The AI asset lifecycle: Managing AI for long-term success

AI asset lifecycle: man sitting at a desk and smiling at an open laptop

Nearly two-thirds (65%) of the 4,470 global leaders ServiceNow surveyed for the Enterprise AI Maturity Index, in partnership with Oxford Economics, reported positive returns on AI investments. Yet only 23% saw gains exceeding 15%. Executives are concerned about regulatory compliance, data privacy, and intellectual property risks.

Addressing these challenges requires clear AI governance and continuous tracking of AI asset key performance indicators. While some organizations are already treating AI as a strategic asset, further steps are needed to ensure it receives the same diligence as other critical assets.

The emphasis should be on AI lifecycle management, which prioritizes maintaining model relevance, sustainability, and alignment with business value over time. This can help ensure AI models fulfill their initial purpose and adapt as data, technologies, and business objectives evolve.

Let’s explore the stages of the AI asset lifecycle (see Figure 1).

AI asset lifecycle: AI ideas, demand prioritization, development cycle, operationalization, maintenance, retirement Figure 1: Stages of the AI asset lifecycle

1. AI ideas

The AI asset lifecycle begins with a centralized idea collection store for the problems related to automations, predictions, and recommendations that AI can potentially solve. This is crucial to make sure no idea gets missed. Additionally, this allows for collaboration and refinement of ideas by different teams.

For example, a marketing team may contribute an idea for an AI chatbot to enhance product awareness while a customer support team submits an idea to improve customer service and a data science team suggests an AI algorithm to optimize product recommendations. Centralizing these ideas in a single location makes it easy to review, combine, and implement them in a cohesive manner.

With this repository in place, the process of ideation and innovation becomes more efficient and effective, leading to the development of cutting-edge AI assets that can truly make a difference.

2. Demand prioritization

Once the ideas have been collected, they can be evaluated based on feasibility, impact, and alignment with the broader organizational strategy. Then an initial data availability assessment can determine the presence of sufficient signals for generating predictive or prescriptive insights.

This is where strategic value is initially identified and enables organizations to make targeted investments in AI programs that hold the greatest significance.

For example, a marketing team building a product recommendation model might focus on long-term strategic goals such as increasing customer loyalty and brand recognition. Tactically, this model can enable more targeted marketing campaigns and personalized content, which can drive immediate engagement and increase conversion rates, directly affecting daily marketing operations.

3. Development cycle

The development phase begins with a comprehensive assessment of requirements and follows an iterative approach with repeated cycles of model building, evaluation, and refinement. It involves cross-functional teams, including data scientists, product managers, and domain experts, collaborating to define objectives, scope, and outcomes.

In planning, teams should identify business and data needs while establishing targets for success metrics, such as accuracy, recall, and cost savings. While the model is being developed and tested, it’s important to document system and user testing results in detail for reference for model enhancements on future AI projects.

During this phase, multiple strategic and tactical business value definitions can be assessed and documented as well. Once model accuracy has been confirmed, the model can be deployed to production to interact with live data and deliver value.

Purposefully managing the AI asset lifecycle can maximize long-term business value, aligning AI investments with both strategic and tactical goals.

4. Operationalization

Deploying an AI/machine learning model into production is an important step, but it doesn’t guarantee the model will deliver the expected value at a regular cadence. Operationalization is the process of actively monitoring the model, integrating it into business workflows, and consistently delivering value to users.

Implementing comprehensive monitoring strategies and responsible AI guardrails is vital to ensure models are used effectively and perform as expected. Tracking execution, response time, and accuracy helps teams sustain performance and address issues quickly.

Additionally, providing data provenance by transparently tracking data sets from ingestion to AI-ready structures—such as engineered features and embeddings—is crucial for evaluators, regulators, and users.

Detecting model drift early enables timely retraining, and monitoring business value drift helps assure alignment with strategic goals, enhancing model updates through proactive interventions.

It’s important to monitor prompts and responses in generative AI models to detect offensive content, prevent sensitive information leaks, and address issues such as hallucinations, inaccuracies, and toxicity. These factors add a vital dimension to monitoring in generative and Agentic AI development.

5. Maintenance

After a robust monitoring system is in place, AI assets require regular maintenance to remain effective. As business needs change, AI models must adapt.

Key maintenance includes tracking user feedback in a centralized feedback store. Sharing insights, spotting common issues, and promoting transparency can help teams improve all models.

Additionally, effective AI incident management can aid teams in efficiently addressing issues, prioritizing updates, and preventing recurring problems.

6. Retirement

The final stage of the AI asset lifecycle is retirement, which happens when a model no longer fulfills its purpose or becomes too costly to maintain. This phase should be systematic, involving an evaluation of why the model is being phased out and reflecting on lessons learned.

Key considerations include assessing the model’s performance, as declining accuracy or relevance may indicate it’s time for retirement. By thoughtfully retiring models, businesses can reallocate resources to assets that provide greater value.

Purposefully managing the AI asset lifecycle can maximize long-term business value, aligning AI investments with both strategic and tactical goals. This approach enables organizations to adapt quickly and stay relevant in a rapidly evolving market while adhering to emerging standards from governments and regulatory bodies, such as the:

At ServiceNow, we created AI Control Tower on the Now Platform to manage our AI assets and workflows. This includes Now Assist Guardian and applications for innovation management, demand management, agile management, incident management, creator workflows, and governance, risk and compliance.

Ready to unlock the full potential of your AI initiatives? Find out how ServiceNow helps organizations put AI to work.