward to partners who can perform them more efficiently or at higher quality. The boundary of the firm becomes a variable rather than a given.
This also changes the nature of advantage. In a world where systems can be replicated quickly, advantages built solely on process or embedded logic are more contestable. What remains defensible is a clear understanding of which jobs are truly distinctive to how a company creates value, and the discipline to build depth in those areas rather than spreading effort across everything.
There is, however, a practical constraint that early adopters have already encountered. Moving quickly is not the same as moving effectively. Organizations that rush to automate without re-architecting their data and processes often find that quality suffers, edge cases accumulate, and systems become difficult to manage. The lesson is not to slow down, but to pair ambition with discipline: to treat data architecture, governance, and ownership as integral to the transformation rather than afterthoughts.
For leaders navigating this transition, a small number of decisions matter disproportionately.
First, treat data architecture as the foundation. Fragmented, vendor-managed data is not merely inefficient; it limits what AI systems can do.
Second, revisit the build-versus-buy decision for each function. The relevant question is no longer whether something can be built, but whether it should be built, and where doing so creates meaningful differentiation.
Third, establish clear governance for internally developed systems. As more software is created outside traditional IT structures, issues of security, maintainability, and accountability become central rather than peripheral.
Fourth, recognize that software choices are increasingly workforce choices. When an AI system performs work that was previously done by teams of people, decisions about technology are also decisions about organizational design.
What Comes Next
It would be a mistake to declare the end of SaaS. Many applications will persist, and some vendors will adapt successfully by moving toward more flexible, outcome-oriented models. The transition will unfold unevenly across industries and functions.
But the underlying logic has changed. For a generation, the question in enterprise software was which tools should we buy. The emerging question is more fundamental: How can we rethink our workflows and decide which we want to build, compose, collaborate on, or buy outcomes?
The answer will vary by company and by context. But organizations that approach this question with clarity will find that it yields something more valuable than a software strategy. It offers a sharper understanding of where they create value, and why that value is difficult to replicate.