Aligning Service Delivery with Enterprise Vision in the Age of AI and Automation
by Muhammad Zaid Musaddeq
Abstract
This paper proposes a dynamic cost model for Managed Service Providers (MSPs) that aligns service delivery with enterprise objectives through a flexible, outcome-based pricing framework. Drawing on the principles of the Spotify Agile Organization Model, the proposed model emphasizes service level agreements (SLAs), delivery agility, and measurable outcomes using story points and Site Reliability Engineering (SRE) metrics. By integrating squad-based pricing with incentives and penalties, the model fosters innovation, efficiency, and transparency while addressing risks such as under-delivery and cost inefficiencies. The framework is designed to support DevSecOps operations, ensuring responsiveness and adaptability to changing business demands. This approach bridges the gap between MSPs and enterprises, promoting a collaborative partnership aligned with strategic goals.
1 Introduction
Managed Service Providers (MSPs) play a critical role in enabling enterprises to achieve operational efficiency and agility in software development and IT operations. Traditional MSP models, such as Full-Time Equivalent (FTE)-based pricing, often lack flexibility and fail to incentivize innovation or efficiency, particularly in leveraging AI for dynamic service delivery. The CMS IT whitepaper highlights the inadequacy of traditional MSP models in the age of AI and automation [Ref-1].
This paper introduces a dynamic model that integrates MSPs into the enterprise vision by aligning pricing with measurable outcomes, fostering transparency, and promoting continuous improvement. The proposed model leverages the Spotify Agile Organization Model, which emphasizes autonomous squads, tribes, chapters, and guilds to enhance collaboration and agility [Ref-2].
The model focuses on two primary service areas: Business-as-Usual (BAU) DevOps/DevSecOps operations and project-based work. It introduces a squad-based pricing structure with story points as the primary delivery metric, supplemented by SRE-focused metrics for availability, bug fixes, and configurations. By incorporating service credits and gain-sharing mechanisms, the model balances cost control with incentives for efficiency and innovation.
2 Proposed Dynamic Cost Model
The dynamic cost model is designed to align MSP service delivery with enterprise priorities, emphasizing delivery quantity, quality, and timeliness. The model is structured around two core components: BAU operations and project-based engagements.
2.1 BAU Managed Service Operations
The BAU component is built on a DevSecOps framework, prioritizing agility through Turnaround Time (TAT) classifications: Immediate (<3 days), Quick (<7 days), and Fast (<15 days). Delivery is measured in story points, which are jointly defined by the purchaser and supplier. Squads, inspired by the Spotify model, are cross-functional teams comprising developers, QA engineers, product owners, and optional SRE and security specialists [Ref-2]. The squad-based delivery, SRE metrics, and automation are key drivers of efficiency and alignment in Application Development and Maintenance (ADM) services, as supported by Cognizant’s Next-Gen ADM Services paper [Ref-3].
The pricing model is squad-based, with monthly costs factored by story point delivery. SRE resources focus on availability, bug fixes, and configurations, with performance tied to SLAs and Key Performance Indicators (KPIs). On top, tools like service credits and gainsharing incentivize efficiency and penalize underperformance- keeping the right balance between parties.
2.2 Project-Based Engagements
For projects, the model proposes a per-squad, per-sprint pricing approach, with sprints lasting 2–4 weeks based on purchaser expectations. Suppliers may transition to a story point-based pricing model once definitions are aligned. Each squad includes a product owner, QA engineer, and other necessary roles, ensuring comprehensive delivery capabilities.
2.3 Key Processes
The model incorporates several processes to ensure transparency and continuous improvement:
- Story Points Catalog: Suppliers must propose a plan for story point definition alignment, reviewed quarterly or bi-annually. Delivery capacity is tracked in terms of story points per squad, as shown in Table 1.
- Demand Forecast and Ramp-Up/Ramp-Down: Monthly forecasts are provided in story points, with suppliers expected to adjust squad sizes within one month.
- SLA and KPI Framework: SLAs and KPIs are updated to reflect the proposed commercial model, with suppliers encouraged to share best practices.
