AI road trip: How to drive your AI pilot to value and success

Snacks? Check. Head-bopping playlist? Check. Extra napkins for your messy friend riding shotgun? Triple check. You’re ready for your cross-country road trip…until you miss the exit for the gas station. And construction causes a half-hour detour. And your phone loses signal. You thought you planned for everything. You were wrong.

Like this road trip, AI pilots almost always face unexpected trials and tribulations. In part one of our AI pilot blog series, Field CTOs from SHI’s Advanced Technologies Group (ATG) explored the MIT report that 95% of generative AI pilots never reach meaningful production and shared our approach to joining the 5% that do.

Now, with insight from our recent SHI Summit – Scaling smarter: Infrastructure for the AI era – we’ll provide guidance you can act on today to steer your AI pilot toward success.

From a readiness self-assessment and proof of value (POV) framework to proven methods for AI model refinement, here are our recommendations:

Approach generative AI with a growth mindset

People. Processes. Technology. These are the basics of any IT adoption strategy – a fact that still holds true in the AI era. According to SHI Field CTO Lee Ziliak, this is because generic AI tools realize little transformation without deep customization and iterative, feedback-driven evolution.

Our previous blog posited that most AI projects fail because they lack meaningful integration, governance, and measurement. To predict how your organization stands, perform the quick POV readiness self-assessment below. You’ll identify gaps in your preparedness, letting you strengthen weak points in your capabilities before it’s too late.

If you score 20 or above, you likely have the growth mindset and organizational alignment necessary to see your pilot to production and generate meaningful value for your organization. If you score below 20, we recommend addressing the gaps highlighted in the assessment or starting with smaller AI use cases.

Self-assess your POV readiness

Do you feel prepared to bring an AI POV from testing to production? Score your readiness across five dimensions, from 1-5 for each:

Data readiness (1=unstructured/incomplete, 5=clean/documented/accessible):

• Do you have 10,000+ domain-specific examples for training or testing?
• Is your data labeled, cleaned, and centrally accessible?
• Have you determined personally identifying information (PII), compliance, and data residency requirements?

Use case specificity (1=vague idea, 5=defined metrics/personas/workflows):

• Can you describe the use case’s exact task in one sentence?
• Do you know the current baseline performance (manual time, error rate, cost)?
• Have you defined target improvements (e.g., “reduce processing time by 60%”)?

Stakeholder alignment (1=solo effort, 5=cross-functional buy-in):

• Do you have executive sponsorship and budget commitment?
• Are end users involved in requirements gathering?
• Have legal, security, and compliance teams reviewed the use case?

Infrastructure maturity (1=starting from scratch, 5=production-ready platform):

• Do you have GPU compute available for experimentation?
• Is there an MLOps pipeline for model versioning and deployment?
• Can you scale to ten-times your current workload without infrastructure changes?

Governance Framework (1=no policies, 5=comprehensive AI governance):

• Have you defined acceptable use policies for AI outputs?
• Is there a process for monitoring bias and hallucinations?
• Do you have incident response procedures for AI failures?

ASSESSMENT SCORING:

20-25 points = Ready to productionize your POV. | 15-19 = Address gaps in your preparedness. | <15 = Consider significant preparation either before or during your initial stages.

Remember: A low readiness score does not preclude you from testing the feasibility of an AI use case. In fact, a quick POV could be an effective way to test your readiness on a smaller scale and provide lessons learned for scaling larger models. This assessment highlights recommended areas of improvement that, if neglected, could result in AI prototypes that fail to produce meaningful value.

Iterative prototyping is your highway to success

Ziliak states the key to effective prototyping is to start small. Focus on a single use case, use real data to reflect realistic scenarios, and iterate often based on testing and stakeholder input.

With iterative prototyping, you’ll work your use cases, gather results, analyze them, then repeat the process until you’ve achieved your desired outcome. This methodology lets you test hypotheses quickly and identify what’s feasible before committing significant time and budget.

Throughout the process, you’ll also reveal any technical and practical limitations of your systems and AI tools, including performance bottlenecks and ethically concerning output biases. Repeated testing and refinement help overcome these issues. All the while, you’ll be able to produce tangible demos that make complex concepts clear for non-technical stakeholders, gaining valuable feedback without getting lost in the weeds.

Our recommended POV framework:

First week: Define success metrics

• Set baseline measurements (current performance without AI).
• Define target metrics with ranges of domain-specific tasks such as accuracy, latency, cost per query, and user satisfaction in blind testing.
• Identify your “kill criteria” if accuracy is less than 70% after week three and pivot your approach.

Second week: Data preparation and baseline testing

• Compile representative test cases covering edge cases.
• Create evaluation rubrics.
• Test baseline model (e.g., GPT-4, Claude, etc.) without customization.
• Document failure modes and misalignment patterns.

Third week: Implement and iterate

• Deploy your chosen approach [retrieval-augmented generation (RAG), fine-tuning, or a hybrid approach (RAFT). See decision matrix below].
• Run A/B tests: baseline vs. customized model.
• Conduct a mid-sprint review with technical and business stakeholders.
• Refine prompts, retrieval logic, or training data based on failure analysis.

Fourth week: Demo and scale planning

• Create a live demo with three use case scenarios.
• Document cost per query, infrastructure requirements, and maintenance needs.
• Present to executive sponsors with go or no-go recommendation.
• If greenlit, create a 90-day production deployment roadmap.

By engaging the right people early and refining continuously, you set the stage for a scalable, successful AI initiative.

