The Speed-to-Value Framework: Getting Started with AI in Health & Wellness

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The Transformation Imperative

In our previous article, “AI Transformation: Why Waiting Is Your Biggest Risk”, we established the critical importance of moving forward with AI adoption rather than delaying implementation. As we highlighted, only 5% of U.S. companies have integrated AI into their products, creating a narrowing window for early-mover advantage that won’t remain open indefinitely.

Now we face the next crucial question: How do you actually get started? While 86% of healthcare professionals report leveraging AI within their organizations[1], a shocking 70-88% of these initiatives never make it past the pilot phase[2]. This implementation gap represents billions in wasted investment—with global healthcare AI spending projected to reach $164 billion by 2030[3]—and countless missed opportunities to improve care.

The challenge is clear: healthcare organizations know AI is critical, but they struggle with implementation. The IDC reports that out of 33 AI proof-of-concept projects, only 4 on average graduate to wide deployment[2]. This high failure rate stems from unclear objectives, insufficient data readiness, and lack of in-house expertise – precisely the challenges the Suggestic framework addresses. Many leaders see AI as either a complicated multi-year journey or a quick technical fix. Neither view captures the truth.

The reality? With the right approach, organizations can achieve meaningful results in 90 days. This doesn’t mean complete transformation overnight but rather gaining measurable value through strategic acceleration—moving quickly on the right priorities while building toward bigger goals.

This is the core of the Speed-to-Value framework: a structured approach that works for both mid-market and enterprise healthcare organizations seeking to implement AI effectively and efficiently.

The Speed vs. Quality Paradox

The numbers are sobering: only 30% of healthcare AI pilots ever reach production[4]. But speed and quality aren’t natural enemies. The real problem is poor implementation strategy.

Many organizations rush into AI projects without proper preparation, then get bogged down by unexpected complications. A 2023 survey found that unclear objectives, insufficient data readiness, and lack of in-house AI expertise were the primary reasons for implementation failure[5]. Others spend years planning the perfect solution while competitors move ahead. Both approaches lead to the same outcome: failure.

Strategic acceleration means moving quickly on the right priorities. It acknowledges that healthcare requires specialized approaches that generic AI implementation methods don’t address:

• Complex health data ecosystems: Healthcare data is notoriously fragmented across siloed systems (EHRs, PACS, labs), with inconsistent formats and terminology[6]
• Stringent regulatory requirements: Any AI solution handling patient data must comply with HIPAA, and clinical AI may require FDA oversight as Software as a Medical Device (SaMD)[7]
• Operational workflow integration challenges: Solutions that disrupt rather than enhance existing operational workflows face significant adoption barriers[8]
• Trust barriers among healthcare stakeholders: Clinicians are often wary of “black box” AI systems, particularly given high-profile past failures[9]

Speed-to-Value recognizes these challenges and builds them into the implementation process from day one.

Phase 1 (Days 1-30): Assessment & Quick Win Identification

The first 30 days are critical. This period isn’t about developing algorithms or deploying technology—it’s about laying the proper foundation through thorough assessment and smart planning.

Start with a comprehensive readiness evaluation:

Data Readiness: What data do you have? Is it clean, accessible, and representative? Can it legally be used for the intended purpose? According to Sunil Dadlani, CIO of Atlantic Health System, “Never start with the technology. Identify what the key challenges and opportunities are and how they align to our business goals and mission.”[10] Many AI projects fail simply because the necessary data isn’t available or usable.

Strategic Alignment: What business problems are you trying to solve? How will AI support your organization’s goals? In a 2023 industry survey, organizations that aligned AI initiatives with specific strategic goals were 2.5x more likely to report successful implementations[11].

Use Case Selection: Based on your data readiness and strategic priorities, which use cases offer the best combination of impact and feasibility? A value/feasibility matrix can help visualize and prioritize potential projects[12]. The ideal first project delivers visible value without requiring massive data integration.

