Moving from pilot to production takes more than just a model

The pace of generative AI adoption is remarkable. McKinsey reports that 79% of organizations have experimented with generative AI in at least one business function (McKinsey & Company). Yet, only 12% of pilots move into production, while 88% stall at the proof-of-concept stage, according to IDC (CIO). The problem isn’t the technology, it’s the AI deployment process.

At Quisitive, after supporting hundreds of AI engagements, we’ve identified a repeatable path to success: Design, Deploy, Enable, Scale. Here’s how each stage helps teams move AI from experiment to enterprise impact.

1. Design for Real Business Value

Too many AI efforts begin as explorations with no clear business outcome. To create a successful AI deployment strategy and scale AI in your business, you need focus. Target 2–3 high-impact use cases that:

• Offer measurable improvements in efficiency or outcomes
• Use available data suitable for AI consumption
• Can be replicated across teams or regions

In our AI Design Lab, we guide stakeholders through a process that aligns use cases with strategic goals, data readiness assessments, and success metrics – ensuring pilots move toward meaningful, scalable results.

2. Deploy with a Secure, Flexible Framework

The next challenge is deployment infrastructure. Many are building ad-hoc solutions that expose sensitive data, create shadow IT, and lack reusability.

Our Airo AI Workspace addresses this by:

• Deploying within your Azure tenant, so no data leaves your control
• Routing requests to multiple models, like GPT-4, Mistral, and Meta, based on cost, performance, or compliance
• Centralizing prompt and agent management with role-based access and auditability

This structure reduces risk and avoids siloed AI efforts.

3. Enable the Right People, with the Right Tools

Success depends on adoption. A workspace is only useful if users understand and trust it.

That’s why Airo supports all user types:

• Business users get intuitive, drag-and-drop builders
• Analysts use script-enabled workflows
• Developers access full SDKs

We deliver guided training on prompt engineering, agent creation, and usage monitoring. Most mid-size firms can operationalize their first agent in under six weeks.

4. Scale with Governance and Feedback Loops

Scaling AI isn’t a one-time launch. It’s an iterative journey. Monitoring usage, model performance, and cost is key.

The Airo AI Workspace includes live dashboards for:

• Model usage and cost tracking (by team or scenario)
• Prompt and agent version control
• User access logs and compliance checks

Admins can refine prompts based on usage, block budgets, and onboard new users with minimal friction. This creates sustainable scaling – no surprises, no missteps.

Why This Approach Works

• 79% of companies use generative AI in at least one function
• 88% of pilots never reach production
• Only 12% succeed in moving to production

This gap shows the outcome isn’t defined by the model. It’s defined by the strategy, infrastructure, enablement, and governance you place around it.