Table 1: Story Points Delivery Forecast
| # of Stories to Be Delivered | Q1 | Q2 | Q3 | Q4 | Q1 |
| Squad 1 | – | – | – | – | – |
| Squad 2 | – | – | – | – | – |
| Squad 3 | – | – | – | – | – |
* Note: Placeholder values to be defined during implementation planning
2.4 Efficiency and Improvement Targets
Vohra demonstrates how AI, NLP, and RPA are being used by MSPs to improve service quality and scalability, validating the inclusion of automation and AI as key efficiency drivers in our proposed model [Ref-4]. These targets should be measured quarterly or bi-annually and should be mutually agreed upon.
Table 2: YoY Efficiency Drivers
| Efficiency Drivers | Y1 | Y2 | Y3 | Y4 | Y5 |
| Automation/ AI | – | – | – | – | – |
| Synergy/Tribe/Squad Velocity | – | – | – | – | – |
| Code Efficiency (LCNC) | – | – | – | – | – |
| Associate/Other Quality Drives | – | – | – | – | – |
* Note: Placeholder values to be defined during implementation planning
3 Risks and Mitigations
The evolution of MSPs over the last 25 years has transformed them from reactive support systems to strategic enablers. The risk of stagnation in traditional models and the need for consultative, AI-driven transformation is highlighted by Sanyal and Simpson in their ZINFI Podcast Series [Ref-5].
Our proposed model addresses several risks associated with traditional MSP pricing models.
Table 3: Commercial Model Comparison
| Model | Risks/Cons | Pros | Mitigations |
| FTE-Based | Fails to incentivize efficiency; quality risk | Granular resource control | – |
| Squad-Based (Story Points) | Undefined story points risk disputes | Payment tied to delivery complexity | Governance forums, quarterly forecast refinement |
| Squad-Based with Incentives | Overstaffing or under-commitment risks | Encourages efficiency via incentives | Balanced incentives, transparent reporting |
Mitigations include governance forums for monitoring, iterative forecast refinement, and penalties for consistent under-delivery (e.g., service credits based on actual deviation after three months).
4 Discussion
The dynamic cost model aligns MSPs with enterprise goals by shifting focus from resource-based to outcome-based pricing. By adopting the Spotify model’s squad structure, the framework promotes autonomy and collaboration. Story points provide a standardized measure of delivery complexity, while SRE metrics ensure system reliability. The model’s flexibility accommodates varying business demands, with monthly ramp-up/ramp-down processes ensuring scalability.
Challenges remain in defining story points and ensuring supplier transparency. The model mitigates these through mutual agreements and regular reviews. Compared to traditional FTE-based models, the proposed approach reduces purchasers’ risk by tying costs to measurable outcomes.
5 Conclusion
The proposed dynamic cost model offers a robust framework for integrating MSPs into enterprise ecosystems. By leveraging agile principles, outcome-based pricing, and continuous improvement mechanisms, it ensures alignment with strategic objectives while fostering innovation and efficiency. MSPs must evolve from traditional service delivery to strategic, AI-enabled partnerships focused on measurable business outcomes. Future work should refine story point definitions and develop standardized metrics for automation-driven efficiency gains [Ref-6].
References:
- CMS IT Services. (2025, March). AI-powered automation in managed services: Building smart solutions. https://www.cmsitservices.com/wp-content/uploads/2025/03/CMS-IT-Services-AI-Powered-Automation-in-a-Managed-Services.pdf
- Kniberg, H., & Ivarsson, A. (2012, October). Scaling Agile @ Spotify with Tribes, Squads, Chapters & Guilds. Crisp. https://blog.crisp.se/wp-content/uploads/2012/11/SpotifyScaling.pdf
- Cognizant. (2023, September). ISG Provider Lens™: Next-Gen ADM Services – Agile, DevOps, and SRE in Application Development. https://www.cognizant.com/en_us/services/documents/isg-provider-lens-next-generation-adm-services.pdf
- Vohra, D. (2025, October). How managed service providers are using AI automation in 2025. NexGen Cloud. https://www.nexgencloud.com/blog/case-studies/how-managed-service-providers-are-using-ai-automation
- Sanyal, S., & Simpson, E. (2025, July). The future of managed service providers in the age of AI and automation [Podcast episode]. ZINFI. https://www.linkedin.com/pulse/future-managed-service-providers-age-ai-automation-sugata-sanyal-p216c
- Technology Services Industry Association. (2025). Navigating the shift to outcome-based value: State of managed services 2025. TSIA Research. https://cdn.prod.website-files.com/65932a5f2ab5244b61f0cc94/685dc77b5a803248938bfe45_TSIA_State%20of%20Managed%20Services%202025_Ebook.pdf
