Beyond deployment: Embracing AI sustainment for lasting value
Written by Jens Eriksvik, Kristofer Kaltea & Peter Wahlgren
Deploying AI systems is just the beginning. To create business impact and realize value, these systems must be sustained to remain reliable, adaptive, and compliant over time. AI sustainment is a strategic approach to extend the lifecycle of AI models, ensuring they are performant, scalable, and aligned with business needs. Algorithma emphasizes a proactive methodology that continuously improves models, manages data effectively, and follows responsible AI guidelines—maximizing value and maintaining a competitive edge.
Building on our earlier insights into managing and maintaining AI systems, effective AI sustainment requires a framework that covers data, models, infrastructure, and cost-efficiency.
"Deploying AI is just the beginning—true business impact comes from how you sustain and adapt these systems over time. Our approach at Algorithma ensures that AI remains not just functional, but valuable and scalable."
- Jens Eriksvik, CEO at Algorithma
Data management is foundational for AI sustainment. It includes data quality assurance—ensuring integrity and consistency through cleansing and validation—and robust data governance for responsible collection, storage, and compliance.
The sustainment framework builds on continuous learning, model improvement, and performance monitoring. Regular model retraining helps AI systems adapt to evolving data patterns, maintaining optimal performance and accuracy over time, aligned with business needs. Proactive performance monitoring through real-time metrics and alerts enables quick interventions to sustain reliable outcomes.
Infrastructure plays a critical role in supporting AI systems. Flexible architecture design allows efficient workload handling, and strategic resource allocation ensures scalability, performance, and compliance while reducing costs and maximizing return on investment.
Responsible AI practices underpin the framework. Regular audits for bias mitigation and transparency techniques foster trust in AI decisions and ensure fairness. Specialized expertise from AI engineers, data scientists, and domain experts is essential to sustain models, manage infrastructure, and uphold responsible AI practices.
"Effective AI sustainment requires a blend of deep technical expertise and practical business insight. Our goal is to ensure that AI not only operates efficiently but aligns with core business objectives and delivers measurable results."
- Kristofer Kaltea, Business Operations Leader at Algorithma
Effective AI sustainment requires cross-functional teams that merge development, operations, and business expertise. These teams maintain robust, scalable models aligned with business objectives. Organizations can build these capabilities in-house or partner with AI service providers for the necessary skills and support.
Unique challenges in AI sustainment
AI sustainment demands more than technical skills—it requires strategic problem-solving. Common challenges include:
Complex infrastructure and rapid deployments: Implement CI/CD pipelines for agile deployments, reducing complexity and accelerating updates.
Managing data drift and bias: Monitor models for drift and bias, supported by strong data governance frameworks.
Balancing security and performance: Protect data using industry cybersecurity standards while maintaining model efficiency.
Skill gaps and talent development: Upskill internal teams or hire strategically for AI and MLOps expertise to maintain sustainment capabilities.
Cost management and ROI: Demonstrate AI sustainment’s financial benefits, showcasing cost savings and increased revenue for sustained support.
By addressing these challenges, businesses ensure long-term AI value and adaptability.
Best practices in AI sustainment: embracing DevOps and data-centricity
AI sustainment maximizes the uptime and reliability of AI systems, enhances performance and adaptability, and fosters trust through responsible practices. The process requires a disciplined, data-centric approach rooted in DevOps principles:
CI/CD pipelines for data processes: Automate continuous updates and model retraining to keep AI systems aligned with business needs.
MLOps practices for tracking performance: Proactive monitoring helps identify data drift and degradation, enabling timely interventions.
Scalable and cost-efficient infrastructure: Design flexible architectures for dynamic workloads and cost-effective scaling.
Cross-functional teams for seamless operations: Blend infrastructure management, AI engineering, and business expertise to foster continuous improvement, rapid deployment, and transparent reporting.
Sustainment success is tracked through SLA-aligned KPIs, refining processes to maintain and improve performance. Transparency in AI decision-making is essential, enhancing collaboration and building trust across teams.
By embedding these best practices, organizations can enhance the longevity, performance, and scalability of AI systems, reducing costs and ensuring operational resilience.
"AI sustainment isn't just about keeping systems running—it's about continuously refining, optimizing, and aligning them with evolving business needs. At Algorithma, we believe that proactive monitoring and transparent collaboration are essential to drive long-term value."
- Peter Wahlgren, Managing Partner at Algorithma
AI sustainment in practice: four key areas
AI sustainment focuses on practical strategies across four key areas:
Development and enhancements: Continually update and retrain AI models to adapt to changing business needs. Measure success by tracking model performance and alignment with business goals.
Solution operations and maintenance: Monitor systems constantly to fix issues quickly and ensure scalability. Success is measured by availability, response times, and incident management.
Support services: Address complex issues with expert teams to ensure reliable system performance. Track resolution speed, system stability, and learnings from incidents.
Collaboration and communication: Foster clear communication and transparency across teams. Measure success by collaboration effectiveness, knowledge sharing, and trust in AI decisions.
