When electricity was first introduced into factories, most owners simply used it to extend working hours, and productivity improved only slightly. The real transformation came only when leaders realized that electricity made it possible not just to work longer, but to work differently. It enabled the creation of assembly lines and precise manufacturing. Technology did not merely optimize the old way of working—it made it obsolete, says Piotr Łobaczewski, Regional Vice President, Digital Solutions, CEE at Salesforce.
Today, we are at a similar turning point, this time driven by artificial intelligence. For small and medium-sized business owners, the opportunity is no longer about marginal efficiency gains. It is about eliminating “operational friction”—the invisible barriers that historically caused process complexity to grow faster than the business itself.
For decades, the irony of business success was that growth created additional burdens. Each new client added another layer of administrative work. Sales and customer service teams fell behind not because they lacked skills, but because they lacked the capacity to keep up. For most companies, scaling was a linear struggle against time.
Lost context: the hidden brake on growth
Growing companies do not neglect customer relationships because they do not care. They fail because customer data is scattered across multiple tools, creating a contextual gap. The result is a slow, quiet erosion of the relationships that once made these businesses stand out. A customer who once felt personally served begins to feel like just another number in a spreadsheet.
According to the latest edition of the Salesforce report Small & Medium Business Trends, 46% of SME leaders feel overwhelmed by the number of tools they use, and 44% say they do not have enough time to learn how to use all their technologies effectively. This “lack of time for context” has become a silent brake on growth and one of the strongest arguments for simplifying the technology stack and automating data-driven work.
This is precisely the problem that AI-powered agents can solve.
However, at the executive level, one key condition must be met: an agent designed to “act” needs not only access to data, but also “trusted context.” This includes consistent definitions, data quality, provenance (lineage), and clear rules for data usage. Without this, automation scales chaos faster than any team can control it.
That is why Salesforce consistently combines three layers that can be compared to a living organism. MuleSoft acts as the “nervous system,” transmitting signals and enabling secure operations via APIs. Data 360 functions as “working memory,” integrating and activating customer profiles and real-time context. Informatica, in turn, resembles the “circulatory system and filter,” ensuring data quality, consistency, cataloging and lineage so that AI agents do not guess, but reason.
This is also the practical objective behind the acquisition of Informatica. Salesforce has indicated that the goal of this transaction is to build a solid and reliable data foundation for AI solutions. It is about combining data management tools—such as data catalogs, quality control, access management and metadata—so that companies can use artificial intelligence safely and responsibly at scale. In practice, this means better data control, easier audits and greater trust in AI-generated outcomes.
Unlike previous generations of business software, AI agents are not just designed to store data or display information on demand. Imagine the competitive advantage of a team that never has to “dig” through databases because its agents have already analyzed the full history of customer relationships and prepared recommendations for the next steps.
The compounding effect of data integration
In the old world of software, more data meant more noise. In the agent-driven world, data becomes a strategic asset. Every interaction and every completed transaction fuels a digital workforce that grows smarter every day. A company that has spent two years integrating its data will possess an intelligence advantage over a much larger competitor that is only just beginning its AI journey.
This is the essence of structural advantage: the ability to build a sophisticated organization without proportionally increasing headcount.
In practice, this creates a “compounding effect” in data. The earlier a company organizes definitions of key entities such as customers, products or suppliers, automates the flow of signals and implements governance rules, the faster the quality of AI-driven insights and actions improves. This advantage is difficult to replicate, as it is built over time through data quality and process discipline—not simply through the number of software licenses.
One of the biggest fears for growing businesses is the loss of a human touch—the belief that scaling means serving customers “at scale” rather than individually. Agent-based AI completely reverses this model. By delegating operational workloads to agents, leaders can redirect human judgment toward activities that drive growth and strengthen relationships.
A flower shop handling hundreds of wedding inquiries or a consulting firm managing dozens of clients can finally deliver personalized service at a scale that previously required a massive customer service department.
The condition for this “personalization at scale” is trust—both cultural (trust-first) and technical. A crucial role is played by layers of security and control that protect data, improve the accuracy of outputs and reduce risks—for example through mechanisms such as grounding, zero retention and toxic content detection. Only then can an agent act as a reliable digital co-worker rather than an unpredictable experiment.
The most visionary leaders are no longer asking whether they should implement AI. They are asking a different question: what would my business look like without the constraints of operational friction?
In the coming era, it will not be the largest companies that win, but those richest in context. It is no longer just about adapting AI to the organization, but about preparing the organization to fully leverage artificial intelligence. Looking back, the companies being built today will see this moment not as a simple productivity boost, but as the point at which the rules of scaling changed forever.
