The gap between promise and reality: What Salesforce's struggle reveals about AI transformation in the tech industry

The great AI disillusionment: Why Salesforce shows that reality looks different

The spectacular 27 percent drop in the share price of CRM giant Salesforce since the beginning of 2025 is not an isolated phenomenon of a single company. Rather, it symbolizes a fundamental discrepancy between the high expectations surrounding artificial intelligence and the harsh reality of its commercial application. While tech companies worldwide proclaim a revolution through autonomous AI agents, Salesforce's situation reveals three key problems that could be symptomatic of the entire industry: the monetization of AI innovations, the structural maturity of the enterprise software market, and the increasing complexity of technology integration. This analysis examines what truly lies behind the supposed promise of the future and what consequences this has for the tech industry.

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Fundamentals and Relevance

Salesforce's situation in October 2025 marks a turning point in the perception of artificial intelligence as an immediate growth driver for established tech companies. Marc Benioff, the charismatic founder and CEO of the customer relationship management company, had proclaimed the age of agent-based AI at the company's Dreamforce conference in San Francisco. His vision: Autonomous algorithms would replace human employees in companies and become Salesforce's most important revenue generator. Reality, however, paints a different picture.

The dramatic decline in Salesforce's share price stands in stark contrast to the general trend in the tech sector, where technology stocks have seen significant gains over the same period. This divergence raises fundamental questions: Has the industry overestimated the speed at which artificial intelligence can be translated into real revenue? Are expectations for autonomous AI agents realistic? And what structural problems lie hidden behind the glittering facade of AI's promise?.

The relevance of this analysis extends far beyond Salesforce. It affects all companies that rely on artificial intelligence as a key growth driver. It impacts investors who are pouring billions into AI technologies. And it touches upon employees whose jobs are threatened by the promised automation. The Salesforce case offers a unique insight into the mechanisms, hopes, and disappointments of an industry in flux.

This article is divided into eight sections, systematically presenting the historical roots, technical mechanisms, current status, practical use cases, critical problems, future developments, and a concluding synthesis of the findings. It will become clear that Salesforce's challenges are exemplary of deeper industry problems that extend far beyond a single company.

From cloud pioneer to AI fighter: The strategic realignment of an industry giant

To understand the current situation, one must trace the origins and evolution of Salesforce. Founded in 1999 by Marc Benioff, the company revolutionized the software industry with a then-radical concept: Software as a Service (SaaS). Instead of selling expensive license packages that had to be installed on customers' servers, Salesforce offered its CRM solution over the internet. Customers paid a monthly fee and could easily access the software through their web browser.

This innovation made Salesforce the market leader in customer relationship management. With a market share of over 21 percent, the company continues to dominate the global CRM market, far ahead of competitors such as Microsoft, Oracle, and SAP. For over two decades, Salesforce was considered a growth stock par excellence. Revenue increased by double digits year after year, the stock price climbed steadily, and the company expanded through numerous acquisitions.

However, even in the years leading up to 2025, the first signs of a slowdown became apparent. The growth of the CRM software industry as a whole slowed as the market became increasingly saturated. Many large companies had already implemented CRM systems, and the low-hanging fruit had been picked. At the same time, new competitors emerged, capturing market share with innovative approaches and lower prices.

In this situation, Benioff increasingly focused on artificial intelligence as a new growth story from 2022 onwards. First, Salesforce introduced Einstein, an AI platform that enabled predictive analytics and automation within existing CRM products. Then, in September 2024, came the big announcement: Agentforce, a platform for autonomous AI agents designed to independently handle tasks in areas such as customer service, sales, and marketing.

The vision was ambitious: By the end of 2025, customers were to create one billion autonomous AI agents via the platform. These agents would not only answer simple queries, but also independently plan and execute complex, multi-stage tasks. They were to act proactively, make decisions, and access the company's entire data base.

In parallel, Salesforce invested heavily in the technological foundation for these AI agents. In May 2025, the company announced the acquisition of Informatica, a data management specialist, for eight billion dollars. The acquisition was intended to ensure that the AI ​​agents had access to high-quality, well-structured data. In the fall of 2024, Salesforce had already acquired OwnData, another data management company, for 1.9 billion dollars.

