Assessing Your Organization's AI Potential

In today's rapidly evolving technological landscape, artificial intelligence (AI) presents unprecedented opportunities for business transformation. Yet, despite 98% of companies exploring AI, only 26% successfully move beyond pilot projects to create real value¹. This gap between AI's promise and reality underscores the critical importance of assessing your organization's AI readiness before embarking on implementation.

To illustrate what a successful AI readiness journey looks like in practice, we'll follow Midwest Health Solutions (MHS), a fictional mid-sized healthcare provider with 15 clinics, as they progress from initial assessment to successful AI implementation. This hypothetical case study demonstrates how the concepts and frameworks translate into practical action for organizations at any stage of AI maturity.

Meet Midwest Health Solutions

Company Background:

• 2,000 employees including 250 physicians
• Annual revenue: $350 million
• Current tech infrastructure: Electronic Health Records (EHR) system, billing software, patient portal, and basic analytics
• Business challenges: Increasing patient wait times, administrative burden on clinical staff, and rising operational costs

MHS's Chief Information Officer, David Li, had been monitoring AI's potential in healthcare for years. With physician burnout increasing and operational costs rising, he believed AI could help address these challenges—but was unsure where to start. After discussions with the CEO and Chief Medical Officer, they formed a small steering committee to assess the organization's AI readiness.

MHS's First Assessment

AI readiness is not merely about technological capabilities—it encompasses strategic alignment, organizational culture, talent infrastructure, and data governance². The MHS steering committee, which included representatives from IT, clinical operations, and executive leadership, conducted an initial assessment to understand where they stood:

Current State Findings:

• Strategic Alignment: No formal AI strategy existed, although there was interest in using AI to reduce administrative burden
• Leadership Commitment: Only the CIO had expressed strong support for AI initiatives
• Talent Framework: No dedicated AI expertise; IT team had basic data analysis skills
• Data Infrastructure: Data existed but was siloed across EHR, billing, and scheduling systems
• Organizational Culture: Clinicians were cautious about technology that might disrupt patient care

Based on their assessment, MHS determined they were at the Exploring stage of AI readiness—the first of five stages organizations typically progress through on their AI journey³:

The Five Stages of AI Readiness

1. Exploring (MHS's Current Stage)

• Focus on learning key AI concepts
• Begin ideating potential use cases
• Build initial AI strategy and experience
• Only about 3% of organizations at this stage report significant value from AI³

2. Planning

• Formalize business strategy around AI
• Study successful AI implementations in industry
• Prioritize use cases based on business impact
• Assess technology and data readiness

3. Implementing

• Secure leadership support and resources
• Build specialized AI expertise within teams
• Begin deploying high-priority use cases
• Establish governance frameworks

4. Scaling

• Create a culture of innovation
• Expand AI initiatives across departments
• Analyze impact of implemented AI solutions
• Refine processes based on early results

5. Realizing

• Embed AI technology throughout operations
• Foster continuous innovation within every team
• Achieve sustainable and measurable value
• 96% of organizations at this stage report significant value from AI³

The committee acknowledged they had a long way to go. "We need a structured approach to move forward," noted the CIO after reviewing their assessment results.

MHS Takes a Proactive Approach

Rather than waiting for perfect conditions, the MHS steering committee decided to take a proactive, iterative approach to building AI readiness. Their journey followed a structured process that organizations at any stage can adapt to their own circumstances:

Step 1: Identify Existing AI Use and Power Users

The steering committee surveyed departments to discover where AI was already providing value:

Discoveries:

• The radiology department was using a third-party AI tool for preliminary image analysis
• Several physicians were using AI scribing tools independently to document patient encounters
• The scheduling department had implemented a basic predictive model for appointment no-shows

Dr. Chen, a physician who had been using an AI scribing tool independently, reported it saved her 1-2 hours daily on documentation, allowing her to see more patients and reduce burnout.

This approach mirrors what successful organizations do. Google, for example, discovered that the first step in their AI journey was understanding where AI was already adding value in their organization. They developed what they call a "customer success flywheel" that begins with deep customer understanding and builds solutions informed by actual usage patterns⁴.

Practical Steps MHS Took:

• Surveyed departments to identify where employees were already using AI tools
• Looked for "shadow AI" applications that teams had adopted independently
• Interviewed power users to understand workflows and success factors
• Documented both positive outcomes and challenges encountered

"We don't need to start from zero," the CIO observed. "We already have people using AI successfully. Let's learn from them and build on what's working."