Common consequences of inadequate sustainment of AI systems
While deploying AI systems can deliver immediate value, the failure to sustain and maintain them leads to a range of detrimental outcomes, diminishing the overall impact of AI initiatives. Key risks of neglecting AI sustainment include:
Model degradation and performance loss: AI models are trained on historical data, and over time, the data patterns they rely on may change—a phenomenon known as data drift. If models are not continuously retrained and monitored, their accuracy, relevance, and performance will degrade. This results in unreliable predictions and poor decision-making, which can undermine business processes that rely on AI outputs.
Increased operational costs and disruption: Without proactive AI sustainment, systems may require reactive, costly interventions. Unforeseen downtime, performance failures, or inaccuracies lead to disruptions in operations, causing a ripple effect across business units. Fixing outdated models can be more resource-intensive than maintaining them regularly, and the impact on productivity can escalate quickly.
Regulatory and ethical risks: The regulatory landscape around AI is constantly evolving, with increasing emphasis on fairness, transparency, and ethical practices. Models that are not regularly audited for compliance can introduce biased outcomes or violate legal standards, risking not only fines but also significant reputational damage. Trust in AI decisions erodes when businesses fail to ensure responsible, up-to-date practices.
Missed business opportunities and competitive disadvantage: AI is a fast-evolving field, and unsustained models can lag behind in terms of adaptability and innovation. If businesses do not invest in continuous model improvement, they may miss opportunities for new insights, personalized services, or market advantages. Sustained AI models are critical for responding to changing customer needs, competitive dynamics, and emerging trends.
To avoid these risks, businesses must adopt a proactive AI sustainment strategy that includes continuous learning, regular performance monitoring, and alignment with evolving business needs. By doing so, AI systems can remain reliable, scalable, and compliant over time, ensuring long-term value and impact.
How to get started with AI sustainment
Building a successful AI sustainment strategy requires a clear focus on actionable steps and establishing the right foundation. Here is a practical approach to getting started:
Assess current AI operations
Begin by evaluating the state of your existing AI systems. Identify strengths, gaps, and areas for improvement in model performance, data management, and infrastructure. This assessment helps clarify the baseline for sustainment efforts and ensures you align the strategy with business goals.Develop a sustainment framework
Create a clear structure for AI sustainment that outlines how models will be monitored, maintained, and updated over time. Define a framework that includes data quality assurance, performance monitoring, regular model retraining, and infrastructure scalability. Incorporate responsible AI practices, such as transparency and bias mitigation, into the framework to build trust and accountability.Establish dedicated teams and processes
Form cross-functional teams that combine expertise in AI development, operations, and business. These teams should be equipped to handle ongoing model improvements, support services, and transparent communication. Develop processes for continuous feedback, CI/CD pipelines, and clear escalation paths for complex issues. Decide if these capabilities will be built in-house or leveraged through external partnerships.Decide on in-house vs. outsourced sustainment
Analyze whether to develop sustainment capabilities internally or to outsource to specialized partners.In-house AI sustainment allows for direct control over processes, deeper alignment with business needs, and faster iterations due to closer integration with existing teams. However, it requires significant investment in talent, training, and technology, making it a better option for companies with strong AI and operational capabilities or those needing highly customized solutions.
Outsourced AI sustainment provides access to specialized expertise, faster implementation of best practices, and often a more cost-effective model due to economies of scale. External partners can handle complex tasks like model retraining, infrastructure management, and compliance. This option works well for companies that need rapid scaling, have limited internal resources, or prefer to focus on core business activities while entrusting sustainment to experienced AI service providers.
Hybrid approaches can also be considered, where some aspects (e.g., data governance) are managed in-house, while others (e.g., model optimization or 24/7 support) are outsourced. The key is to match the sustainment model to your organizational goals, internal capabilities, and the complexity of the AI systems in use.
Set measurable KPIs and monitoring systems
Identify key performance indicators aligned with SLA commitments and business objectives. Implement real-time monitoring systems to track model performance, data drift, and system health. Use this data to refine sustainment processes iteratively, ensuring that improvements are timely and impactful.Implement proactive support and governance
Establish support services that provide rapid response to incidents and system issues. Document all resolutions and learnings to improve system reliability over time. Develop governance policies for data integrity, security, and responsible AI use, providing clear guidelines for sustainment teams.Plan for scalability and cost-efficiency
Design your infrastructure to be flexible and scalable, ensuring it can meet changing demands and business growth. Strategically allocate resources to optimize costs without compromising system performance. Align infrastructure decisions with long-term business goals to ensure cost-effective and reliable AI sustainment.Promote continuous improvement and collaboration
Sustainment is not a one-time effort. Foster a culture of ongoing improvement, where teams regularly assess system performance and explore ways to enhance AI models and operations. Encourage knowledge sharing and collaboration between teams, ensuring all stakeholders are aligned and engaged in the AI sustainment process.
By following these steps and carefully evaluating whether to sustain AI in-house or outsource to expert partners, businesses can lay a solid foundation for effective AI sustainment. This ensures AI systems remain robust, scalable, and aligned with strategic objectives, delivering long-term value and adaptability.