Despite these massive investments and the grand vision, the hoped-for revenue leaps failed to materialize. In the second quarter of fiscal year 2025/26, Salesforce's revenue grew by 9.8 percent to $10.24 billion. While this slightly exceeded expectations, it marked the fifth consecutive quarter of single-digit growth. The outlook for the coming quarter was even more subdued, fueling concerns that the AI ​​initiative would not deliver the anticipated commercial success.

The anatomy of autonomous AI agents: Technology between vision and feasibility

To understand why monetizing AI agents proves so challenging, one must examine the technical foundations and mechanisms of these systems. Agentforce is based on several technological components that must work together to achieve the promised autonomy.

At its core is the so-called Atlas Reasoning Engine, which functions as the neural network or brain of the AI ​​agents. This engine is designed to mimic human thought and action, correctly categorize tasks, prioritize steps, and ultimately execute them accurately. Unlike previous AI assistants such as Copilot, which relied heavily on human interaction, the Agentforce agents are intended to operate largely autonomously.

The second key component is the Salesforce Data Cloud, which harmonizes all relevant company data in real time and makes it available to the AI ​​agents. The quality and completeness of this data are crucial for the agents' performance. This is also where one of the biggest challenges lies: Many companies have collected their data in various systems over the years without uniform standards or regular data cleansing.

The third component consists of integration tools like MuleSoft and pre-built connectors that allow agents to interact with existing workflows and external systems. These interfaces enable agents to operate not only within the Salesforce environment but also to communicate with other enterprise applications.

In addition to these Salesforce-native components, Agentforce also integrates large language models from third-party providers such as OpenAI, Anthropic, and Google Gemini. These models provide the underlying natural language processing and general world knowledge upon which the specific agents are built.

The functionality can be illustrated using the example of a customer service agent: A customer contacts the company with a request. The agent analyzes the request, accesses the relevant customer data from the data cloud, compares it with similar cases from the past, develops a multi-step solution plan, executes these steps, and communicates the result to the customer. All of this happens without human intervention, unless the agent encounters a problem that exceeds their capabilities.

In theory, it sounds impressive. In practice, however, there are numerous pitfalls. The agents are only as good as the data they can access. If the data is incomplete, outdated, or inconsistent, the agents will make incorrect decisions. Integration into existing enterprise systems is often complex and requires considerable effort. And while agent configuration is advertised as a low-code process, it still demands significant technical understanding and Salesforce-specific expertise.

Another problem is the lack of trust. Many companies hesitate to hand over control of critical business processes to autonomous agents without robust testing procedures and security mechanisms. The risk of errors, data breaches, or undesirable behavior is real, as examples from other industries demonstrate.

The difficult road to profitability: Three fundamental challenges

Salesforce's problems can be boiled down to three key challenges that are representative of the entire industry: the monetization of AI innovations, structural market maturity, and the complexity of technology adoption.

The first challenge concerns monetization

Although Salesforce has developed a technologically advanced product with Agentforce, the crucial question remains: How can it be monetized? Agentforce's pricing model is based on two dollars per conversation, a usage-based approach that differs from traditional licensing models. However, many potential customers are hesitant to adopt this technology on a large scale until the return on investment is clearly demonstrable.

The costs of operating AI agents are substantial. The underlying large language models require expensive computing resources. According to industry estimates, a single query to a generative AI model costs up to ten times more than a traditional Google search. These costs must be passed on to customers, which limits price acceptance. At the same time, customers expect AI agents to deliver clear added value that justifies the higher costs.

To date, only about 12,000 companies are using Agentforce, a vanishingly small number considering Salesforce's massive customer base of several hundred thousand businesses. Recurring annual revenue from Agentforce is less than $500 million, a fraction of the total revenue of over $40 billion. Even if this number triples or quadruples in the coming years, as Salesforce hopes, the contribution to overall revenue would still be limited.