Technical Infrastructure: Do you have the necessary computing resources, security protocols, and integration capabilities? The HIMSS Analytics INFRAM model provides a framework for assessing your technology infrastructure readiness[13].

This assessment phase is often rushed or skipped entirely—a major reason why so many AI projects fail. Taking time for proper assessment doesn’t slow progress; it accelerates success by preventing costly missteps.

The final step in this phase is identifying your quick win: a high-impact, achievable use case that can demonstrate value and build momentum. For example, automating a repetitive operational task may be more achievable than a complex clinical prediction model for your first project.

The Healthcare AI Decision Matrix

Once you’ve completed your assessment, you’ll need to decide how to implement your chosen use case. The Healthcare AI Decision Matrix helps evaluate your options based on timeline, technical requirements, and compliance considerations.

 

Approach	Description	Pros	Cons	Best For
In-house Development	Building AI solutions with your internal team	• Full control • Customization • Intellectual property ownership	• Requires specialized talent • Typically takes 18+ months • Maximum IT strain	Organizations with strong data science teams and unique use cases
Generic AI Platforms	Using general-purpose AI tools and adapting them to healthcare	• Faster implementation • Less specialized talent needed	• Limited healthcare expertise • Potential compliance issues	Non-clinical applications with minimal regulatory requirements
Specialized Healthcare Partnerships	Working with healthcare-focused AI partners	• Faster deployment • Healthcare domain expertise • Minimal IT burden	• Less control • Ongoing partnership costs	Organizations seeking faster implementation with healthcare-specific compliance

 

The data shows a clear trend: 67% of healthcare organizations now favor partnerships over building in-house[14]. Additionally, 64% are willing to co-develop AI solutions with startups as of late 2023[15]. This “buy vs. build” decision should be based on your organization’s resources, timeline needs, and the complexity of your use case—what we call “right-sized AI.”

Mid-market organizations typically benefit most from specialized partnerships that provide healthcare-specific expertise without requiring large internal teams. Enterprise organizations may have more capacity for in-house development but often accelerate their timeline through strategic partnerships. Large hospital systems have shown better success rates, with about 46% of their AI POCs reaching production compared to the average of 30%[16].

Overcoming the Three Critical Barriers

Healthcare AI implementation faces three critical barriers that must be addressed from the start:

1. Data Integration Complexity

Healthcare data is notoriously fragmented across systems, often in different formats and structures. A 2023 industry report noted that “the one unifying principle that bubbles to the top of the list is data fragmentation across systems, locations and formats”[19]. To overcome this barrier:

• Start with use cases that minimize integration complexity
• Leverage existing data before attempting complex integration
• Consider partners with pre-built healthcare connectors and experience with standards like HL7 FHIR
• Begin data harmonization efforts early, possibly adopting a common data model like OMOP[20]

2. Trust and Adoption Barriers

AI tools must gain clinician and stakeholder trust to succeed. As one expert noted, AI projects will “ultimately fail if employees don’t use the tools”[21]. Initial steps include:

• Involving end-users from the assessment phase
• Framing AI as augmenting, not replacing, human expertise
• Starting with “assistive” AI that supports decisions rather than making them
• Identifying and engaging champions early

A case study from a student health center showed how providers initially “were hesitant” and “apprehensive about something that uses AI,” but after brief demos and successful trials, over 30 providers embraced the tool within months[22].

3. Compliance and Regulatory Navigation

Healthcare’s regulatory landscape adds significant complexity. Address this by:

• Including compliance stakeholders in initial planning
• Clearly defining what PII will be used and how
• Documenting regulatory considerations for each use case (HIPAA requirements, potential FDA oversight)
• Choosing partners with healthcare compliance expertise and validated solutions

While these barriers present challenges, addressing them proactively during your first 30 days creates a clear path for successful implementation.

