Webinar Highlights: How to Manage AI Innovation

5 Jun 2024

by Cynthia Kristensen

In our recent webinar, Simon Field, Senior Enterprise Architect, and Chief Information Security Officer Nick Murison discussed a step-by-step approach for managing and accelerating AI innovation.

While AI is already delivering benefits across many sectors, this new technology isn’t without risk.

According to Ardoq’s new Emerging Technology Adoption Report 2024, 65% of more than 700 CIOs surveyed say AI is “the most high-risk technology” that they’ve ever invested in, and if anything goes wrong, the burden will be “on their shoulders.” Furthermore, 61% say FOMO (fear of missing out) is one of the main reasons they are looking to innovate with AI and other emerging technologies.

Given AI's immense opportunities on one hand and the risk of failure on the other, it’s critical that businesses understand AI's impact on the organization and prioritize investments. 


7 Steps to AI Innovation Management

Organizations experimenting with AI are likely to build technical capabilities, deploy new technology products, and upgrade technologies to newer versions that include AI-powered features.

Follow these seven steps for AI innovation management and you will be able to:

  • Model AI and ensure innovation efforts are focused on areas that can have a big impact on business capabilities.
  • Get a comprehensive view of the risks and impacts of initiatives.
  • Accelerate AI efforts from conception to realization.

Step 1: Discover
Map out your organization's AI expertise and identify opportunities or gaps. The goal is to understand what technologies and capabilities are available in various parts of the organization and how to leverage them.

Step 2: Ideate
Once you know the situation, document and analyze ideas. Make sure you identify the value you will derive from each idea rather than just doing it because it seems right.

Step 3: Describe
Articulate and refine your ideas into actionable concepts. The description phase is more than just describing the ideas and the technologies involved. It is also about explaining the changes required to skills and processes.

Step 4: Prioritize
The next step is to prioritize AI initiatives. There are several ways to do this, including using a funnel approach. With this approach, you initially embark on a reasonably large number of experiments and then promptly eliminate those that don't seem to offer value or where the costs outweigh the benefits. Then after the next iteration, narrow down the options. The goal is to focus resources on a well-managed number of initiatives rather than scattering resources across your organization.

Step 5: Plan
Create a plan for each approved AI initiative and link it to strategic objectives. Include ownership, budgets, and timelines.

Step 6: Architect
Articulate your future state architecture, describing new solutions and their dependencies on technologies, existing systems and data. Ensure you illustrate how the solution will comply with policy and regulatory requirements. 

Step 7: Deploy and Learn
The final step involves deploying your AI solutions into the field. Track your progress and accomplishments, document technical debt, and revisit benefits based on real experiences.

What's Next?

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Cynthia Kristensen Cynthia Kristensen Cynthia is a Product Marketing Manager at Ardoq and has over 20 years experience in senior marketing roles and management teams at B2B tech companies.
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