The second key challenge is the structural maturity of the CRM market

After two decades of strong growth, the customer relationship management (CRM) software market has reached a saturation point. Most large and medium-sized companies in developed markets have already implemented CRM systems. The potential for organic growth through new customer acquisition is limited.

At the same time, competition has intensified. Microsoft with Dynamics 365, Oracle with its cloud applications, SAP with its CRM solutions, and numerous specialized providers such as HubSpot, Zendesk, and Zoho are all vying for market share. These competitors have caught up in recent years and sometimes offer more affordable or specialized solutions.

In this environment, it will be more difficult for Salesforce to achieve double-digit growth rates, even with innovative AI features. Customers don't simply switch their CRM system because a vendor offers new AI capabilities. Implementing a CRM system is complex, expensive, and time-consuming. Companies are reluctant to switch as long as their existing system is working.

Analysts like Karl Keirstead of UBS have pointed out that the CRM market is already relatively mature, while customer AI investments in this area are still in their very early stages. There is therefore a time lag between the market maturity of core products and the maturity of AI add-ons. This gap makes it difficult for Salesforce to regain its past growth momentum.

The third fundamental challenge concerns the complexity of technology adoption

Although Salesforce promotes Agentforce as a user-friendly, low-code solution, the reality for many customers is considerably more complex. Successful implementation of AI agents requires a solid data foundation, well-defined processes, technical expertise, and significant investments in training and change management.

Many companies struggle with fundamental challenges such as poor data quality, isolated data silos, inadequate IT infrastructure, and a lack of AI expertise. These problems must be solved before AI agents can reach their full potential. This requires time, resources, and a long-term approach, which many companies shy away from.

Added to this is the skills shortage. The demand for AI experts, data specialists, and Salesforce administrators far exceeds the supply. Companies have to pay high salaries to attract and retain qualified employees. This further increases the cost of implementing AI solutions and extends the time to value creation.

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Success stories and disillusionment: What practice teaches us about AI agents

To get a complete picture, it is worth looking at specific use cases and practical experiences with AI agents, both at Salesforce itself and at other companies.

Salesforce itself has undertaken one of the most high-profile implementations of AI agents: in its own customer service. CEO Marc Benioff announced in September 2025 that the company had reduced its customer service team from 9,000 to 5,000 employees, a cut of 45 percent. The laid-off employees were replaced by AI agents, which, according to Benioff, have already handled 1.5 million customer interactions, with similar customer satisfaction levels to human agents.

This drastic measure demonstrates, on the one hand, the potential of AI agents to automate repetitive tasks and reduce costs. Salesforce saves considerable personnel costs through these layoffs and can simultaneously handle more inquiries. On the other hand, it raises ethical and practical questions. The quality of customer service for more complex inquiries requiring human judgment and empathy remains to be seen. Other companies, such as Klarna, which pursued similar automation strategies, have had to admit that service quality suffered.

A second example is AI agents in sales. Several Salesforce customers have implemented agents that automatically qualify potential customers, schedule appointments, and send follow-up emails. These agents work around the clock and can handle hundreds of leads simultaneously. According to Salesforce, some customers have reported that the productivity of their sales teams has increased by 20 to 30 percent through the use of such agents.

However, there are limitations. The agents function best with standardized processes and clearly defined qualification criteria. They quickly reach their limits in complex B2B sales processes that require in-depth product knowledge and strategic negotiation skills. Furthermore, some users report a degree of dissatisfaction among potential customers who prefer to speak with a human.

Beyond Salesforce, numerous other companies are using AI agents. ServiceNow, a direct competitor of Salesforce in the IT service management sector, has developed its own platform for AI agents. These agents are designed to independently diagnose and resolve IT problems, process service requests, and orchestrate workflows.

Microsoft also relies on agent-based AI with its Copilot products, albeit with a slightly different approach. The Microsoft agents are more deeply integrated into existing Office 365 products and focus on supporting individual productivity rather than autonomous process automation.