Refine proofs of value with RAFT

Generic AI models often lack organizational context, leading to inconsistent performance and compliance headaches.

As rising inference costs and governance concerns add complexity to AI testing, Ganesh Sharma Tyagali, CTO at NStarX.ai, reveals that IT leaders are turning to one of three strategies to improve their prototypes: RAG, fine-tuning, and agentic AI.

RAG grounds large language model (LLM) responses in external, domain-specific data to reduce hallucinations and accelerate time to value. However, organizations may struggle to optimize performance and latency.

Fine-tuning trains models on carefully curated domain-specific datasets for better accuracy, privacy, and long-term cost efficiency, though it comes with high upfront costs and computational requirements. To do fine-tuning right, you need deep AI engineering expertise – a talent gap many organizations have yet to close.

Agentic AI automates tedious workflows, enables multi-step reasoning, and maintains context across interactions. However, agents often require complex orchestration and governance to implement correctly.

These strategies come with their own pros and cons, but Tyagali suggests another, balanced approach: hybridizing with retrieval-augmented fine-tuning (RAFT).

How RAFT works

RAFT combines real-time context retrieval with deeply integrated knowledge for scalable, accurate results. Tyagali breaks it down as such:

1. First, your team fine-tunes your AI model on domain-specific data.
2. Then, you train the model to distinguish useful information from irrelevant noise.
3. Once it is fine-tuned, deploy your AI with a RAG pipeline for real-time data retrieval.
4. As a result, your model will know what information to look for and how to use retrieved context to generate ideal responses.

RAFT – and all these strategies – are far more involved than these brief explanations, but that’s the beauty of working with an experienced partner like SHI. We break down overwhelming intricacies into plain language that all your stakeholders can understand. Our expertise and proven experience deploying AI at scale help make the difference between a frustrating, half-baked model and a refined, customized AI that helps your users reach their full potential.

Decision matrix: RAG vs. Fine-tuning vs. RAFT vs. Agentic AI

Of these refinement methods, which one is best for your organization? Use this matrix to help make your decision:

SHI AI & Cyber Labs: Imagine. Experiment. Adopt.

Custom AI development requires a highly skilled team, from quality assurance (QA) engineers and solution architects to UX designers, senior data scientists, and more. For most organizations, it’s a talent gap they cannot fill fast enough.

In part one of this series, our experts discussed our AI center of enablement (AICOE), an engagement focused on six core capabilities: vision, AI portfolio, people, use cases, governance, and measure.

With our AI & Cyber Labs, we take this framework even further, giving you the facilities and expertise you need to prototype and iterate upon your ideal AI model. Built as part of our key partnership with NVIDIA, these labs are a trustworthy, proven path toward reaching your AI destination.

We make AI work through a simple yet effective mantra: Imagine. Experiment. Adopt.

Imagine:

When you first engage our AI & Cyber Labs, we’ll work with you to workshop the art of the possible (AotP) with generative AI. We’ll prioritize your use cases based on value and complexity, then finalize prototyping candidates. In this phase, we’ll help identify any gaps in data, infrastructure, or governance that could derail your initiative.

Experiment:

Then, our experts will refine the scope of your use cases and identify applicable generative AI patterns and solution architectures. Together, we’ll define your ideal user experience and experiment with the model(s) you’ve selected. During iteration, we’ll build a working prototype – either in our lab or your data center. We’ll rapidly test hypotheses, with weekly stakeholder reviews to ensure we’re on track. Our team has deployed 100+ enterprise AI models, so we can help you avoid common pitfalls like data leakage, prompt injection vulnerabilities, and infrastructure bottlenecks.

Adopt:

Once you’re happy with your AI prototype, we’ll build out the roadmap for deploying your model at scale. We’ll implement security and privacy guardrails and establish ways to continuously monitor performance and user feedback. As we move to deploy, we’ll plan and measure adoption metrics to ensure you’re getting full value from your AI model. This includes setting up MLOps pipelines, establishing model governance committees, and training your internal teams to maintain and improve the model over time.

The AI road trip: Don’t join the 95% who get lost

Like any cross-country road trip, the destination of your AI POV is always exciting, but you’ll never get there if you don’t plan for the journey. 95% of AI deployments fail to deliver value because 95% of organizations fail to take the right approach.

As you look to innovate with generative AI, here’s how you can join the 5% who succeed:

1. Learn why most AI pilots fail by reading part one of this blog series.
2. Complete the POV readiness self-assessment to identify gaps before you start.
3. Use the POV framework to prototype and iterate with clear metrics and feedback from both technical and non-technical stakeholders.
4. Reference the decision matrix to choose whether RAG, fine-tuning, RAFT, or Agentic AI is the right approach for your organization and refine and improve AI responses and data retrieval.
5. Prioritize use cases, experiment with multiple AI models, and implement privacy guardrails with our AI & Cyber Labs.

Remember: you don’t have to undergo your AI journey alone. With our AI & Cyber Labs, we turn your AI pilot into a structured, proven experience. We make it easy to prototype your preferred models and build out use cases and demos that any stakeholder can understand.

NEXT STEPS

Ready to prototype your ideal AI model and join the 5% who see real value with their POVs? Contact AI@SHI.com to connect with our experts and see how our AI & Cyber Labs can help.

Ganesh Sharma Tyagli is Chief Technology Officer (CTO) at NStarX, a practitioner-led, AI-first engineering services provider. Ganesh has over two decades of IT industry experience spanning SDLC management, zero-to-one product incubation, product engineering and delivery, system architecture, AI and machine learning, and more.