Brandsit

Tag: Saas

  • Will AI kill traditional software? Tech giants fight for the market

    Will AI kill traditional software? Tech giants fight for the market

    There is a growing debate in Silicon Valley, which last month cost the software sector almost a trillion dollars in market valuation. The question is fundamental: will generative artificial intelligence, capable of writing code and automating processes on its own, make traditional SaaS platforms redundant? Industry leaders, from Oracle to Salesforce, have moved to counterattack, arguing that their greatest asset is not the code itself, but the unique data on which they operate.

    Oracle’s Mike Sicilia and Salesforce’s Marc Benioff reject the vision of a ‘software apocalypse’ with one voice. In recent meetings with analysts, both stressed that AI is not an existential threat, but a turbocharger for existing systems. Oracle, whose shares rose 10% after optimistic forecasts, is betting on flexibility and deep embedding in financial and logistical processes. According to analysts, it is the possession of ‘proprietary data’ that provides the most effective moat against new players such as Anthropic.

    Despite the confidence of the giants, the market remains sceptical of companies whose data is easier to replace. An example is Workday, whose share price has been hit hard. Although the company manages a huge amount of HR information, critics note that HR data is often subject to rigid, standardised formats. This makes them more susceptible to replication by agile AI models.

    However, Aneel Bhusri, returning CEO of Workday, raises a compelling technical argument: today’s artificial intelligence is probabilistic – based on probabilities and patterns. Meanwhile, critical corporate systems need to be deterministic; they need to deliver the same precise result every time, especially in the area of payroll or accounting.

    Instead of obituaries, market observers suggest evolution. Salesforce is promoting its Agentforce platform, and Oracle is integrating AI into its entire technology stack, from database to end-user applications. The advantage of the traditional players comes from switching costs – companies have spent decades building operations around these tools. While AI lowers the barrier to creating new software, it will not so easily replace decades of experience in managing complex business processes.

  • Is AI the end of SaaS? The myth of free code in business

    Is AI the end of SaaS? The myth of free code in business

    In the public debate on the future of technology, a thesis that inspires euphoria in some and existential fear in others is increasingly common. Its content is deceptively simple: since artificial intelligence can generate complete application code in a few seconds, the marginal cost of software development drops to zero. In view of the fact that any user equipped with a sophisticated language model can reproduce the architecture of a powerful system in a single afternoon, traditional companies based on the Software as a Service model would supposedly lose their raison d’être. This vision is based on a fundamental cognitive error. Confusing code syntax with business service is a trap that ignores the essence of the modern digital economy.

    The real value of software has never been in the binary instruction record itself, but in the promise that this record realises. The current fascination with free code is akin to the delight in the fact that paper and ink are cheap, which would supposedly render notarial contracts or financial analyses worthless. Meanwhile, the role of the traditional SaaS model is being dramatically strengthened. It is becoming a shield separating the customer from the chaos and unpredictability of generative algorithms.

    When considering the economic foundation of this thesis, it is worth looking at the financial structure of mature technology companies. The belief in the imminent death of the industry assumes that the programming process accounts for the lion’s share of a company’s expenditure. Operational reality, however, draws a very different picture. In mature business models, the R&D budget typically oscillates around a quarter of total revenue, and the physical process of writing code itself is only a fraction of the engineering work. Most of the resources are consumed by architectural decisions, domain modelling and interpretation of intricate user requirements. The mathematics here are inexorable: the impact of artificial intelligence on the total cost structure is a few to several per cent in real terms. This is an optimisation, not a budget revolution.

    Moreover, the savings generated at the code development stage are rapidly consumed by rising operational costs. Intelligence-based software does not operate in a vacuum; it requires enormous computing power. Each query to an intelligent system generates a cost higher than a traditional database reference. As a result, the barrier to entry for new players wishing to compete solely on the price of ‘free code’ remains extremely high. It is not possible to permanently undercut the market when process costs rise along with the ambitions of the algorithms.

    In B2B relationships, trust is a rarer currency than computing power. Corporations do not pay for a collection of functions, but for system availability more than ninety-nine per cent of the time, for compliance with strict security standards, and for the certainty that data is processed according to the letter of the law. A clone of an ERP or CRM system generated by artificial intelligence remains just a digital mock-up. It lacks the legal background, certification and business continuity guarantees that constitute the operational security of the client.