SAP and Oracle are pursuing similar strategies and developing AI agents that are directly embedded in their ERP and CRM systems. SAP has introduced Joule, an AI assistant that analyzes business processes, provides recommendations, and automates tasks. Oracle is focusing particularly on AI-powered cloud infrastructure and positioning itself as a platform for compute-intensive AI workloads.

What all these examples show is that AI agents work best in clearly defined use cases with structured data and standardized processes. The more complex, unpredictable, and human-centric a task is, the more difficult it becomes for autonomous agents to achieve or surpass human performance.

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Criticism, controversies and unresolved questions: The dark side of the AI ​​revolution

Salesforce's problems and the broader challenges of implementing AI agents have sparked an intense debate about the technology's promise and limitations. Several critical aspects deserve special attention.

The first point of contention concerns job losses. Salesforce has sent a clear signal by laying off 4,000 customer service employees: AI agents are not only replacing inefficient processes, but also people. Benioff had previously asserted that AI would not lead to the disappearance of office jobs. Reality shows otherwise.

This trend isn't limited to Salesforce. According to data, more than 64,000 jobs in the technology sector were eliminated in the US alone in 2025, many of them related to increased automation through AI. The irony is that many of these companies are simultaneously looking for new employees, particularly in AI development and sales of AI products. So, a shift is taking place where certain jobs become obsolete while others emerge. But the question remains whether the newly created jobs will compensate for the lost ones in both number and quality.

The second critical aspect is the discrepancy between marketing and reality. Salesforce and other tech companies have promoted AI agents with grandiose promises: a revolution in the workplace, magical productivity gains, autonomous systems that replace human employees. The reality, however, is that many implementations are still in the pilot phase, and the promised productivity gains often fail to materialize or are only realized in limited areas.

A Capgemini study found that while 90 percent of surveyed executives are convinced that agent-based AI offers a competitive advantage, only 14 percent have actually begun implementation. The majority are still in the planning phase, and almost half lack a concrete implementation strategy. Trust in fully autonomous AI agents has declined significantly in the past year, from 43 to 27 percent.

A third problematic point is the dependence on individual tech giants. Salesforce Agentforce is tightly integrated with the Salesforce ecosystem. The agents function best when all data and processes reside within the Salesforce world. Integrating external knowledge sources or systems requires considerable effort. This creates a vendor lock-in effect, making it difficult for customers to switch to alternative solutions.

Similar criticism is leveled at Microsoft, SAP, and Oracle. Each vendor is trying to create its own ecosystem in which its AI agents function best. This complicates the integration of different systems and forces customers to choose a single primary vendor. Initiatives like the Model Context Protocol, which aims to enable standardized communication between AI agents from different vendors, are still in their infancy.

A fourth controversial aspect concerns data privacy and security. AI agents require access to extensive company data to function effectively. This creates potential security risks, especially when this data is shared with external AI services like OpenAI or Anthropic. Although Salesforce and other vendors emphasize that they have implemented strict data protection measures, concerns remain, particularly in regulated industries such as healthcare or financial services.

The fifth critical point is the environmental impact. Operating large AI models requires enormous amounts of computing power and therefore energy. The data centers that run these models consume millions of kilowatt-hours of electricity and produce significant CO2 emissions. At a time when companies are increasingly under pressure to meet their sustainability goals, the environmental footprint of AI systems is becoming a growing concern.

A look into the future: Between consolidation and the next wave

Despite all the current challenges, experts assume that AI agents will play an increasingly important role in companies in the coming years. The question is not whether, but how quickly and in what form this technology will prevail.

Gartner predicts that by 2026, approximately 40 percent of all enterprise applications will include task-specific AI agents, a significant increase from less than 5 percent in 2025. By 2035, agent-based AI could account for roughly 30 percent of global enterprise software revenue, more than $450 billion. The market for autonomous AI and autonomous agents is projected to grow from $8.62 billion in 2025 to $263.96 billion by 2035, representing a compound annual growth rate (CAGR) of over 40 percent.