Step 2: Start Small with a High-Impact Prototype

Based on the positive experience with AI scribing, MHS decided to focus their first AI initiative on alleviating documentation burden for physicians:

Prototype Plan:

• Goal: Reduce physician documentation time by 25%
• Scope: 4-week pilot with 10 physicians across different specialties
• Technology: Integration of AI scribing tool with their existing EHR
• Metrics: Time spent on documentation, physician satisfaction, documentation quality

The IT team rapidly implemented the AI scribing tool with the 10 physicians, provided basic training, and set up a feedback mechanism. They intentionally kept the scope narrow to ensure they could deliver results quickly.

This approach follows the example of successful organizations like McKinsey, which developed its GenAI platform "Lilli" through a remarkably efficient process. They started with a lean prototype built in just one week by a small team. This proof of concept was enough to secure investment approval for further development. Within five months, they had rolled out a platform that now saves employees up to 30% of their time⁵.

Practical Steps MHS Took:

• Built a simple prototype addressing a specific business challenge
• Focused on a problem with clear metrics for success
• Kept the initial scope narrow and achievable within 4 weeks
• Used existing data sources rather than waiting for perfect data

"Start small, focus on tangible outcomes," became the team's mantra during this phase.

Step 3: Gather Continuous Feedback and Iterate

During the pilot, MHS established multiple feedback channels:

Feedback Mechanisms:

• Weekly check-ins with pilot participants
• Daily usage metrics tracking
• Documentation quality audits
• Patient satisfaction surveys

Mid-Pilot Findings: While most physicians saw time savings, three participants struggled with the accuracy of the tool when using complex medical terminology. The IT team worked with the vendor to improve specialty-specific recognition and provided additional training for these participants.

A cardiologist in the pilot group initially reported challenges with specialty terminology. The IT team scheduled a session with the vendor to address these specialty-specific challenges, and two weeks later, the accuracy had significantly improved.

This iterative approach mirrors the success of companies like Ulta Beauty, which created an AI-powered recommendation engine to bridge physical and digital customer experiences. What made their approach successful was their focus on continuous feedback and refinement. The system analyzes campaign results and customer interactions to continuously improve personalization, resulting in 95% of sales coming from returning customers⁶.

Practical Steps MHS Took:

• Established clear feedback channels for both users and stakeholders
• Scheduled regular review sessions to discuss what's working and what's not
• Tracked metrics that matter to the business, not just technical performance
• Made iterative improvements based on actual usage patterns

Step 4: Identify Required Context and Data Sources

As the pilot progressed, MHS realized that the AI scribing tool performed better with more context:

Key Data Elements Identified:

• Physician specialty and common terminology
• Patient medical history prior to appointment
• Standard care protocols for common conditions
• Clinic-specific documentation requirements

The IT team created a secure interface to provide the AI tool with relevant patient history from the EHR and specialty-specific terminology databases. This significantly improved accuracy for specialists.

This focus on context and data echoes the approach taken by Liberty London, which implemented AI to automatically classify and route customer support tickets. Their success came from identifying the specific context and data needed: customer intent, sentiment analysis, and language processing. By focusing on these specific data points, they were able to significantly improve customer service efficiency⁷.

Practical Steps MHS Took:

• Mapped the data sources needed for their specific use case
• Identified data gaps and integration challenges
• Assessed data quality, accessibility, and governance requirements
• Developed a plan for data enrichment where needed

Step 5: Measure Business Value, Not Just Technical Metrics

After the 4-week pilot, MHS evaluated outcomes against business objectives:

Results:

• Average documentation time reduced by 35% (exceeding 25% goal)
• Physician satisfaction increased by 28%
• Physicians able to see 2 additional patients per day on average
• ROI calculation: Additional revenue from increased patient visits projected to exceed the cost of the AI system by 3x in the first year

The steering committee presented these results to the executive team, focusing on business impact rather than technical details. They emphasized the improvements in physician well-being and patient access, which directly impacted revenue.

This approach to measuring business value mirrors Google's strategy with their Smart Focus AI. Google developed an AI system to help their sales representatives focus on the most valuable customer engagements. Rather than measuring technical metrics like model accuracy, they focused on business outcomes: customers found 23.5% more value with Google (measured as customer spend over subsequent weeks), and representative satisfaction scores doubled compared to previous non-AI approaches⁴.

Practical Steps MHS Took:

• Defined clear business metrics that their AI initiative would impact
• Established a baseline measurement before implementation
• Set up comparison metrics to measure performance with and without AI
• Calculated ROI based on actual business improvements, not proxy metrics

Step 6: Build a Strategic Roadmap

Based on the successful pilot, MHS now had evidence that AI could deliver measurable value to their organization. The executive team was impressed by the results and asked the steering committee to develop a comprehensive strategy for expanding AI initiatives.

