jberk in BlogInsights

From Platforms to Ecosystems: How Agentic AI Is Changing Platform Logic

enterprise ai

After years of relatively low-contact sparring, Salesforce and ServiceNow are entering into intense market competition. With the increasing demand for AI agents and enterprise AI, both vendors are pushing aggressively into the other’s core business.

Mark Benniof (Salesforce’s CEO) has announced the introduction into the ITSM space, while Bill McDermott (ServiceNow’ CEO) has stated that they’re ready to go all-in on CRM. In this line, ServiceNow has recently acquired Logik.ai’s to boost their CRM offering. Salesforce, on the other hand, incorporated many enterprises in the past years looking to gradually enhance their ITSM services, like MuleSoft and Slack.

Businesses have a varied tech stack, using different solutions from diverse vendors

But, while this shift has many market explanations, the clearest clue that we have is within the actual enterprise software situation. Nowadays, businesses have a varied tech stack, using different solutions from diverse vendors. For example, the same organization can use SAP S/4HANA for ERP, ServiceNow for IT operations, and Oracle Databases for supply chain management.

However, over the last few years, advances in enterprise AI have shaped the logic of these various platforms. And now, Agentic AI has marked another innovative AI wave that will change the digital framework of all organizations towards a more unified and end-to-end environment.

In the words of Bill McDermott “We have a key differentiator at the architectural level because we don’t have to try to translate between our AI models and some third-party system.” This way, the shift in business platform logic can be summarized as “one platform, one architecture, and one data model”.

But what do these changes exactly consist of? How has enterprise AI changed the logic of business platforms? Today, we’ll see how you can prepare your organization for the next AI innovation wave.

ServiceNow vs Salesforce: Towards an End-To-End Business Platform Logic

So, Salesforce and ServiceNow have long dominated CRM and ITSM, respectively. But the rise of AI agents has blurred the lines between these domains, pushing both companies to compete in each other’s markets. Salesforce is expanding into IT service management, while ServiceNow is moving into CRM workflows.

This way, the point here appears to be that anything that ServiceNow can do, Salesforce can do. But things are not as easy as they look. And what has accelerated this race are the new AI agents, which are changing both our relationship with machines and our current business platform logic. Since they don’t work in silos—they require seamless access to cross-functional data to make informed decisions and act.

AI agents has blurred the lines between CRM and ITSM platforms, pushing both companies to compete in each other’s markets

Until now, traditional business platforms were designed around separate applications. But AI agents demand another enterprise AI approach. More exactly, an end-to-end one, where IT, HR, customer service, and finance operate as a single, intelligent system.

This shift is forcing enterprises to rethink their digital architecture. Instead of isolated tools, organizations need unified enterprise AI platforms that enable real-time decision-making and automation across all departments. So, the competition between Salesforce and ServiceNow isn’t just about CRM vs. ITSM—it’s about a radical change in how we understand business platforms.

How Has enterprise AI Changed the Logic of Our Digital Landscape?

So, AI evolution has changed business platform logic by replacing siloed, static processes with interconnected, dynamic intelligence. In other words, each of the AI waves fostered the passage from siloed models, where business platforms functioned separately (albeit sharing data) to an end-to-end logic, where the areas of an organization are taken as a whole.

But, to put this in perspective, here’s a breakdown of how AI models drive this evolution:

1. The Automation Era (2010-2016): Isolated Efficiency

So, in their first stages, AI was rule-based and strictly procedural. Basically, ITSM systems automated workflows, but every department still operated in isolation—HR, Finance, and Customer Service had separate platforms, data, and decision-making processes.

For example, a customer complaint would generate a ticket, but if it required logistics support, it had to be manually transferred. In other words, there was no cross-functional intelligence. So, here, AI improved individual workflows, but the next step was making them smarter.

2. The Predictive AI Era (2016-2021): Smarter, But Still Siloed

In the second of the AI waves, we can see how ML models began optimizing business processes, predicting ticket categories, resolution times, and prioritizing tasks. But AI still worked within departmental silos—basically, the systems here were able to analyze data, but couldn’t unify decisions across platforms.

Let’s take a bank’s AI model as an example. They predicted fraud risk in transactions but couldn’t communicate with customer support or compliance teams to automate a resolution. So, AI had predictive power, but it still wasn’t connecting the enterprise areas.

