Many companies prefer to rent AI-based solutions instead of building their own business assets. While most enterprises can track AI expenses, few can accurately determine the return on investment.
Many companies prefer to rent AI-based solutions instead of building their own business assets. While most enterprises can track AI expenses, few can accurately determine the return on investment.
According to Paul Domanski, founder of the AI system developer Cape Town Domanski.AI, the lack of quantitative measurement of results has been the biggest problem in the AI industry. Instead of selling standard subscriptions or generic training, Domanski launched the In-Seat AI methodology. This methodology installs custom-developed AI systems directly into existing employee roles, allowing organizations to restore production capacity, retain institutional knowledge, and, crucially, own the created AI systems and intellectual property.
Paul Domanski states, 'Too many companies are renting AI instead of building business assets.' He explains that their model is simple: 'We embed AI into people, not replace them. Repetitive work is automated while human judgment remains under full control. Most importantly, the client owns the AI workflow we built specifically for their business.'
The financial implications of this approach are measurable. In one early example, it was found that one repetitive workflow consumed between 128 and 240 person-hours annually in a seven-person organization. Using an average annual employment cost of R600,000 per employee, this equates to approximately R37,000 – R69,000 in annual costs for one routine business operation.
Domanski.AI focuses not on replacing staff but on freeing up that time to perform higher-value tasks. This allows experienced employees to concentrate on revenue generation, strategic decision-making, and client interaction, rather than engaging in routine administrative work. Unlike traditional AI consultants or software vendors, Domanski.AI transfers ownership of each custom-developed AI system to the client. The business continues to use the preferred AI platform, but the workflow architecture, business logic, institutional knowledge, and role-specific information remain the client's intellectual property, not another vendor's subscription product.
Domanski emphasizes that AI is transforming from an operational expense into a long-term business asset because companies do not have to rely on someone else's proprietary systems to access their own organizational knowledge. The methodology has already been tested in real-world scenarios. During a four-hour implementation workshop with Mack Brands, an AI system built on the company's own files generated a customized distributor presentation of 10–15 slides. This system successfully rejected intentionally introduced false information through evidence verification mechanisms and allowed the team to create a second AI operational agent during the same workshop.
Emma Kendrick Cox, Global Marketing Director at Mack Brands, noted: 'By lunchtime, three managers in my department had already asked Paul to conduct individual sessions to build AI around their own workflows.'
A second example involved LiquidGold, a three-person direct-to-consumer business. After a 72-minute remote implementation, a marketing process that had stalled for two years was transformed. The company received a functioning Facebook ad scheduler, numerous ad copy options, campaign images, video content, and a complete campaign deployment checklist, with ad variations being generated in less than five minutes.
Domanski carefully distinguishes between measurable performance and exaggerated AI claims. He states: 'We do not promise miracles. We measure the workflow time before and after implementation, correction rates, quality improvements, and whether the recovered capacity is redeployed into productive work. These are metrics that executives and CFOs can verify.'
The process begins with a four-hour Foundational Workshop, where Domanski works with leadership teams to identify high-value repetitive workflows, create a functional AI prototype using the company's own data and processes, and develop an implementation roadmap. Organizations moving to full-scale deployment receive a credit for participating in the workshop as part of the implementation program.
Although the first documented implementations focused on marketing and creative teams, the methodology applies to any repetitive, knowledge-intensive workflow in finance, HR, operations, customer service, compliance, and sales. According to Domanski, as South African organizations move beyond AI experimentation, the competitive advantage will increasingly belong to those enterprises that can demonstrate measurable productivity gains and retain ownership of the systems generating that productivity. He concludes: 'The future will not belong to the companies buying the most AI subscriptions. It will belong to those who own the intelligence embedded in their own business.'
When submitting resumes and tailoring documents for different opportunities, candidates may notice that the responses received are often similar. An investigation conducted by Stanford University suggests that this similarity may not be a coincidence.
The researchers analyzed millions of applications and concluded that various companies may employ artificial intelligence systems that replicate the same acceptance and rejection criteria. The research, titled Algorithmic Monocultures in Hiring, examined about 4 million applications submitted by over 3.4 million individuals to 156 companies across 11 economic sectors.
A crucial factor identified was that all these organizations were using algorithms provided by the same vendor. This detail allowed for the identification of the phenomenon known as 'algorithmic monoculture,' a term inspired by agriculture, where vast areas are dedicated to a single type of crop. When many companies adopt similar tools, the probability increases that they will evaluate applicants following essentially the same logic, which applies to both successes and failures inherent in the models.
Another relevant finding concerns similar candidate profiles. According to the study, individuals with analogous characteristics tend to receive consistent evaluations, even when competing for positions at different corporations. The primary results reveal that approximately 10% of candidates participating in four selection processes are rejected in all of them. Additionally, about 4% of those who apply for ten positions suffer ten consecutive rejections.
The rejections occur more frequently than expected in independently made decisions. It is notable that many resumes are eliminated before even being reviewed by a human recruiter. To confirm whether this behavior was random, the researchers compared the data with a theoretical baseline and previous studies on recruitment without algorithmic centralization, demonstrating that successive rejections reflect a common pattern across different selection processes.
According to the simulations conducted, continuing to submit resumes is still advantageous. The study points out that increasing the volume of applications improves the chances of obtaining an opportunity, although this benefit decreases when companies use identical systems. In a scenario where decisions are autonomous, about ten applications would be sufficient to achieve a high probability of receiving at least one positive recommendation. However, when processes are mediated by centralized platforms, this number rises to approximately 25 applications to ensure a 99.9% probability.
The authors also issued a warning about the concentration of the technology market focused on recruitment. Since few vendors serve many companies, any existing biases or limitations can spread rapidly. Furthermore, the lack of transparency in these platforms hinders independent research and complicates the understanding of how such tools impact employment access, especially since, for many candidates, this entire process occurs without them knowing that an algorithm performed the initial resume screening.