The committee developed a 12-month AI strategic roadmap:

Short-term (0-3 months):

• Expand AI scribing tool to all interested physicians
• Formalize AI governance structure
• Secure executive sponsorship from CEO and CMO

Medium-term (3-6 months):

• Explore AI for appointment scheduling optimization based on the success of the existing basic predictive model
• Hire a dedicated AI/ML specialist
• Implement data integration infrastructure to reduce silos

Long-term (6-12 months):

• Develop predictive analytics for patient no-shows and readmissions
• Create an AI Center of Excellence
• Begin exploring advanced clinical decision support applications

This strategic roadmap addressed key dimensions of AI readiness that MHS identified in their initial assessment:

1. Strategic Alignment

The roadmap ensured AI initiatives directly supported MHS's business goals of reducing physician burnout, increasing patient access, and controlling costs.

2. Leadership Commitment

With the CEO and CMO now actively championing AI initiatives, MHS had the leadership support critical for success. Research shows that in organizations at the "realizing" stage of AI readiness, 100% of senior leaders have clearly communicated their commitment to AI, compared to just 6% at the "exploring" stage⁸.

3. Talent Framework

The plan to hire dedicated AI expertise and eventually create a Center of Excellence followed best practices for building multidisciplinary teams⁹:

• Phase 1 (Start Small): AI Engineer + Product Manager or Designer
• Phase 2 (Multidisciplinary Team): Add UI/UX Designer, Frontend Developer, and Backend Developer/Data Engineer
• Phase 3 (Distributed Teams): Small, agile teams assigned to different products or components

4. Data Infrastructure

The medium-term focus on data integration addressed MHS's identified challenge of siloed data across systems—a critical foundation for more advanced AI applications.

5. Organizational Culture

By starting with a high-impact use case that directly benefited clinicians, MHS addressed the cultural resistance identified in their initial assessment. Physicians who experienced the benefits firsthand became advocates for further AI adoption.

One Year Later: MHS's AI Transformation

One year after their initial AI readiness assessment, MHS had moved from the Exploring stage to the Implementing stage of AI readiness:

• The AI scribing initiative had reduced documentation time across the organization by 30%
• Physicians now saw an average of 15% more patients, increasing revenue while reducing burnout
• A new AI appointment scheduling system had reduced wait times by 22%
• Leadership had fully embraced AI, with the CEO regularly communicating its strategic importance
• MHS had established a dedicated AI team with clear governance procedures
• The organization had been recognized with a regional healthcare innovation award

Dr. Chen, whose early adoption of AI scribing inspired the organization-wide initiative, noted that the difference in physician morale was substantial compared to a year prior.

David Li, now Chief Digital and Information Officer, reflected: "When we started, we were just exploring what AI could do for us. Today, it's become integral to how we operate. But we're still early in our journey—the next phase is about scaling these successes across more areas of the organization."

Lessons from MHS's Journey: Your AI Readiness Assessment

MHS's experience offers valuable lessons for organizations at any stage of AI readiness. To evaluate your own organization's AI potential, consider these critical questions aligned with the steps MHS followed:

1. Initial Assessment

• How clearly defined are your organization's AI objectives?
• Do these objectives directly support your core business goals?
• What is your current leadership commitment to AI initiatives?
• What AI expertise exists within your organization?
• How accessible and integrated are your data sources?
• How receptive is your organizational culture to AI-driven change?

2. Identifying Existing AI Use

• Where are employees already using AI tools (officially or unofficially)?
• Who are your AI power users and what benefits are they experiencing?
• What challenges have early adopters encountered?
• What lessons can be learned from existing implementations?

3. Prototyping High-Impact Solutions

• What business problems could AI help solve in the short term?
• Which use cases have clear, measurable success criteria?
• How can you keep initial scope narrow and achievable?
• What existing data and systems can you leverage?

4. Gathering Feedback

• What mechanisms will you establish for continuous feedback?
• How will you track both technical performance and user experience?
• What process will you follow to implement improvements?
• How will you maintain communication with stakeholders?

5. Enhancing Data Context

• What specific data sources are needed for your AI use case?
• Where are there gaps in your current data infrastructure?
• How will you ensure data quality, security, and governance?
• What integration points are required for optimal performance?

6. Measuring Business Value

• What specific business metrics should your AI initiative impact?
• How will you establish a baseline for comparison?
• What methodology will you use to calculate ROI?
• How will you communicate value to leadership?