3. The GenAI Era (2022-2024): Content Creation Meets Business Logic

In this third AI wave, we can see how LLMs appear to generate responses, automate self-service, and even write code. So, AI improved user experiences, but actions remained disconnected. In other words, GenAI could answer questions, but couldn’t execute across multiple business systems.

Let’s take, for example, AI chatbots in e-commerce. They helped customers track an order, but resolving issues still required human intervention with IT or logistics. So, GenAI transformed how businesses accessed data, but true end-to-end execution was still missing.

4. The Age of Autonomous Agents (2024/2025): AI as an Assistant, Not Just a Tool

As Amit Zavery (ServiceNow President, CPO and COO) pointed out, “Agentic AI is the new frontier.” They have come to change not only the human-machine relationship, but also the very logic of business platforms. We know that they don’t just assist but autonomously execute tasks across business functions. So, instead of multiple AI systems working separately, a single agent can reason, retrieve information, and act independently.

In McDermott’s words, “With just a front-end AI agent, a customer is limited in their ability to take action across the enterprise to drive the ticket to resolution.” For example, a telecom AI agent can diagnose connectivity issues, initiate technical support, and offer refunds—all without human intervention.

But to work like this, business platforms must leave the old, siloed logic for an end-to-end platform where business processes are fully integrated and automated across all departments.

Why AI agents need an end-to-end business platform?

Now, AI agents mark the transition toward an end-to-end business platform logic. The reason for this shift is because of three basic agents’ requirements. These are:

Unified Data Access: Agents need real-time, company-wide information to make decisions.

Cross-Department Automation: Instead of separate workflows, AI agents manage multi-step, multi-system processes.

Autonomous Decision-Making: AI agents don’t just recommend actions—they execute them across different business functions.

What will happen with CRM and other business platforms?

Now, with the shift to end-to-end logic introduced by the AI agents, it seems that traditional CRM, ITSM, HR, and ERP platforms will no longer function as standalone systems. Instead, they will converge into unified AI-driven platforms, reshaping how businesses operate.

This doesn’t mean that business platforms will end to exist, but businesses will have to change the siloed logic of their platforms. For example, CRM will no longer be just a sales and service tool—it will integrate AI-driven workflows that connect marketing, sales, IT, and customer service in real time.

With the shift to end-to-end logic, traditional CRM, ITSM, HR, and ERP platforms will no longer function as standalone systems

In this case, enterprise AI agents will handle entire customer journeys, from lead generation to issue resolution, without the need for human handoffs. On the other hand, ITSM and HR platforms will likely transition from isolated service desks to enterprise automation hubs, where AI can predict needs, resolve issues autonomously, and connect workflows across finance, legal, and operations.

Finally, ERP systems will evolve from passive record-keeping tools into AI-driven decision-making engines. So, instead of just storing financial and operational data, they will dynamically analyze and optimize business processes, allocating resources in real-time based on enterprise AI-driven predictions.

Bridging the Gap: How Businesses Can Transition to AI Agent-Driven Platforms?

Now, the future of business platforms isn’t about better CRM, ITSM, or ERP individually—it’s about a fully connected, enterprise AI enterprise ecosystem. In this sense, as always, organizations that adapt will gain unmatched efficiency, speed, and automation.

Companies like Salesforce and ServiceNow are already shifting toward AI-native platforms, where AI agents don’t just assist users but actually execute tasks. In short, the competition is now about who will build the most integrated, intelligent enterprise system, where agents can operate freely across all business functions.

However, the shift from siloed to an end-to-end enterprise AI platform isn’t an overnight switch. So, businesses need a hybrid approach—where AI agents coexist with existing systems while progressively reshaping workflows.

Besides, organizations must also bear in mind two common enterprise AI challenges. On the one hand, to align these technologies with their goals and needs to ensure ROI. On the other, to prevent the security risks that these systems could have, like data exposure, poisoning, prompt injection, etc.

But don’t worry. At Inclusion Cloud we can help you out. Let’s schedule a meeting and discuss how we can future proof your business for the next AI innovation wave. And don’t forget to follow us on LinkedIn for more AI insights and industry trends!