What Enterprises Should Look for in an AI for Managed Service Provider
Artificial intelligence has firmly stepped beyond the confines of pureplay technology companies and research labs to stride purposefully as an operational core in organizations across industries — large, medium and small.
What we do find, though, is a definite gap between intent and execution. The constraints and barriers to realizing AI’s potential and value are significant — more so, when companies look to build in-house AI capabilities. This causes a definite distraction from their core business, as they have to develop a specialized technical talent force and build the right and relevant infrastructure. This is where working with the right AI for managed service provider helps bridge the gap between vision and execution through trusted AI service management practices.
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Such service providers specialize in integrating AI for the business ‘sans’ the headaches of multiple overheads. AI managed services deliver the full range of AI lifecycle services — including data aggregation, ingestion, cleansing and analysis. The value of AI managed services lies in the ability to deploy insights in relevant models in production environments, build tailored large language models (LLM) and deploy AI agents.
In short, AI managed service providers offer advanced and sophisticated AI capabilities, without the organization having to invest in infrastructure and expert talent. Plus, they provide the advantages of scalability, security, and cost-efficiency.
Choosing the right AI Service Management
In deciding to adopt the right AI managed services provider, organizations should look at the right blend of domain and technical depth, operational expertise, understanding of the organization’s goals, and strong strategic and implementation capabilities. Key parameters to consider include:
- 1. Proficiency in deployment of customized LLMs, emerging tools and technologies, integration of APIs, and artificial intelligence service management across diverse data sources and storage systems
- 2. The right talent composition in their managed AI services ecosystem — do they have the right blend of AI/ML engineers, data scientists, prompt engineers, DevOps and MLOps specialists, and industry experts?
- 3. Ability to offer platform flexibility — can they build on build on open standards and modular frameworks to ensure ownership of important assets, and the flexibility to move to in-house teams or other external partners in the future?
- 4. Flexibility to handle real-time performance requirements with auto-scaling, as well as high-volume batch workflows.
- 5. Service level guarantees on model performance, frequency of retraining, rollback of versions and compliance
- 6. Verifiable evidence of track record — such as case studies, retention by clients and certifications
- 7. Approach to privacy, governance, and security — and how they implement their strategy into practice
- 8. Flexibility and reasonableness of pricing models — are they willing to have a skin-in-the-game approach?
Learn More: How is AI Transforming Enterprise Data
Without a doubt, choosing the right AI managed services provider is a high-stakes decision. But done right, you will have a partner who can translate and transform your vision to reality, collaborate as a ‘One-Team’ with your people, act as an extension of both your strategy and operation, and significantly bolster your capabilities and outcomes. But this will be possible only by drawing up relevant and purposeful goals, meticulously defining the scope of activities, designing the right evaluation process (both for selection of the provider and for outcomes achieved).
With a clear roadmap, enterprises can unlock the full potential of AI managed services and achieve long-term value through strategic artificial intelligence service management.