7. Building a Strategic Roadmap

• What short, medium, and long-term initiatives should be prioritized?
• How will you secure and maintain executive sponsorship?
• What talent and team structure is needed to support your AI strategy?
• How will you address data integration challenges?
• What approach will you take to cultural change management?

Next Steps: From Assessment to Action

Based on your assessment results, consider these targeted actions to advance your AI readiness:

For Organizations at the Exploring Stage (Like MHS at the Start)

• Conduct a thorough assessment of your current AI readiness
• Identify existing AI use and power users within your organization
• Start small with high-impact, focused prototypes
• Establish feedback mechanisms and iteration processes
• Build the foundational strategy and secure leadership buy-in

For Organizations at the Planning/Implementing Stages (Like MHS Today)

• Formalize your AI governance approach
• Invest in building multidisciplinary teams
• Implement data integration infrastructure
• Establish clear metrics for measuring AI impact
• Develop a strategic roadmap for scaling successful initiatives

For Organizations at the Scaling/Realizing Stages

• Develop centers of excellence to disseminate best practices
• Create feedback mechanisms to continuously improve AI systems
• Address organizational and cultural barriers to adoption
• Explore new business models enabled by AI capabilities
• Foster continuous innovation within every team

Conclusion: Your AI Readiness Journey

The path to AI readiness doesn't require massive upfront investment or perfect conditions. As MHS's journey demonstrates, organizations can make significant progress by following a structured approach:

1. Start with a thorough assessment of your current state
2. Identify where AI is already adding value within your organization
3. Develop focused prototypes with clear business objectives
4. Gather continuous feedback and iterate based on real-world usage
5. Ensure access to the right data and context
6. Measure impact through meaningful business metrics
7. Build a strategic roadmap that addresses all dimensions of AI readiness

Remember that only 26% of organizations successfully move beyond AI pilot projects to create real value¹. Those that succeed share a common approach: they focus not just on technology but on the holistic transformation needed to leverage AI's full potential¹⁰.

Your organization can be among those success stories by taking a measured, strategic approach to AI readiness. The journey may be challenging, but the potential rewards in efficiency, innovation, and competitive advantage make it well worth the effort.

Download Our AI Readiness Assessment Template

Take the first step toward AI success with our comprehensive assessment tool designed to help you evaluate your organization's AI potential across all critical dimensions.

Footnotes

1. Boston Consulting Group (BCG) report cited in Datalumina, "Why Generative AI Often Fails to Deliver Value," https://www.datalumina.com/insights/insight/why-generative-ai-often-fails-to-deliver-value ↩ ↩²

2. Microsoft, "Building a foundation for AI success: Business strategy," https://www.microsoft.com/en-us/microsoft-cloud/blog/2023/11/01/building-a-foundation-for-ai-success-business-strategy/ ↩

3. Microsoft, "The AI Strategy Roadmap: Navigating the stages of value creation," https://www.microsoft.com/en-us/microsoft-cloud/blog/2024/04/03/the-ai-strategy-roadmap-navigating-the-stages-of-value-creation/ ↩ ↩² ↩³

4. Google Customer Engagement Team, "AI for Customer Engagement at Google," https://aibusiness.com/ml/ai-for-customer-engagement-at-google ↩ ↩²

5. McKinsey & Company, "Rewiring the way McKinsey works with Lilli," https://www.mckinsey.com/capabilities/mckinsey-digital/how-we-help-clients/rewiring-the-way-mckinsey-works-with-lilli ↩

6. CMS Wire, "AI in Customer Experience: 5 Companies' Tangible Results," https://www.cmswire.com/customer-experience/ai-in-customer-experience-5-companies-tangible-results/ ↩

7. Zendesk, "Liberty London Customer Service AI Case Study," https://www.zendesk.com/customer/liberty-london/ ↩

8. Microsoft, "The AI Strategy Roadmap: Navigating the stages of value creation," https://www.microsoft.com/en-us/microsoft-cloud/blog/2024/04/03/the-ai-strategy-roadmap-navigating-the-stages-of-value-creation/ ↩

9. Datalumina, "From Concept to Scale: Building the Perfect GenAI Team for Success," https://www.datalumina.com/insights/insight/from-concept-to-scale-building-the-perfect-genai-team-for-success ↩

10. Microsoft and Ipsos survey of 1,300+ business and technology decision makers, https://www.microsoft.com/en-us/microsoft-cloud/blog/2024/04/03/the-ai-strategy-roadmap-navigating-the-stages-of-value-creation/ ↩