    However, the problem of ‘probable rightness’ arises. In critical sectors such as banking, medicine or global logistics, an outcome that is ‘almost right’ is in fact completely wrong. These systems require a deterministic backbone – a structure that will deliver the same predictable outcome every time, regardless of the circumstances. The truly desirable software is not that which has been written entirely by artificial intelligence, but that which has been designed to be managed safely and predictably by it.

    It is worth emphasising, therefore, that the uniqueness of a solution does not come from the fact that it has code, but from the ability to turn technology into sustainable use value. The fear of devaluing the IT industry stems from the erroneous assumption that software is the end product. Meanwhile, software is merely the carrier of a service. As technology becomes more complex and unpredictable, customers will be willing to pay more and more for someone who will tame this complexity and take full responsibility for it. SaaS is undergoing a mature transformation. It is ceasing to be a tool for editing data and is becoming a guarantor of stability in an uncertain digital environment.

  • The paradox of on-demand technology: cloud and AI innovation paid for by rising costs and chaos

    The paradox of on-demand technology: cloud and AI innovation paid for by rising costs and chaos

    Companies around the world are aggressively investing in on-demand technologies such as the public cloud, software as a service (SaaS) and generative artificial intelligence. The aim is to accelerate innovation and maintain competitive advantage.

    But a new global report from Capgemini Research Institute, based on a survey of 1,000 managers from organisations with revenues of more than US$1bn, reveals a hard truth: rising costs, complexity and lack of oversight are beginning to threaten the viability of these critical investments.

    A surge in investment and expectations

    The transformation towards consumer models is a reality. According to the report, the share of on-demand technology in IT budgets is set to increase from the current 29% to 41% over the next year.

    For the majority of business leaders (77%), the scalability and performance of the cloud is the foundation for growth, enabling faster time to market. Nevertheless, behind this enthusiasm lie significant operational and financial challenges.

    The hidden costs of digital transformation

    The Capgemini study identifies key problem areas that undermine the benefits of flexible technology models.

    • Uncontrolled cost growth: As many as 82% of executives surveyed report significant increases in spending on cloud, SaaS and Gen AI. The problem is not only price increases, but also notorious budget overruns. 76% of organisations spent more on the public cloud than they anticipated (by an average of 10%), and similar problems affected budgets for Gen AI (68%) and SaaS (52%). The main reasons were inefficient use of resources and decentralised, uncontrolled purchasing.
    • IT in the shadows and security risks: Purchasing decisions are increasingly being made outside IT departments. Business units already account for 59% of spend on generative AI and 48% on SaaS. What’s more, an estimated 12% of all software-as-a-service spending remains completely outside any form of governance. Almost all managers (98%) admit that they make technology purchases bypassing the IT department, leading to inefficiencies, duplication of tools and the creation of security vulnerabilities.
    • Disappointing return on investment: Despite the huge financial outlay, the real benefits often remain below expectations. Only 29% of companies achieved the anticipated cost savings from SaaS implementations. Similarly, only 33% achieved the desired quality of cloud services and 38% saw the expected acceleration of innovation through generative artificial intelligence.

    FinOps: a theoretical solution, immature in practice

    The answer to cloud cost management chaos is supposed to be the discipline of FinOps. And while 76% of organisations have or plan to implement dedicated teams in this area, their approach is still in its infancy.

    Most companies focus on basic cost monitoring tools, but only 37% regularly analyse this data and take optimisation measures based on it.

    Most symptomatically, only 2% of FinOps teams manage cloud, SaaS and Gen AI costs in an integrated way. This shows that FinOps is still seen as an operational tool, rather than a strategic function influencing business decisions.

    Sustainability: the forgotten dimension of optimisation

    Inefficient management of on-demand technologies also has an environmental dimension. More than half of companies (53%) agree that sub-optimal use of resources leads to excessive energy consumption and increased carbon emissions.

    Despite this awareness, only 36% have a strategy that integrates sustainability goals with financial operations. Optimising resources, switching off unused instances or designing energy-efficient architectures are actions that can bring both financial and environmental benefits.

    The paradox of on-demand technology is that tools created to increase agility and efficiency, without proper oversight, become a source of chaos and unpredictable costs. The data clearly shows that the way to realise their full potential is through mature, integrated financial, security and sustainability management.