Paradigm Shift: What Makes an App Intelligent?

Most enterprise AI is theater. It's just chatbots slapped onto legacy systems that change nothing fundamental. Real intelligent software, on the other hand, requires rebuilding from the ground up with AI as the operating core. That architectural choice will separate winners from losers in the next decade.

The Broken Promise of Modern Software

If you’re like me and spend about 1.8 hours per day searching for information, you're losing approximately one full day per week.  That’s a full day every week, just hunting for data that should be at your fingertips.

Enterprise software promised to automate workflows, streamline processes, and centralize information. Yet knowledge workers still click through multiple systems, hunt across databases, and manually piece together insights before taking action.
Most enterprise applications remain static, menu-driven structures. Think dashboard, then listing page, then details page. Want information? Navigate the maze. Need to analyze something? Do it yourself. The pattern repeats across nearly every business system.

That isn't automation. It's digitized manual labor.

Why Layering AI on Top of Apps Falls Short

The current approach is predictable: Take an existing system, add a chatbot interface, call it intelligent. Google added "Help me write" to Gmail. Enterprise platforms did the same. The underlying architecture remains unchanged.

This approach fails for three fundamental reasons.

When information lives in silos, AI works with fragments. Think about email. We communicate face-to-face, in meetings, through documents, in chat. Email is just a small part. When AI only sees the email thread, it misses the true context. Such tools aren't very useful.

AI suggests actions but requires humans to execute them. You've traded one type of work for another, often adding cognitive overhead.

When AI is layered over legacy systems, it doesn't change how people work. It adds friction.

The Paradigm Shift to AI As The Operating Core

There's finally a chance to redesign systems from the ground up, to make them truly adaptive. But doing so requires rethinking software architecture fundamentally.

We need applications where AI is the core operating system, not an optional add-on. And we need to centralize data or give AI access to different sources. By combining everything into one context, we can create insights, develop strategies, and automate processes.

This requires four essential capabilities:

Intelligent HR Management as a Practical Example

Let’s see what this looks like in practice.

Traditionally, managers wanting to understand employee performance look at one-on-one notes, team discussions, spreadsheets, and HR systems. They analyze everything to identify who's struggling and it's time-consuming work.

With AI at the core accessing all these sources, something new can happen. The system continuously scans employee performance data, feedback notes, attendance logs, and compliance records. When it identifies an employee with declining review scores, frequent sick leaves, and policy breaches, it doesn't wait. The system proactively presents a dashboard with employees, their performance scores, recommendations, and specific actions to take.

Click on actions for someone with low performance, and the AI can:

• Draft a performance letter for your review

• Recommend training sessions or coaching with talking points

• Flag policies that underperforming employees need to re-read and test their understanding

• Automatically schedule coaching sessions

The AI takes action based on the situation. Your job shifts from information gathering to reviewing, approving, and having the actual conversation, the only part that requires human judgment.

Addressing The Real Challenges

Let's be direct about the obstacles. Rebuilding systems with AI at the core isn't simple, and the concerns are legitimate.

The Cost Question

Yes, building AI-first systems costs more upfront than adding a chatbot to existing software. However, the economics favor this approach long-term. McKinsey research shows that companies implementing generative AI across multiple functions see 3-5 percent  productivity gains in the first year alone.  More critically, layered AI creates ongoing inefficiency costs because at first, you have to maintain legacy systems and AI layers.

Strategic product development that places AI at the core eliminates redundant systems rather than adding to them. The ROI also comes from this workflow elimination.

Integration and Data Governance

Connecting AI to multiple data sources raises valid security and privacy concerns. The key is treating AI access like you'd treat any system integration with proper authentication, data encryption, audit trails, and role-based permissions.

Unlike traditional integrations that move data between systems, modern AI architectures can query data in place, reducing exposure. The system accesses only what each user is authorized to see, maintaining existing permission structures while providing unified context.

For regulated industries, this actually improves compliance. When AI operates at the core, you have centralized logging of all actions and decisions, creating better audit trails than scattered human activities across multiple systems.

