Why AI adoption is a climb (and how to avoid getting stuck in the valley)

It usually starts with good intentions: a team tests a generative AI tool, or a department launches a proof of concept around predictive analytics. The pilot delivers some interesting results, but then everything slows down and progress stops.

Why?

• Lack of alignment: Pilots often live in isolation, disconnected from broader business priorities.
• Unclear ownership: Without cross-functional buy-in, no one drives the initiative forward.
• Technology-first thinking: Teams focus on tools rather than use cases, creating cool demos instead of scalable impact.
• Missing the basics: Data quality, governance, or integration challenges make it difficult to move beyond experiments.

The result: organizations get stuck in endless pilots and testing, not failing completely, but also not moving forward.

The AI adoption journey: a climb, not a straight line

AI adoption is not a linear checklist. It’s more like a climb up a mountain, where each altitude brings new challenges, but also new perspectives. You don’t go from basecamp to the summit in a single leap. You prepare, go up in stages, and rely on expert guidance to reach the top safely.

Here’s what the climb looks like when you follow the 9altitudes framework.

1. Discover (Basecamp)

Every successful climb starts with preparation. At basecamp, companies explore where AI can deliver the biggest impact. Before jumping into tools, they take time to understand where AI can really make a difference. At this discovery stage, it’s good to look at your main business challenges and see where automation or prediction could add value.

Skipping this step is like climbing without a map. You might move fast at first, but you risk heading in the wrong direction.

Want help spotting opportunities? 9altitudes offers AI readiness assessments and inspiration sessions.

2. Ignite (The First Ascent)

Early wins matter. They build confidence and momentum within the organization. That’s why we encourage companies to begin with simple use cases that show results fast. For many industrial companies, this could be using generative AI or Copilot for summarizing reports or drafting customer updates.

With the right training and change management, teams quickly experience the productivity benefits of AI: faster reporting, smarter communication, and better analysis. These wins are the first meters of altitude gained, and they motivate everyone for the harder climb ahead.

9altitudes provides Copilot training and change support to get teams on board quickly.

3. Extend (The Mid-Climb)

Once teams see the potential, the next step is weaving AI into real workflows, or in other words: scaling from pilot to process. This is where organizations often struggle on their own.

We recommend looking at repetitive tasks like invoicing, order tracking, or onboarding. Automating these processes frees up people for more valuable work. Test, adjust, and then scale step by step. Through iterative build–test–deploy cycles, AI becomes embedded into daily operations. Now, the climb starts to feel real: the organization is no longer just experimenting with AI, but actually working with AI.

9altitudes helps organizations design and deploy custom AI models for industry-specific challenges.

4. Elevate (The Summit Push)

Once the basics are in place, explore how AI can create deeper insights. Standard, out-of-the-box agents are rarely enough for industrial environments. To get real impact, you’ll need tailored models that fit your processes and data.

Predictive models, anomaly detection, or computer vision can reveal patterns that people might miss. Use IoT data to forecast maintenance, improve quality control, and reduce downtime. This is where AI moves from a support tool to a true business driver.

9altitudes helps organizations design and deploy custom AI models for industry-specific challenges.

5. Empower (Peak Performance)

Reaching the summit means more than running a single use case. The real value comes from combining everything into a cohesive ecosystem. At this stage, companies bring together agents, copilots, and custom models into one intelligent fabric.

For example, a predictive maintenance agent can connect IoT sensors with ERP data, automatically generate work orders, and trigger follow-up workflows. In this way, AI evolves from isolated tools into a true strategic capability.

At this stage, 9altitudes works with you to create AI opportunity maps and governance frameworks for long-term success.