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"Build vs. Buy: A Guide to Implementing AI in Complex Service Operations"

Across industries, business leaders face mounting pressure to operationalize AI despite challenges in stalled deployments, talent shortages, cost overruns, and mounting technical debt. According to McKinsey, only 1% of internal GenAI rollouts are considered mature, and Gartner notes that 80% of AI proof-of-concept projects fail to reach full production.

A new white paper, published by Aquant, shows business and technology leaders these hurdles are not technical — they're organizational. Aquant is a leading provider of service intelligence software, purpose-built to help organizations capture expert knowledge, scale frontline performance, and reduce operational inefficiencies through AI. 

Drawing on insights from real-world case studies, the guide outlines why more companies are abandoning internal builds in favor of proven, scalable platforms.

Business leaders downloading the white paper will gain actionable insights in four critical areas for driving in-house progress alongside strong third-party partnerships in AI deployments

1. Accelerating time-to-value with ready-made, service-specific AI:

Commercial platforms are designed to go live in weeks — not months — so teams can realize measurable improvements in efficiency, accuracy, and service outcomes without getting bogged down in long development cycles.

2. Reducing risk and cost by avoiding technical debt and failed internal prototypes:

Internal builds often lead to unexpected delays, escalating costs, and resource strain. Buying a mature platform eliminates these risks with built-in scalability, compliance, and expert support.

3. Scaling service team performance through embedded expert guidance:

Specialized AI platforms capture and deploy tribal knowledge at scale, enabling newer technicians and frontline workers to perform with the accuracy and efficiency of seasoned experts

4. Staying compliant and competitive with continuous updates and governance features:

Enterprise-grade platforms include role-based access, secure infrastructure, and regular updates to meet evolving data privacy and AI regulations, allowing organizations to innovate without compromising compliance.

Offered Free by: Emerj Artificial Intelligence Research
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