AI SolutionsAI & DataData Analytics
Article • • 5 minute read

Key Considerations for Determining AI Readiness

Gathering stakeholders, fostering collaboration amongst those groups and coming to consensus on goals and objectives is a vital first step to AI success.

The incredible success of generative AI applications like ChatGPT has forced business and IT leaders alike to think strategically about the transformational technology, whether they're ready to or not.  

Given the rapid pace at which generative AI claimed its place in mainstream consciousness, most organizations were left flat footed and are still working to catch up.  

The question we get asked most often by clients is one you've probably asked yourself: What is the simplest and most effective way I can get started with AI?  

WWT co-founder and CEO Jim Kavanaugh recently appeared on CNBC talking about just that. Kavanaugh, a staunch AI advocate within WWT, said leaders should approach it from two different standpoints. 

First, organizations need to understand what AI is and what it means for their business. What use cases will drive the most value? What outcomes are you looking to achieve? And what changes — if any – will you need to make from an IT infrastructure standpoint to enable those aspirations?  

Even in today's landscape of abundant data, rapid LLM innovation and access to accelerated compute, success is not guaranteed. AI adoption doesn't happen in a silo. Leaders should be tapping into centers of excellence and expertise that may already exist within their company to collaborate and talk openly about how they can support and scale AI enterprise wide.  

Once various stakeholders are aligned on goals and objectives of AI for the organization, leaders can move on to putting in place policies, procedures and governance best practices to help implement AI solutions in a controllable, scalable way.  

"There is massive opportunity, but there will be a lot of sorting out from what it actually means for organizations and what is the best fit for organizations moving forward," Kavanaugh said. "It's not going to be something that is solved quickly, but everyone will look to move quickly and get on that bandwagon, which will create headwinds for what it means for their businesses." 

That Kavanaugh is taking such an active role in AI is a good lesson in and of itself for leaders.  

Organizational commitment to AI initiatives — particularly in the C-suite — and leadership's influence over prioritizing AI initiatives are two key topics most leaders need to consider when assessing their organization's AI readiness.  

"Having leaders that understand AI's potential and its alignment with the organization will be crucial to future success, and the collaboration it will require will create centers of excellence along the way that can act as hubs for innovation," he said.  

Other key issues to solve for include:  

  • Data quality and governance: Does your organization understand its own level of data and AI maturity? Are the AI models or solutions under consideration compliant with your policies?
  • Access to data, AI models and reference architectures to train on: Do you have the data scientists, engineers and other experts who understand your organization's specific data capabilities and requirements? Informing or training AI models — whether proprietary or off-the-shelf — on inadequate, incomplete or incorrect data will only lead to failure down the line.
  • Budget available for AI technology: This ties back to executive commitment. If you don't have the budget available to advance AI, you will not get very far. Regardless, be thinking about roadmaps for implementation, human-centered service design, and automation vs. augmentation scenarios.
  • Current level of AI usage within the organization: Many within your organization are already experimenting with generative AI for business purposes (e.g., writing code or marketing pitches, analyzing pricing models, or automating certain tasks). Strive to understand those use cases and determine how they may help or hinder future initiatives. Understand potential concerns about how AI will impact those employees and how to create a positive, AI-inspired workforce.
  • Customer readiness to use AI: Understanding your customers and their needs will help you identify and develop intelligent solutions that are ready to be consumed.
  • Threat of AI from competitors: Do not ignore the competitive threat posed in your market or industry by how others are using AI. While it's essential to be a fast follower, leaders should avoid chasing every new trend; instead, they should focus on thoughtful and controlled AI implementation, and assess current and future resource needs based on prioritized initiatives.

Upon deciding which objectives will be initially supported by AI and achieving initial success, organizations will need to consider two additional areas for scaling AI across the enterprise:  

In the end, it is important for senior leaders to vividly understand and think of AI as an organizational shift, and to fully grasp and embrace the potential usage and adoption of AI technologies across the enterprise. 

Gathering stakeholders, fostering collaboration amongst those groups and coming to consensus on goals and objectives is a vital first step to AI success. The role executive leadership has in driving those outcomes cannot be understated.