The Human Oversight Balance

"Won't this remove human judgment where it's needed?" The opposite is true. AI at the core redirects human judgment toward higher-value decisions.

In our HR example, the AI doesn't terminate employees or make final performance decisions. It surfaces the situation, drafts materials, and automates scheduling. The manager still conducts the conversation, makes judgment calls, and handles exceptions. The difference is they spend 80% of their time on these high-value activities instead of 20%.

The architecture should include escalation rules: when confidence is low, when stakes are high, when policies conflict, the system routes decisions to humans. This is intelligent task allocation.

Reimagining Software Interfaces with MCPs

Here's where things get interesting for product design.

Traditional software architecture assumes users navigate to each application. You open Jira for tasks, Salesforce for customers, Gmail for communication. Each has its own interface, authentication, and navigation paradigm.

The Model Context Protocol (MCP) is changing this. MCP is an open standard that allows AI systems to connect to multiple applications to fetch data.

What This Means in Practice

Anthropic demonstrated this with their Claude integration: instead of opening multiple apps, you work from a single AI interface that can access various systems and render interactive components from each.

For example, working with Jira through MCP you can ask: "What tasks am I working on this week?", get interactive task cards showing details, time logs, and comments, and then update the ticket status, log time, reassign—all without opening Jira.

Shopify takes this further. Their MCP implementation lets you search products across all Shopify stores, see interactive product cards with images and specifications, add items to cart, and complete purchases entirely within the AI interface.

Why This Connects to AI-At-The-Core Architecture

MCP is the interface layer that makes AI-core systems practical. When AI operates as your core system, you need a way to interact with the insights and actions it generates without rebuilding every application's UI.

Think about our HR example again. The AI identifies performance issues and recommends actions. With MCP, it can:

• Pull employee data from your human resource information system (rendering relevant fields)

• Show calendar availability from Google/Outlook (with booking interface)

• Generate a performance improvement document (with editing tools)

• Create a coaching ticket in your learning management system (with tracking status)

These are  presented in one coherent view, regardless of which systems store the underlying data.

The Interface Layer of the Future

This suggests a fundamental shift in how we build and use business software. Instead of each application building its own frontend, applications expose their functionality through MCP-compatible interfaces. Users work from their preferred AI environment and access everything they need through dynamically generated components.

We're seeing early examples now, but this will mature rapidly. The implication for software development is profound: building applications increasingly means building data models, business logic, and MCP connectors rather than full-stack applications with custom frontends.

This is the first time we're seeing an API standard that can both retrieve data and generate appropriate interface components for that data. Combined with AI at the core, it completes the vision of truly adaptive software.

Why the Transformation Matters Now

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.  We're going to see a significant gap between companies that effectively integrate agentic workflows and those that don't.

Companies implementing these systems effectively will have a considerable competitive edge.

It's the right time to learn more about this, even though technologies aren't fully mature. People don't know how to do all of this perfectly yet. But it doesn't matter.

Get your teams to experiment now. Start with a single workflow that's data-intensive and rule-based, whether it is expense approvals, lead routing, inventory reordering or something else. Build a pilot with AI at the core, not layered on top.

Any knowledge you gain about AI architecture and implementation will position you far ahead of competitors. The window for competitive advantage is narrowing as these capabilities become more accessible.

Building Applications for the Next Decade

The distinction between AI as a layer and AI as a core is philosophical. It's the difference between tools that help humans work faster versus partners that work alongside humans.

With AI at the core, your apps evolve from record-keepers to decision partners. The transformation doesn't eliminate human judgment. It redirects it toward strategy, relationship-building, and creative problem-solving instead of data gathering and routine follow-ups.

The promise that SaaS made twenty years ago of automating workflows and freeing knowledge workers from repetitive tasks is finally becoming possible. But only for those willing to rebuild software with AI at the core, not layered on top.

Teams who build on this blueprint will shape the next decade of B2B software.

Jeremie Tisseau
Chief Design Officer

With over 17 years in UX design, Jeremie grew a web agency into a leading Thai consultancy. He offers services like product design, user research, and UX/UI design to major clients, including Mastercard, Agoda, and Central Group.