AI readiness: How to enhance your AI strategy development
Leading AI adoption? Get tips on guiding your organization through the AI transformation and uncover the key elements of an effective AI strategy.
Feb 24, 2025 • 4 Minute Read
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Whether your organization is exploring use cases or has already implemented AI, building an AI strategy is critical to generating ROI for AI initiatives. Do organizations currently do enough AI strategy development to prepare their organizations for success?
To find out, we surveyed 600 tech executives and leaders from various industries across five key pillars of AI readiness:
- Data
- Governance
- Investment
- Skills
- Strategy
In this article, we focus on the Strategy pillar. Regardless of where you fall, here’s what you can do to enhance your AI strategy and build leadership buy-in for AI.
Key elements of an effective AI strategy
When we talk about AI strategy development, what do we mean? Broadly speaking, your AI initiatives are tied to long-term organizational goals and you’ve secured leadership buy-in at all levels for AI.
More specifically, organizations leading in AI strategy have:
- Identified a problem AI can solve
- Set clear and actionable goals for AI use
- Created an AI plan for at least the next 12 months
- Set a dedicated AI leader or team to support AI initiatives
- Established KPIs to measure the success of AI implementation
Why strategy development and leadership buy-in are critical to AI readiness
Organizations are scrambling to adopt AI. And while exploration and experimentation are vital to adopting any emerging technology, 80% of AI projects ultimately fail.
Among the reasons they don't succeed? Stakeholders don’t understand the problem AI will solve or are too focused on the technology itself, rather than how they can use it to augment and improve operations, the user experience, or other technologies.
If your organization implements AI without setting a clear goal or considering the role of leadership in successful AI adoption, you hinder your success before you even get off the ground.
Tips for building an AI strategy and getting leadership buy-in
AI transformation doesn’t have an end date. It’s an ongoing process, and your strategy will need to evolve alongside it. These tips will help you build or enhance your AI strategy.
Align your AI strategy with business goals
The top customer-facing AI use cases for organizations include chatbots, voice assistants, and support ticket sorting and generation.
If you haven’t already, start by identifying a problem to solve with AI. Maybe you want to improve the customer experience by providing more personalized experiences or product recommendations. Maybe you’re looking to detect cybersecurity vulnerabilities faster. Whatever your end goal is, consider how AI will help you achieve organizational outcomes.
From there, set actionable goals for your AI implementation. Unfortunately, more than half of organizations (54%) lack clear and actionable goals for AI technology. While high-level ideas are a good starting point, you need a concrete action plan to deliver and get the most out of AI technologies.
Not sure where to start? The 9 Real-World AI Use Cases guide can help you identify potential use cases in your organization.
Build buy-in: How leaders can drive AI transformation
66% of organizations say they fund AI initiatives aggressively. The only way they can do that is because they’ve shown leaders how AI will bring value to the organization.
These steps can help you secure leadership buy-in for AI projects:
Understand who holds the budget, and what their priorities are, to show them how AI initiatives will help them meet their goals and KPIs.
Determine an AI champion or team who will rally stakeholders, lead the AI project, and see it through completion.
Identify external drivers like compliance or risk management requirements. These can add extra weight to your proposal.
Learn more about Documenting and Communicating a Generative AI Strategy in this Pluralsight course.
Create a long-term plan for AI technology
33% of organizations have established plans for AI that extend beyond one year. The remaining 67% have short-term plans for less than a year out.
Long-term success with AI requires a long-term plan. As you set your goals, gather your AI team and build a plan for at least the next 12 months.
Planning a year ahead shows your organization’s commitment to the technology. Your plan may evolve within that year, but you’re thinking about it as more than a trend for your organization—it’s part of your long-term business strategy.
Learn more about Developing an Artificial Intelligence Strategy for Your Organization.
Determine KPIs to track ROI on AI investments
When you set KPIs for AI projects, you can see if you’re actually making a return on investment beyond subjective measures like AI “making life easier” for employees.
Did using AI solve your problem? Did you achieve your goals? From there, you can adjust, iterate, and reallocate your budget as needed.
The top three metrics organizations use to track the success of their AI initiatives are productivity increases, increased revenue, and AI pilots moving into production.
Additional metrics will vary depending on your goals. For example, if you’re using AI for threat detection, you may look at detection rates, response times, and cost savings. If you’re using AI for data synthesis, metrics like decision-making speed, accuracy, and time savings will be more aligned to your goals.
Discover more resources to improve your organization’s AI maturity
At the end of the day, it’s not about how you scored, but knowing where you scored. Once you have a baseline, you can target areas for improvement and take your organization’s AI readiness up a notch.
Learn more about how to ensure your organization is AI-ready:
- How to bridge the AI skills gap with upskilling for AI
- How to mitigate risk with AI governance
- How to improve your data management strategy for AI
- How to create an AI investment strategy to maximize ROI
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