Strategic deployment
How to build and or deploy a private instance of generative AI
Researching and planning
Proof of Concept
Deployed to production
See the value but not started yet
Do not plan to use generative AI models
Enterprises are not passive onlookers but active participants in shaping the potential of generative AI to fit their unique operational needs.
Those who transition from a public generative AI model to a private instance are tasked with creating a strategic approach to operationalization that covers the following areas: scalability, disaster recovery, high availability, cost, customization and integration into existing applications.
“My primary advice is to be deliberate in your focus,” says Juan Orlandini, CTO of Insight North America. “Whether you’re crafting AI models from the ground up, embedding AI as a feature in existing products, or tailoring pre-built models to your specific needs, it’s critical to pursue these efforts with clear intent and purpose. Specifically, focus on the business outcome, measure the value as you deploy and iterate with that data.”
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Key benefits
Private, secure AI operations within your Azure subscription
Real-time productivity boosts with a web-based chat app
Integrated cost management and performance insights
Expertly guided two-week implementation Learn more
Assessment and planning: Begin with a Strategy Workshop to align generative AI capabilities with business goals and desired outcomes. It’s a collaborative effort to map out the enterprise’s vision and identify where AI can have the most significant impact.
Initial deployment and experimentation: Deploy an AI accelerator, such as Azure®️ OpenAI® Service GPT, to explore its capabilities. Hands-on experimentation by cross-functional teams is vital to understand the tool’s potential and limitations.
Integration with knowledge bases: Connect generative AI with the organization’s knowledge base to infuse it with relevant information. This step ensures that the AI outputs are aligned with the company’s data and insights.
Development of a roadmap: Create a comprehensive roadmap for ongoing AI initiatives, including a copilot solution brief that outlines future applications and expected results.
Minimum Viable Product (MVP) development: Start with an MVP to demonstrate the practical benefits of generative AI, allowing for iterative improvements based on real-world use and feedback.
Customizing AI models to enterprise specifics without compromising sensitive data.
Ensuring robust security measures are in place to protect against data breaches and unauthorized access.
Navigating regulatory and compliance requirements, which can be particularly complex in industries like healthcare and finance.
Building trust within the organization by demonstrating the tangible benefits of AI through successful use cases.
Mitigating biases in AI to promote fairness and accuracy in automated decisions.
The business potential of generative AI is best viewed through the lens of real-world applications. Insight, as an early adopter, has used its internal solution InsightGPT to achieve profound operational improvements across the organization:
Human Resources: Automated analysis of employee surveys has saved weeks of manual work and offered deeper insights into workforce sentiment.
Sales: Generative AI has streamlined the categorization of extensive data sets and freed up hundreds of hours for strategic activities.
Project management: The creation of critical project documents like Statements of Work has become more efficient and cut costs by 50%.
Legal: Contract summaries generated by AI have improved efficiency without compromising on detail or accuracy.
Warehouse management: Automated device updates have eliminated concerns about human error and significantly enhanced productivity.
These cases exemplify the breadth of generative AI’s applications and reinforce the importance of following a strategic, step-by-step approach to its implementation.
However, despite their best intentions, business leaders may find it difficult to follow these guidelines. “Many companies need to modernize their data platforms before they’re ready to truly take advantage of the capabilities of AI,” says Mike Gaumond, SVP of Strategy at Insight.Insight Lens™ for Gen AI Accelerator is a proven solution for this purpose.
Insight Lens™ for Gen AI Accelerator expedites the secure implementation of generative AI in business operations. It uses Azure® OpenAI® to easily meet privacy, scalability and compliance needs. It offers a private instance within the Azure environment that enables businesses to realize GPT’s power rapidly and at scale. This solution provides a web-based chat application for desktop and mobile use. Performance insights, user and access management, cost tracking and targeted chat prompts are among the features that help organizations speed up time-to-value.
The process spans over two weeks and is designed to align with business goals and desired outcomes, integrate with knowledge bases, and provide a roadmap for future AI initiatives. This engagement can be extended beyond two weeks for more customized solutions involving complex data integrations and specific use cases.
My primary advice is to be deliberate in your focus. Whether you’re crafting AI models from the ground up, embedding AI as a feature in existing products, or tailoring pre-built models to your specific needs, it’s critical to pursue these efforts with clear intent and purpose. Specifically, focus on the business outcome, measure the value as you deploy, and iterate with that data.
