AI Project Discovery: A 5-Step Roadmap to Real Business Value

Bringing artificial intelligence into your business processes can be a tough initiative to start.

Many businesses know they want AI, but they don’t know what they want the AI to do. Unfortunately, it isn’t magic. It’s a strategic business tool that requires a clear path for integration and implementation.

It requires a partner.

This action plan breaks down our 5-step partnership plan, showing you exactly how we will work together to turn your business challenges into AI-powered systems that deliver measurable ROI.

What is AI Project Discovery?

Project discovery is the part of the process in which our team brings together all stakeholders, gets to know you and your business, and understands what drives your day-to-day operations. That information is used to create a map of your processes, constraints, and data landscape. That working map becomes the baseline for prioritizing the highest-value opportunities for AI implementation.

Why is AI Project Discovery Important?

AI project discovery is important for risk mitigation and cost control by laying the foundation of good decisions in the realities of the project. It brings constraints, data quality issues, and dependencies to the surface, helping align project stakeholders on the problems that need to be solved, and protecting the scope from costly changes later in the development cycle.

What’s the Main Outcome of AI Project Discovery?

The main outcome of AI project discovery is the creation of a shared vision for the business problem that AI needs to solve and the data needed to make that possible. With the benefit of that clarity, the team can determine what data needs to be collected, cleaned, structured, and labeled as well as what model training and overall strategy will be needed to implement the solution.

That data then gets combined with our technical expertise to ideate and produce the best AI solution for the problem. Determining those solutions will also determine the type of data that has to be collected, cleaned, and used, as well as the type of model and training needed to implement an overall strategy.

How Does AI Project Discovery Work?

The AI project discovery process normally involves a kickoff, use case brainstorming and ideation, technical viability analysis, proof of concept (POC) creation, and the development of a detailed roadmap to plan the implementation based on findings from the process.

Those steps are really just an overview, or intent we use to create a practical playbook. We translate that intent into execution with our 5-Step AI project discovery roadmap.

Provato’s 5-Step AI Project Discovery Roadmap to Real Business Value

Our 5-step process to AI project discovery is run as intensive, focused workshops that dive directly into outcomes. We’ve learned how important it is to get stakeholders aligned on goals, KPIs, and their shared view of high-impact use cases. After validating data, platforms, and skills, a POC is created to prove value.

This process can run over the course of days or weeks depending on the size of the project.

Step 1: Project Discovery and Alignment

Before we write even a single line of code, we need to fully understand the world your business operates in. That process is known as project discovery.

What is Your Role In AI Project Discovery?

You need to share your biggest headaches, your most important goals, and what success looks like for your business. It helps to provide examples, screenshots, sample data, process documents, and edge cases. Your active participation is key to understanding what you need the most help with.

Occasionally, it can be hard to focus on just a few pain points in the business that you’d like to address. In this case, we often recommend thinking about the tasks or jobs that drain your team of motivation and energy while also being a poor use of “human time.” The best AI solutions for pilot programs often revolve around automating repetitive jobs, so that humans are freed up to address tasks that have a greater business value and regular nuance or context to do well. By telling us what those lower value tedious jobs are, we can help you address them for immediate positive ROI.

What is Our Role In AI Project Discovery?

We will assess your current data readiness, technical capabilities, interview staff to capture true business needs, and establish a solid project management foundation. This helps us develop a shared view of risks, success metrics, project goals, business processes, and completion milestones between your team and ours.

Step 2: Ideation, Strategy, and Justification

Step 2 focuses on building the blueprint and the business case. This is where things move from “what if” to “here’s how we will do it.”

What is Your Role in Ideation and Planning?

We need you to be an active participant in ideation sessions. You have unique industry expertise that simply cannot be replaced in any other way. You will provide the data and access to us that is needed in order to validate the project’s feasibility and value.

What Is Our Role in Ideation and Planning?

Our role is to facilitate the brainstorming workshops that will bring the high-value use cases to the surface of the discussion. Our goal is to create a compelling business case for your leadership with data-backed ROI model development.

Step 3: Implementation

In step 3, the solution is brought to life. Small-scale tests become working products that your team can start using, transitioning learned concepts to validated POCs.

What is Your Role in Implementation?

When it comes to implementation, the main asset you need to provide is a small team of end users who can test the MVP and provide honest, clear, and detailed feedback about what works, or what doesn’t and how it can be improved upon. These subject matter experts will help guide the project towards success and smooth adoption once it is fully integrated with your existing workflows.

What is Our Role in Implementation?

Our north start at this point is the creation of a POC to test the idea against the main problem we are attempting to solve. Once the POC is validated, a MVP will then be created, which involves the model development and data engineering required to build that first functional version of the solution.

Step 4: AI Integration, Deployment, and Adoption

This phase is all about getting the newly built solution into the hands of your team and users. We handle the AI integration into your daily business processes and workflows so that it can begin producing value. The goal is a fully deployed AI solution that is embraced by a well-trained team.

What is Your Role in Integration, Deployment, and Adoption?

In this step you need to be an evangelist for the product internally. You will need to help us be sure that our integration is intuitive for users. Finally, you will want to encourage your internal teams to embrace the new, smarter way of working.

What is Our Role in Integration, Deployment, and Adoption?

We handle the integration of the new AI solution into your existing systems and workflows. Development shifts to change-management, and helping train your team so they know how to use the new tools.

Step 5: Scaling and Growth

The first project is only the start of a large technology revolution. This final phase is all about helping you build on success to create a lasting culture of creating business value with AI.

What is Your Role in Scaling and Growth?

You must identify individuals in your organization who can become your AI leaders. You need to have them start thinking about the future and how they can apply AI in other areas of your business.

What is Our Role in Scaling and Growth?

