The Path to the AI Summit
On June 2, 1953, the coronation for Queen Elizabeth II was held as she ascended the throne. This was also the day that news broke that Sir Edmund Hillary and Tenzing Norgay had recently summited Mt. Everest. The news of the success was rushed by runner from the expedition’s base camp to a radio post at Namche Bazaar in Nepal and sent by coded message to London.
The summit almost didn’t happen as Hillary and Norgay reached a steep rock step below the summit. Hillary wedged himself into a crack in the face and inched himself up over the course of an hour. This steep rock face right below the summit is now famously called the Hillary Step. While Hillary and Norgay had successfully started their climb months earlier and had sustained themselves through the high-altitude and cold-weather conditions, their effort to scale the mountain required yet another level of skill and endurance.
Artificial Intelligence represents today’s equivalent of reaching new heights. Our point of view is that reaching the AI summit requires climbing three distinct mountains, the Setup, Sustain, and Scale mountains. The scale effort requires teams that have already accomplished a lot to dig deep and meet the next challenge on the best-practices. During the Scale phase, the goal of the organization is to work through the challenges of scaling up the AI momentum, adoption, technology, and community outreach.
The Third Mountain: Scaling Artificial Intelligence
In our eBook, we explore the five steps to ensure you can scale your AI momentum. These include solving for the AI talent gap, scaling up the AI organization and driving adoption, investing in a best-practices training program, leveraging big data sources, and establishing and exploring broader AI ecosystems.
- Solve for the AI Talent Gap
Mountain outfitters that want to expand their services find that guides with the necessary experience on the mountain are hard to come by. The best guides end up working for the few top outfitters and training new guides takes a lot of time and investment. The same challenges are faced by organizations looking to build AI teams. Finding and retaining capable AI data scientists is extremely challenging and requires a combination of extended talent acquisition and internal talent targeting to find the right people.
- Scale the AI Organization and Drive Adoption
Various organizational models can be leveraged when setting up AI. The type of model that should be used will depend on the organization’s culture and the maturity of the AI function. The team in charge of AI needs to support and drive AI adoption within the organization and take the time to integrate with citizen data scientists.
- Invest in a “Best Practices” Training Program
Climbers that are new to mountaineering need to practice glacier climbing skills. The team members are paired up in teams of three or four and learn how to travel on a rope team to mitigate the risk of slipping on steep hard snow. This is a foundational skill that all high-mountain climbers learn. As the AI team grows, the need for defined standards and best practices will also increase. The team needs to be working together to set up best practices and create a reusable knowledge repository. All new hires will need to access this training during their onboarding and as they operate as a part of the team.
- Leverage Big Data Sources
AI works best when it has access to large datasets. That means integrating big data collected from operations. This might include sensor data from compressor units in the field, medical data from wearables, or videos and images collected in a city. Each of these represents big data that can be used to train AI models. As mentioned earlier, establish momentum with AI by working with small data and graduate to big data when appropriate.
- Establish and Explore AI Ecosystems
The toughest challenge with AI today is not with the technology, but with culture and change management. Organizations need to create collaboration and alignment channels amongst the various internal AI practitioners who often work on similar types of challenges. Organizations also need to reach outside and engage with the external community to drive partnerships, demonstrate thought leadership, and create a brand that will attract the brightest talent.
Why Climb the Mountain?
Your efforts to scale your AI investments will require you to establish and showcase your AI best-practices while using innovative approaches to scaling talent, technology, and adoption. This is often the hardest mountain to climb, but ultimately the most rewarding. While organizations recognize the benefits of AI, few have taken enough time to set-up and sustain their efforts in order to experience realize the full value of scaling artificial intelligence and reaching the summit.
Whether you are at the start of this journey or somewhere along the way and looking for a different route to the summit, we welcome you to reach out to us at Sense Corp. To learn more, download our eBook, Reaching the AI Summit: The Definitive Step-By-Step Playbook for Setting Up Your AI Organization.
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