Action Steps to Begin Your AI Journey

Ready to start your healthcare AI journey? Here are five immediate steps to take:

1. Map your organization’s AI readiness using the assessment framework. Evaluate your data assets, technical infrastructure, talent, and strategic priorities.
2. Identify 2-3 potential use cases with clear value propositions. Look for the intersection of high impact and feasibility based on your current data and resources.
3. Evaluate your resource gaps and partnership needs. Be honest about your organization’s capabilities and where external expertise might accelerate success. Consider that 61% of organizations implementing generative AI are pursuing partnerships with third-party vendors to develop customized solutions[23].
4. Establish baseline metrics for measuring success. Define what “good” looks like for your initial implementation and how you’ll measure progress.
5. Secure executive sponsorship with realistic goals and expectations. Ensure leadership understands both the potential and the realistic timeline for value. A 2024 survey found that 60% of healthcare leaders expect to see positive ROI from AI in under 12 months[24]—setting proper expectations is crucial.

The Speed-to-Value framework isn’t about unrealistic promises. It’s about strategic acceleration—moving quickly on the right priorities while building a foundation for lasting success.

At Suggestic, we’ve developed a proven approach that combines enterprise-grade AI infrastructure with deep healthcare expertise. Our platform accelerates transformation while ensuring security and compliance, enabling organizations to achieve results in weeks rather than months. 

Your competitors aren’t waiting. Let’s identify your first AI win and ensure you don’t fall behind. Schedule a call today to see tangible results in the next 90 days.

Schedule a Call

 

In our next installment, we’ll explore the implementation and integration phase, including data integration approaches, compliance guardrails, and user engagement strategies.

About Suggestic: We accelerate enterprise AI transformation through our unique combination of proven infrastructure, specialized healthcare expertise, and a partnership model that builds lasting capabilities. With over 50 successful enterprise deployments and consistently exceptional results, we’re ready to help you capture the AI advantage.

 

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Appendix

Healthcare AI Readiness Checklist 

□ Clear strategic objectives aligned with organizational goals 

□ Accessible, quality data for target use cases 

□ Executive sponsorship and resource commitment 

□ Defined success metrics and value expectations 

□ Identified clinical or operational champions

 

Signs You Need a Specialized Partner 

• Limited internal data science expertise
• Need for rapid implementation (under 6 months)
• Healthcare-specific compliance requirements
• Complex integration with existing systems
• Previous failed AI implementation attempts

 

References

[1] Medscape and HIMSS 2024 report on healthcare AI adoption 

[2] IDC research cited in “88% of AI pilots fail to reach production” 

[3] Market Research Future/Grand View Research projections on healthcare AI market 

[4] Bessemer Venture Partners Healthcare AI Adoption Index 2024 

[5] CIO Magazine, “88% of AI pilots fail to reach production — but that’s not all on IT” 

[6] Health IT experts quoted in TechTarget on healthcare data fragmentation 

[7] FDA guidance on Software as a Medical Device (SaMD) 

[8] Journal analysis on workflow integration as adoption factor 

[9] Healthcare IT research on clinician trust barriers in AI adoption 

[10] Aidoc blog, “5 Things You Need to Assess AI Readiness at Your Facility” 

[11] Industry survey on AI strategic alignment (referenced in materials) 

[12] Clinical value/feasibility matrix framework from informatics literature 

[13] HIMSS Analytics INFRAM Model documentation 

[14] Survey data on partnership preferences in healthcare AI 

[15] Bessemer Venture Partners, “Healthcare AI Adoption Index” 

[16] Bessemer Venture Partners on hospital system AI success rates 

[17] Case study: Doctor’s Data implementation metrics 

[18] Case study: Global CPG implementation metrics 

[19] TechTarget article on healthcare data integration challenges 

[20] TechTarget on OMOP as a healthcare data harmonization approach 

[21] TechTarget article on employee adoption of AI tools 

[22] Case study on healthcare provider AI adoption journey 

[23] McKinsey survey on generative AI adoption strategies, Q4 2024 

[24] Bessemer Venture Partners survey of healthcare leader ROI expectations