Artificial intelligence is rewriting the rules of how technology companies engage, deliver, and earn. For years, the LAER model—Land, Adopt, Expand, Renew—defined customer success in the SaaS era, but that model was built for a world where customers bought tools, not results.

Your customers aren’t paying for access to features in the age of AI; they’re paying for outcomes. They expect measurable business value, not usage metrics. Selling business outcomes requires a different engagement model. To survive and scale, you must evolve from LAER to DARE: Design, Activate, Realize, Evolve—a services-intensive framework built to deliver guaranteed outcomes and continuous value realization.

Key Takeaways

• Outcome, not adoption, is the new objective. Success isn’t about how many users log in—it’s about whether your AI delivers measurable business results.
• Services drive AI profitability. ARGS, VOS, and OOAS redefine services as the engine of value—not the cost center.
• DARE replaces LAER. The new engagement model starts with design, ends with evolution, and makes your ability to deliver and prove outcomes your ultimate differentiator.

Why LAER Can’t Survive the AI Era

The LAER model worked when the software was static. You landed a customer, drove usage, strove for adoption, and hoped for renewal. But AI isn’t plug-and-play—it’s adaptive, dynamic, and embedded into each customer’s unique environment.

AI value isn’t unlocked by adoption; it’s unlocked by integration. Your customers struggle with poor data, legacy systems, unclear strategies, and a shortage of specialized talent. These are not “adoption problems.” They’re transformation problems—and they demand services-intensive solutions.

That’s why the AI era creates an “AI Adoption Chasm,” and the LAER model struggles because it cannot cross it. The primary barriers to AI success are not a lack of features, but deep, systemic hurdles that require intensive services:

• Data and infrastructure: Most enterprises face poor data quality, legacy system integration, and data silos. An AI model is useless without a clean, modern data foundation.
• Talent scarcity: There’s a global shortage of skilled professionals who can build, deploy, and maintain AI systems.
• Strategic ambiguity: Many organizations lack a clear strategy for identifying high-impact use cases and creating a strong business justification, leading to widespread AI Hesitation.

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If LAER was built to measure usage, DARE is constructed to measure outcomes—a critical distinction in a world where your credibility depends on delivering tangible results.

Related: The AI Services Era: Why Services Are Now Your Greatest Advantage

The Shift From Selling Tools to Delivering Outcomes

AI redefines who carries the risk. In a SaaS world, customers pay whether or not the product is delivered and operational. In an AI world, the vendors are on the hook for results.

Companies like Riskified and Hitachi Rail have already crossed this threshold. They don’t sell software; they sell outcomes—guaranteed fraud protection or on-time train performance. That’s the new model: shared risk, shared reward.

But delivering guaranteed outcomes isn’t possible without services. To make AI work in a real customer environment, you must assume ownership of the ongoing operational burden—not just implementation. Once the model goes live, the hard work begins: monitoring drift, maintaining data integrity, securing pipelines, and managing cost volatility.

This is what TSIA calls AI Operational Debt—the silent, accumulating risk that kills ROI when unmanaged. The companies that thrive in this environment are those that build long-term service models to continuously manage that debt.

Related: Why Advanced Services Are Defining the Next Era of AI

Introducing DARE: The New Model for AI-Era Engagement

The DARE framework—Design, Activate, Realize, Evolve—replaces LAER’s linear approach with a cyclical, outcome-driven model built for AI’s perpetual change.

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Design: Engineering for Outcomes (Replaces “Land”)

Forget the one-time motion of closing the deal; start engineering the outcome. The Design phase focuses on AI Readiness and Governance Services (ARGS)—deep consulting engagements that assess data health, define success metrics, and establish governance before a line of code is deployed.

This is where you solve the “last-mile problem.” It’s not about selling a product; it’s about proving your customer is ready to succeed with it.

Activate: Hands-On Deployment (Replaces “Adopt”)

In the AI era, adoption is important, but it’s not the endgame—activation is everything. This is where the Forward Deployed Engineer (FDE) enters. Acting as a hybrid of consultant, engineer, and product expert, the FDE embeds within the customer’s operations to assess, design, configure, test, and deliver the first measurable outcomes.

Early wins drive belief. The Activate phase ensures AI isn’t theoretical—it’s operational.

Realize: Continuous Outcome Proof (Replaces “Expand” and “Renew”)

The Realize phase is where your credibility lives or dies. Expansion and renewal aren’t events—they’re results of continuous outcome realization.

Here, managed services become non-negotiable. Only by owning the operational levers—data integration, infrastructure, AI models—can you guarantee business outcomes and confidently engage in outcome-based pricing.

Realization isn’t about uptime or feature usage; it’s about metrics the C-suite cares about: reduced downtime, higher throughput, improved efficiency.

Evolve: Continuous Value Optimization (The New Phase)

Static is dead. AI demands ongoing vigilance. The Evolve phase introduces Value Optimization Services (VOS)—the continuous management of model performance, data integrity, cost control, and security.

This is where you fight AI Operational Debt in real time. It’s where you transform from a vendor into an operational partner. And it’s where long-term profitability lives.

From Product-Led Growth to Service-Led Growth

DARE isn’t a tweak to LAER—it’s a wholesale replacement. You can’t execute this model through Product-Led Growth (PLG) motions that rely on self-service adoption. Instead, you need Service-Led Growth (SLG)—a model where expert services drive both adoption and revenue.

FDEs and managed service teams aren’t “cost centers”; they’re the value creators that keep AI profitable. They ensure your customers realize outcomes not once, but continuously.

In this world, you’re not selling an AI tool—you’re selling a 15% reduction in unplanned downtime, a 20% improvement in yield, or a 10% cut in operating costs. That’s the kind of value the C-suite will sign for—and renew indefinitely.

Related: Pricing-Led Transformation Under AI Economics

DARE Is the New Operating System for AI Services

The AI era doesn’t reward those who build tools—it rewards those who deliver outcomes. LAER got you through the SaaS revolution. DARE gets you through the AI one.

Your challenge isn’t driving feature adoption; it’s ensuring continuous, measurable outcome realization. That requires retooling your services portfolio, building readiness and governance capabilities, and managing the operational debt that accumulates after deployment.

DARE isn’t a theory—it’s a survival strategy. Companies that embrace it will lead the Outcome Economy. Those that don’t will become case studies.

Related: Why Advanced Services Are Defining the Next Era of AI

FAQs

How does DARE differ from LAER?

LAER focused on feature usage and renewal; DARE focuses on measurable outcomes and continuous value realization. It replaces linear customer success with a cyclical, services-intensive model.

Why are services so critical in the AI era?

Because AI doesn’t run itself. Managing data quality, model drift, cost, and security requires ongoing expertise. Services are no longer optional—they’re your profit engine.

What capabilities do I need to execute DARE?

You’ll need to build AI Readiness and Governance Services (ARGS), Value Optimization Services (VOS), and Outcome-Oriented AI Services (OOAS)—each designed to ensure you can deliver, prove, and evolve business outcomes at scale.