Introduction: Beyond the Hype of AI ROI

The enterprise landscape is saturated with promises of transformative AI. Yet, beneath the surface of widespread adoption, a difficult reality is emerging. Recent industry data reveals a significant gap between AI investment and measurable value. According to a 2025 survey from S&P Global Market Intelligence, 42% of companies abandoned most of their AI initiatives, a dramatic increase from 17% the previous year [1]. Many organizations find themselves in “pilot purgatory,” launching countless proofs-of-concept that never deliver meaningful financial impact.

This high failure rate stems from a common approach: treating AI as a technology project rather than a business solution. Vague promises of ROI are no longer sufficient. Enterprises require a fundamentally different model—one built on accountability, transparency, and a direct link between performance and payment. At Qurrent, we address this challenge with a contractual commitment to delivering guaranteed, measurable outcomes. This article provides a transparent, step-by-step explanation of how our guarantee works, moving from initial discovery and baseline analysis to a formal, outcome-based contractual agreement.

Step 1: Identifying Opportunity and Establishing the Baseline

Every successful AI workforce deployment begins with a deep, analytical understanding of a client’s current operational state. This is the first and most critical phase of our engagement, as outlined in our methodology. Before we can improve a process, we must first measure it. This involves a collaborative discovery process where we work with stakeholders to identify a specific, high-impact operational bottleneck that is ripe for automation.

This is not a theoretical exercise. We gather concrete data to establish an objective performance baseline. For example, in our work with the real estate marketplace Pacaso, this meant measuring the average response and resolution times for customer support tickets. For the virtual world platform Second Life, it involved quantifying the time and resources spent triaging incoming engineering bug reports. This baseline data—whether it’s operational cost, ticket volume, or customer satisfaction scores—becomes the undisputed starting point against which all future performance of the AI workforce is measured. It forms the foundation of our shared understanding of the problem and the benchmark for success.

Step 2: Co-Authoring the KPI Framework and Performance SLA

Once a clear baseline is established, the next step is to define what success will look like in concrete, quantifiable terms. This is a deeply collaborative process where we work directly with customer stakeholders to co-author a framework of Key Performance Indicators (KPIs). These are not generic metrics; they are tailored to the specific business outcome the customer needs to achieve. 

Examples of KPIs we frequently track include:

• • Reduction in operational cost

• • Decrease in first-response time for customer inquiries

• • Increase in automated resolution rate

• • Improvement in customer satisfaction (CSAT) scores

• • Reduction in manual error rates

These co-authored KPIs form the core of a formal Performance Service Level Agreement (SLA). An SLA is a contractual document that defines the level of service and the specific metrics a provider is expected to meet [2]. By embedding these business-centric KPIs into a Performance SLA, we move beyond abstract goals and create a clear, enforceable standard that the deployed AI workforce must achieve. This ensures all parties are aligned on the definition of success before the work even begins.

Our Contractual Promise: The Mechanics of the Guarantee

The agreed-upon KPIs and performance targets are not just internal project goals; they are written directly into the commercial agreement. This is the essence of Qurrent’s ‘contractual guarantee’. Our model is a form of an ‘outcome-based contract,’ where a provider’s compensation is directly tied to the achievement of specific, measurable results rather than the delivery of services alone [3]. This fundamentally aligns our incentives with our customers’ success. We win when you win.

This structure transforms the traditional vendor-client relationship into a true partnership. To ensure accountability, our agreements are built on the principle of a ‘remedy clause’. This clause defines precisely what happens if the AI workforce does not meet the contractually agreed-upon performance targets outlined in the SLA. It establishes shared risk and provides our customers with commercial recourse, ensuring that their investment is protected. This approach to business operations automation de-risks the adoption of AI and ensures that the focus remains squarely on delivering tangible business value.

Proof in Practice: The Qurrent Guarantee in Action

Our case studies demonstrate the tangible results of our contractual commitment to outcomes.

For example, in our engagement with Pacaso, we deployed an AI workforce to augment their customer support for high-net-worth customers. The guaranteed outcomes were centered on improving service quality and efficiency. The AI agents successfully provided 24/7 support, resolved access requests, and intelligently escalated complex issues, leading to improved customer satisfaction and lower operational costs. You can read the full details in the Pacaso case study.

Similarly, for Second Life, we implemented an AI workforce to streamline the management of community bug reports and feature requests. The guaranteed outcomes focused on reducing response times and improving workflow efficiency. The AI agents automated the process of identifying duplicates and providing personalized responses, which significantly improved the experience for their community and the efficiency of their engineering team. The complete results are detailed in the Second Life case study.

These examples serve as tangible proof that a partnership built on a contractual guarantee can and does deliver measurable, impactful results.

Vendor Evaluation Checklist: How to Assess an AI Provider’s Guarantee

As you evaluate potential AI partners, it is critical to look past marketing claims and scrutinize the substance of their guarantees. To help you assess any AI provider’s commitment to outcomes, we recommend asking the following questions, which are based on best practices for evaluating AI vendors [4]:

• • Baseline & Data: Can you show us the baseline data from a similar project? How do you propose to measure our current performance baseline before we begin?

• • KPI Definition: How are KPIs defined, and who from our team will be involved in that process? Are the KPIs tied to business outcomes or technical metrics?

• • Contractual Commitment: Is the performance guarantee written into the contract as a formal Performance SLA? Can we review the specific language?

• • Transparency & Reporting: How do you ensure full transparency in performance reporting? What tools or dashboards will we have access to?

• • Integration: How will your solution integrate with our existing systems? What are the data requirements and governance controls?

Conclusion: A Partnership Built on Performance

In an industry where AI project failures are common, a new model of accountability is required. Qurrent’s guarantee is not a marketing slogan but a structured, contractual framework for a partnership built on performance. By establishing clear baselines, co-authoring KPIs into a formal SLA, and tying our commercial success to our customers’ outcomes, we de-risk AI adoption for the enterprise.

This approach ensures that investments in AI workforces deliver the real, measurable business value that leaders expect. It shifts the conversation from the potential of technology to the certainty of results. We believe this is the only way to build a sustainable future for AI in the enterprise—one based on transparency, accountability, and shared success. To learn more about how a partnership with Qurrent can help you achieve your business goals, we invite you to start a conversation with our team.