Our team will conduct a knowledge transfer if it applies. Either way, we will revisit the project roadmap and help you identify the next high value opportunity for AI implementation.

What are the Benefits of AI Project Discovery?

The benefits of AI project discovery are:

• Risk Mitigation
• Increased ROI
• Stakeholder Alignment
• Prevents Scaling Inefficiencies
• Feasibility Confirmation
• Expectation Management

By going through the AI project discovery, organizations confirm the feasibility of a solution, manage expectations, and reduce risks. This process prevents institutionalizing poor workflows while building alignment among stakeholders, both of which drive investment in high-value opportunities that deliver tangible ROI and business value.

What Challenges Should You Expect in AI Project Discovery?

Some challenges you should expect during AI project discovery are:

• Poor Data Quality
• Lack of AI Expertise
• Confusing AI for Traditional Software
• Lack of ROI Clarity
• Bias and Ethics Concerns
• Legacy System Integration
• Low Initial Employee Adoption

These challenges are common and manageable when proactively addressed at the onset of project discovery. By knowing what blockers exist upfront, handling them one by one, and planning for iteration as the challenges evolve, you are able to de-risk delivery and accelerate your time to realized value.

Beyond technical hurdles, there are governance issues that need to be identified during discovery as well.

The Importance of Responsible AI During Discovery

During project discovery you will define what’s possible, but you also need to identify what’s appropriate. Responsible AI helps by:

• Protecting organizations and society against biased outcomes
• Protecting and scales against current and future regulation and compliance demands
• Safeguarding the long-term viability of solutions built, keeping them sustainable and resilient
• Building trust by promoting the explainability of the system and how it operates

By accounting for these early in the process through discovery, you will be in a better position to avoid bias, privacy issues, and compliance risks down the line.

Who Should Be Involved in the AI Project Discovery Process?

AI project discovery must involve a cross-functional team comprised of:

• Leadership and Stakeholders
• Technical and Data Experts
• User Experience and Project Management Owners
• Governance, Legal, and Ethical Advisors

In our experience, it is best to have a diverse set of perspectives on an AI discovery project. Doing so drives the delivery of real business value, ethical standards adherence, and alignment with company values.

Leadership and Stakeholders

This group helps guide the project strategy and keeps it focused on true business needs, not just technical hurdles. Typically speaking it will involve four main roles.

At the top of the group hierarchy is an executive sponsor to secure funding, resources, and helps create buy-in across the organization. Below them is a product owner who acts as the visionary for the end product, working with technical teams to articulate the problem and desired outcomes of the solution.

Below those two roles is the business analyst who translates business needs into actionable project requirements. Lastly in this group comes the domain expert. The domain expert provides the deep knowledge needed to create a solution that is relevant and effective in it’s intended business context.

Technical and Data Experts

Our team normally comprises the needed technical and data experts for the project. Those roles start with an AI architect who designs the high-level architecture of the solution, from tech stack to final integration. A data scientist will also need involved to uncover patterns that they will use to design, test, build, and evaluate machine learning models.

Next comes a data engineer who handles the pipelines and infrastructure needed to collect, clean, and make data accessible to train models with. A machine learning engineer’s work focuses on tuning the machine learning models into scalable, integration ready solutions.

User Experience and Project Management Owners

This is the team that keeps a project on track. They facilitate communication between the various teams and their members to make sure that the final product meets the user needs.

The team will be lead by a project manager who oversees the entire discovery process to maintain timelines, milestones, and budgets. It must also include a UX/UI designer to ensure the AI solution is intuitive for users and how they will interact with it. There should also be some end users involved as the target audience to provide feedback on usability and problem resolution.

Governance, Legal, and Ethical Advisors

This group of governance, legal, and ethic experts is vital for mitigating risks from the very start. There must be a role responsible for AI ethics who will address concerns of fairness, bias, transparency, and explainability. A legal or compliance expert makes sure all of the data used adheres to any privacy regulations and standards that exist.

Why a Consultant Is Important in Discovery?

AI consulting offers an objective perspective that can challenge assumptions and eliminate blind spots. Gaps between what the assumed problems are and what the data points to are often invisible to internal teams.

Our external facilitation slices through organizational politics and accelerates the path from vague wants to actionable specifics, taking the whole process from months down to weeks.

What Are The Deliverables of AI Project Discovery?

The deliverable of AI project discovery are:

• A Product Vision Statement
• Feasibility Assessment
• Implementation Strategy
• Clearly Defined Project Scope
• Proof-of-Concept Creation
• List of Requirements
• Project Budget

These deliverables create the cohesive blueprint needed for moving from concept to completion so that the next step can begin: The Project Lifecycle.

What Comes After AI Project Discovery?

After project discovery comes product, solution, or custom AI model development.

The AI Project Lifecycle

AI project discovery is just the beginning of a larger process. Once the discovery phase is complete, the full development AI project lifecycle begins. This process involves data preparation and engineering, model development and training, deployment and integration, monitoring and maintenance (MLOps), and lastly scaling and expansion. You could say, this is where the rubber meets the road, and is the most time consuming part of any AI development services project.

Laying The Groundwork for Custom AI Model Development

A throughout AI project discovery process will lay the needed groundwork for custom AI model development. Custom AI model development is the process of building a highly specialized and customizable AI model to solve a problem specific to one business, or business need. The bespoke artificial intelligence models developed serve only the unique needs they were created for, and don’t work to solve other problems or business needs. The discovery process gives the teams everything they need to identify the problem(s) custom models need to solve.

Designing Sustainable AI for Longevity

As you move from discovery into implementation, sustainable will become an important factor to keep in mind. Sustainable AI keeps solutions efficient, adaptable, and responsible in their use of data, compute power, and human oversight.

Building AI systems is hard enough already. It’s worth investing in building a system that will last.