These forecasts are based on the assumption that the current challenges will be overcome gradually. Several developments could contribute to this:

First, the technology itself will continue to evolve. The underlying Large Language Models will become more powerful, efficient, and cost-effective. New models like OpenAI's o1 with improved reasoning or Anthropic's Claude with longer context windows will enable more complex tasks. The cost of AI inference has already fallen dramatically, by a factor of 280 between November 2022 and October 2024. This trend is likely to continue, making AI applications more economically attractive.

Secondly, companies will learn to use AI agents more effectively. Early adopters will gain experience, identify best practices, and share them with the wider community. Training programs, certifications, and consulting services will emerge to support companies in their implementation.

Third, standardization could progress. Initiatives such as the Model Context Protocol or ServiceNow's agent-to-agent protocol aim to enable communication between AI agents from different vendors. If such standards become established, this would facilitate integration and reduce vendor lock-in.

Fourth, a consolidation of providers is to be expected. The market for AI agents is currently fragmented, with dozens of startups and established players vying for market share. Acquisitions and market consolidation are likely to occur in the coming years, similar to what has happened in other technology segments in the past. Large companies like Salesforce, Microsoft, Google, SAP, or Oracle will acquire smaller providers to expand their AI capabilities.

For Salesforce specifically, the crucial factor will be whether the company can successfully integrate the Informatica acquisition and generate real added value for Agentforce. The acquisition is the largest in the company's history since the Slack purchase in 2021. It carries risks, as evidenced by the downgrade by RBC, which drastically lowered the price target. However, it also offers opportunities if Salesforce can thereby create a more comprehensive data management platform that makes AI agents more effective.

In the medium term, by 2030, Salesforce aims for revenue exceeding $60 billion, which corresponds to an organic growth rate of over 10 percent per year. This would represent a return to double-digit growth, after falling below this mark since mid-2024. Whether this goal is realistic depends significantly on whether Agentforce and other AI products achieve the anticipated success.

In the long term, development could move toward complex multi-agent ecosystems, as Gartner predicts. In such systems, specialized agents work together, coordinate their actions, and share information. One agent could analyze customer inquiries, another develop solutions, a third coordinate implementation, and a fourth monitor quality. This orchestrated collaboration would be able to automate even more complex business processes.

But there is still a long way to go. The next two to three years will be crucial to see whether the current problems can be overcome and whether the promised productivity gains and revenue increases actually materialize.

Lessons from the Salesforce crisis for the tech industry

The analysis of the Salesforce issue reveals fundamental truths about the state of artificial intelligence and its commercial application. The central finding is that there is a significant discrepancy between the technological feasibility of AI agents and their economic profitability in the current market environment.

Salesforce exemplifies an industry that entered the AI ​​era with high expectations but is now confronted with the harsh realities of monetization. The three main problems identified—monetization difficulties, market saturation, and adoption complexity—are not specific to Salesforce but affect the entire enterprise software industry.

Experience shows that technological innovation alone is not enough. Companies must also develop a compelling business model, demonstrate clear customer value, and lower adoption barriers. Salesforce has created a technologically impressive product with Agentforce, but translating that into sustainable revenue growth remains a challenge.

For investors, this means they must distinguish between short-term hype and long-term value. The high valuations of many AI companies are based on expectations of future profits that may not materialize or may be significantly delayed. A sober analysis of actual adoption rates, revenue contributions, and profitability is essential.

For companies looking to deploy AI agents, the recommendation is: Start with clearly defined use cases, invest in data quality and change management, and don't expect miracles overnight. The most successful implementations focus on a few, but well-executed projects, rather than launching many superficial experiments.

For employees, this development means that certain tasks will be automated by AI, while new roles will emerge. Investing in AI-related skills – whether in development, management, or the strategic application of AI – is becoming increasingly important.

The Salesforce case is therefore far more than the story of a single company in trouble. It is a lesson in the challenges of technological transformation, the gap between vision and reality, and the need to maintain a clear view of economic realities despite all the enthusiasm for new technologies. The AI ​​revolution will come, but it will be gradual, bumpy, and selective—not the often-invoked Big Bang, but a continuous process with its ups and downs.

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