- Technology leaders need to assess the technical feasibility and the business value of AI initiatives before they make a significant investment.
- They need to show not only that AI use cases can work as intended but also that they will yield tangible business benefits to secure support for a larger pilot or implementation.
- Leaders face time constraints to demonstrate the viability and benefits of AI use cases. If the assessment takes too long, it could lead to funding delays or missed opportunities.
Our Advice
Critical Insight
Identify relevant capabilities to support your AI use case, and streamline your vendor selection process to find the right implementation partner.
Impact and Result
- Identify relevant capabilities to support the AI use cases you identified.
- Validate AI use cases against business requirements.
- Identify vendors to support AI use cases, devise a vendor selection model, and select products for review.
Workshop: Launch Your AI Proof of Concept
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Module 1: Explore the Relevant AI Market
The Purpose
Validate the AI use case and explore relevant vendors.
Key Benefits Achieved
Activities
Outputs
Validate use case.
- AI pilot use case scope
Validate success criteria.
- Validated success criteria
Identify AI marketplace capabilities and trends.
- Alignment on vendor category and longlist of vendors for consideration
Evaluate existing technology stack.
- Initial system design for AI use case
Module 2: Gather Requirements
The Purpose
Key Benefits Achieved
Activities
Outputs
Identify and prioritize requirements.
- List of prioritized requirements that satisfies the given use case
Compare vendor features and capabilities.
- Validation of use case coverage across different vendors
Draft a vendor shortlist.
- A vendor shortlist based on capabilities and use case coverage
Module 3: Evaluate Vendors
The Purpose
Key Benefits Achieved
Activities
Outputs
Create a vendor selection model.
- A vendor selection model to be used to evaluate vendor shortlist
Build interview scripts.
- Interview script to ensure reliability and consistency during the interview process
Post-workshop: Conduct vendor interviews.
- (Post-workshop) Completed vendor interviews
Validate technical issues.
- Known list of technical considerations
Module 4: Prepare Implementation
The Purpose
Key Benefits Achieved
A validated implementation plan that can be executed by your team.
Activities
Outputs
Determine the internal capabilities needed to execute your proof of value.
- List of internal capabilities required to execute the proof-of-value pilot
Review implementation activities.
- A validated list of implementation activities to leverage during the pilot
Launch Your AI Proof of Concept
Accelerate your implementation.
WORKSHOP OVERVIEW
Analyst perspective
Look before you leap.
![]() |
The first goal of a proof of concept (PoC) is to determine the technical feasibility of your AI use case. An AI PoC aims to answer whether the technology can work in a real-world environment, identify technical and integration challenges, and mitigate risks before proceeding to a larger-scale implementation. In contrast, a proof of value (PoV) focuses on demonstrating the broader business value and benefits of the technology. An AI PoV aims to answer whether the technology can deliver tangible benefits, such as cost savings, efficiency improvements, or revenue increases. A successful PoV will show not only whether the technology can perform the intended functions, but also whether it will achieve specific business outcomes. PoVs are more resource intensive than PoCs, but the deeper exploration of the technology's impact on the business is well worth the effort. Determine whether the AI use cases under consideration can work as intended, but don’t forget to assess their business value as well before committing to a full-scale implementation. Michel Hébert |
Executive summary
Your challenge |
Common obstacles |
Info-Tech’s approach |
---|---|---|
|
|
|
Info-Tech Insight
Streamline your vendor selection process to identify relevant capabilities to support your AI use case and find the right implementation partner.
Your challenge
You need to evaluate the feasibility and value of AI initiatives.
- Technology leaders need to assess the technical feasibility and the business value of AI initiatives before they make a significant investment.
- Proofs of concept are only a part of the equation. Leaders need to show not only that AI initiatives can work as intended, but also that they will yield tangible business benefits.
- Feasibility and value assessments face significant time constraints. If the project team takes too long to secure support for a larger pilot or implementation, it could lead to funding delays or missed opportunities.
- Assess if the technology selection and enablement were “done right” with positioning proof of value first in your organization.
- Build organizational “AI muscle” before leaping into a full deployment at scale.
Common obstacles
Finding suitable vendors can be difficult without industry expertise and standards.
- Leaders struggle to define relevant use cases effectively, validate AI use cases against business requirements, and find the right vendor to support them.
- AI use case assessments are smaller in scale than full implementations, but they still involve licensing, hardware, and personnel costs. Managing limited budgets well is essential.
- Meanwhile, organizations lack AI expertise to assess the explosion of potential use cases. Without established standards or reliable data, it can be difficult to conduct meaningful vendor selection.
- As always, subjective vendor content marketing and slick sales presentations can obscure the real capabilities of the product.