The AI-Centric Era: How Software Is Entering a New Frontier of Intelligence

“AI-Centric” refers to a business model where artificial intelligence is no longer an auxiliary tool but the core operating system of the enterprise—powering products, decisions, and processes across every layer of the organization.
In other words, AI doesn’t just live inside software anymore; it is the software.

If Software-as-a-Service (SaaS) once changed how software was distributed and monetized, AI-centric transformation is redefining what software fundamentally is—and who builds it.
We are entering an age where software no longer waits for human input. It learns, acts, and decides autonomously.

1. The End of SaaS as We Know It

The SaaS revolution was once about convenience: subscription-based software, accessible anytime, anywhere.

But the new revolution—driven by AI—is about autonomy, adaptability, and intelligence.

Modern AI models have evolved beyond supporting roles. They now form the nucleus of software products themselves.
Instead of users prompting systems, AI systems are initiating actions, orchestrating workflows, and driving outcomes.

McKinsey calls this shift the “AI-Centric Imperative”—a strategic necessity for every software provider.
Those that fail to transition risk being outpaced by “AI-native” startups born with automation, data orchestration, and intelligent agents at their core.

2. When ‘AI-Centric’ Becomes Architecture, Not a Slogan

Unlike earlier years, when AI was an add-on, an AI-centric company is built around AI from day one—from its product design to its go-to-market strategy and internal operations.

According to the McKinsey Software CxO AI Impact Survey (Q2 2025):

• 93% of software executives have already reinvented core products with AI,

• 90% have AI-enabled product-development lifecycles,

• 93% are automating internal operations,

• 88% are reskilling and reorganizing talent,

• and 64% have transformed their business models.

The conclusion is clear: AI-centric transformation is not a roadmap—it’s already execution in motion.

3. The Seven Strategic Shifts Defining the AI-Centric Frontier

1️⃣ Reinventing Core Products

Software is no longer defined by features but by contextual intelligence—its ability to understand, reason, and act.

McKinsey identifies three emerging product architectures:

1. Agents as Users/Augmentation – AI operates within existing SaaS systems to augment user capabilities.

2. Agent-Centric Architecture – AI becomes the primary interface, orchestrating logic, data, and execution.

3. Agents as Experts – a mesh of domain-specific agents (finance, legal, marketing) working together under unified orchestration.

Traditional SaaS incumbents gravitate toward augmentation, while AI-native startups are architecting systems entirely around agents.

2️⃣ Evolving Business Models

As AI begins to perform tasks once done by humans, “per-seat pricing” becomes obsolete.
Software companies are moving to consumption-based pricing, charging for actions, tokens, or measurable outcomes.

McKinsey’s data shows that companies adopting this model achieve:

• 2× higher annual revenue growth, and

• 12% higher net revenue retention than subscription-based SaaS peers.

Revenue in the AI-centric era is no longer tied to user licenses—it’s tied to the value AI delivers.

3️⃣ Revamping Go-to-Market Strategies

Selling AI software is not like selling SaaS subscriptions.
Buyers are now CEOs, CTOs, and CFOs demanding tangible business outcomes, not dashboards.

A new hybrid role has emerged: Forward-Deployed Engineers (FDEs)—technical specialists embedded with clients to customize, fine-tune, and operationalize AI in real business contexts.
In parallel, agent marketplaces are rising, where organizations can purchase, test, and deploy AI agents as easily as installing apps.

4️⃣ Redesigning Product Development

AI is transforming the entire product-development lifecycle (PDLC)—from requirement gathering and prototyping to code generation, testing, and deployment.
According to McKinsey, 89% of software companies already apply AI in R&D, boosting productivity by 30–50% and halving time-to-deployment.

5️⃣ Automating Internal Operations

AI is driving massive efficiency gains across internal functions:

• 74% of companies use AI for customer support,

• 58% in sales,

• 54% in back-office functions.

Cost reductions range from 20% to 50%, with the highest savings in customer service.
One HR-software provider, for example, reported 25% higher sales productivity and 80% faster quote generation after introducing AI agents into its workflow.

6️⃣ Building AI-Ready Infrastructure

Many organizations are technologically advanced but not AI-ready.
True AI readiness requires a unified data layer, agent orchestration, and governance frameworks capable of running hundreds of autonomous agents simultaneously.

Nearly 80% of software leaders expect infrastructure spending to rise by at least 10% within a year—an inevitable investment to scale intelligent operations safely and efficiently.

7️⃣ Reskilling and Reorganizing Talent

AI is reshaping workforce composition across the software sector.
McKinsey projects that 30–40% of current roles (IT, operations, support) will be automated, while 15–25% new roles will emerge in engineering, orchestration, and creative marketing.
Overall industry productivity could rise by 20–30% as humans shift from “executing tasks” to supervising, training, and collaborating with AI.

4. From Selling Licenses to Selling Outcomes

AI is redefining value exchange between vendors and clients.
In the past, software was a tool. Today, it delivers results.

AI-centric companies are beginning to price based on performance outcomes—for instance, charging by the percentage reduction in inventory costs achieved through predictive optimization rather than per-user access fees.

5. The New Operating Blueprint: When Agents Become Colleagues

In an AI-centric enterprise, agents are not tools but digital co-workers—each with roles, responsibilities, and performance metrics.
Some leading organizations already operate at a ratio of one employee to three agents, creating hybrid human-AI teams that blend creativity and computational precision.

This evolution demands a cultural shift: people must learn to manage, evaluate, and collaborate with intelligent systems rather than merely operate them.

6. The Financial Upside: Rethinking Margins and Cost Structure

McKinsey’s financial modeling reveals dramatic improvements for AI-centric organizations:

• EBITDA margins could rise by +12–14 percentage points,

• Gross margins from 69–84% to 73–86%,

• Support costs down 50%,

• Marketing and R&D costs down 20–30%,

• while infrastructure spending increases slightly to support compute needs (8–14%).

The result: a leaner, more profitable enterprise where automation drives structural efficiency rather than incremental gains.

7. Why This Matters for Startups, Developers, and Investors

For startups, “AI-centric” is not a choice—it’s a survival strategy.
Without legacy systems, they can build natively around data and intelligent agents from day one.

For developers, value creation now depends less on syntax and more on collaborating effectively with AI systems.
For investors, “AI readiness” will soon become a primary metric in evaluating enterprise resilience and growth potential.

8. Conclusion: Not Every Company That Adds AI Is AI-Centric

AI is redefining the very nature of software.
But adding AI features to a legacy system doesn’t make a company AI-centric.
Becoming AI-centric means re-architecting the organization around intelligence itself.

McKinsey’s verdict is blunt:

“Becoming AI-centric is not optional—it’s existential.”

In the decade ahead, the companies that rebuild themselves around AI, not merely adopt it, will define the next frontier of the software industry.

📘 Source:
McKinsey & Company – “The AI-Centric Imperative: Navigating the Next Software Frontier,” October 2025.