A Strategic Framework for how to Prioritize AI Projects
Most businesses know they need to do something with AI. You’ve probably got a dozen promising AI ideas competing for limited resources. Figuring out which projects to tackle first becomes the real challenge.
This blog post will go into our framework approach to help you cut through the noise and prioritize AI initiatives.
Want to watch this blog post rather than read it? Here is the video version of this blog post:
Start With Your Strategic Objectives
Before ranking any AI projects, you need clarity on what you’re trying to achieve. AI can solve many problems, but without clear priorities, you’ll end up chasing initiatives that don’t align with your actual business needs.
Common strategic objectives include:
Financial Goals: Most companies right now are focused on cost reduction. If your bottom line needs immediate relief, optimization projects that reduce operational costs should rank higher than experimental innovation projects.
Innovation and Differentiation: Looking to create new revenue streams or stand out from competitors? These projects focus on new capabilities that change how you compete in your market.
Risk Mitigation: Some organizations need governance structures first. If employees are using AI tools without guidelines, or if you’re in a regulated industry, addressing these risks might be your top priority.
Problem Resolution: Specific operational bottlenecks causing daily friction. These are the “if we could just solve this one thing” projects that everyone knows about.
Culture and Talent: Attracting and retaining top people. AI projects that make your company more appealing to skilled workers or reduce frustrating manual work can support talent goals.
Knowledge Democratization: Breaking down information silos. When critical knowledge lives only in certain people’s heads, AI can help capture and distribute that expertise across your organization.
Most companies we work with have multiple objectives. That’s fine. Just rank them. Make it clear that Financial is priority #1, Problem Resolution is #2, and so on. This ranking becomes crucial later when comparing different projects.
Check Your Foundation First
Sometimes the best “AI project” means fixing the basics that will make AI projects actually work.
We wrote an entire piece on assessing your organization’s AI readiness that covers this in depth, but the core insight is simple: you’re only as strong as your weakest link.
Evaluate your organization across seven domains: Strategy, Product/Value tracking, Governance, Engineering capability, Data infrastructure, Operating Model, and Culture & People. If you score high on technical capability but low on governance, you’re creating risk. If you have great strategy but poor data infrastructure, AI projects will struggle.
Before committing resources to an ambitious AI initiative, honestly assess where you stand across these domains. Your weakest area becomes your highest priority to address.
In this example diagram we see an organization that needs to focus on AI initiatives that are value generators and more governance. Focusing on these two aspects first will raise the bar overall and get the org to level-2 since the org is only as strong as its weakest area.
Another way to assess readiness: look at how AI integrates into your operations. Your organization’s or department’s maturity level determines what projects make sense:
- Foundations: Basic AI literacy, learning to prompt effectively
- Augmented Services: Human doing the work, AI helping
- Integrated AI: AI helping with work, human reviews baseline work done by AI
- AI-First: AI handles agentic tasks, human monitors for quality assurance
Quick Wins vs. Long-Term Transformation
Once you understand your objectives and readiness level, you need to decide on your approach: are you looking for immediate validation or building toward larger transformation?
Quick Wins help you:
- Prove AI delivers value quickly
- Build organizational confidence
- Generate momentum for larger initiatives
- Show fast returns to leadership or boards
North Star projects offer:
- Compounding value over time
- Lower long-term risk through thoughtful design
- Stronger competitive differentiation
- More effective change management
Select the right approach for your business depending on what is important to you, your team/department or company. Make sure it aligns with your goals.
If you do choose a North Star approach, make sure it’s modular. Break that vision into discrete projects that each deliver value. For example, one of our manufacturing clients envisions an “AI Engineer” that can generate quotes, answer technical questions, and automate routine engineering tasks. Rather than trying to build everything at once, we’re tackling quote generation first, then expanding to other capabilities. Each phase delivers measurable value while building toward the larger vision.
The Top Five Quick Win Categories
If you want to start with quick wins, these five areas consistently show high returns with reasonable implementation timeframes:
- Customer Service Automation: Chatbots and support systems that handle routine inquiries
- Marketing and Sales Content: AI-assisted content creation for SEO, social media, and campaigns
- Internal Knowledge and Productivity: Making tribal knowledge accessible and accelerating routine tasks
- Administrative Workflows: Automating repetitive processes that consume staff time
- Quality Control and Inspection: AI-powered checks that catch issues faster than manual review
These areas can work well as starting points because they typically:
- Address existing processes you already understand
- Require less specialized expertise to implement
- Build organizational trust through visible improvements
- Create data and processes that support future AI initiatives
Don’t just chase quick wins because they’re easy. If your business faces a crisis that requires longer-term transformation, quick wins won’t solve it. Sometimes you need to go deep.
Understanding Your Constraints
Effective prioritization requires knowing your boundaries. Every organization has constraints, and ignoring them leads to failed projects.
Map out these constraints before ranking projects:
Time and Budget Limits: What’s the maximum you’re willing to invest? If a project exceeds these limits, can you break it into smaller phases?
Risk Tolerance: How much uncertainty can your organization handle? A 5% chance of complete failure might be unacceptable even if the potential upside is high and longlasting.
Regulatory and Compliance Requirements: Legal limitations can dramatically increase project complexity. One client discovered halfway through planning that their compliance requirements tripled the project scope. Better to know upfront.
ROI Minimums: Some projects might be worthwhile in isolation but don’t clear your organization’s return threshold. Be explicit about what ROI you need.
External Dependencies: Are you relying on vendors, partners, or systems outside your control? Those dependencies could push a project beyond your constraints.
Organizational Capacity: How many projects can you realistically manage simultaneously? We see companies try to run five AI initiatives per year when they only have bandwidth for two.
The Project Ranking Framework
Now we get into the practical mechanics of comparing projects. We evaluate initiatives across six key criteria:
1. Goal Alignment
How well does this project support your stated objectives? If reducing costs is your #1 priority, a project that offers strong cost reduction ranks higher than one focused on innovation, even if the innovation project sounds more exciting.
For example, here we see the priorities of this business are: Financial (1), Problem-solutions (2), and Risk mitigation (3). With these 3 criteria in mind, the project: “Customer service automation” is better aligned than the other two projects.
2. Constraint Fit
Does this project fit within your constraints? If it requires 18 months and you need results in 6, it either needs to be broken down or moved aside for now.
3. Urgency
Some problems can’t wait. If a key employee is retiring in six months and taking critical knowledge with them, capturing that knowledge becomes urgent regardless of ROI calculations.
4. Implementation Effort
Lower effort doesn’t automatically win, but when comparing two projects with similar returns, the one requiring less effort rises to the top. Effort includes technical complexity, data requirements, change management needs, and integration challenges.
5. Expected Impact
What measurable improvement does this project deliver? Time saved, money earned, problems solved, knowledge democratized. Quantify this as specifically as possible.
6. Strategic Factors
These are the intangibles that don’t fit neatly into other categories. Maybe a project supports an important partnership, aligns with your competitive positioning, is important to one of your board members, or creates capabilities that enable future initiatives. These factors can tip the scales between otherwise similar projects.
Quantifying Project Value
Ranking requires making projects comparable. That means translating different types of value into common terms.
For optimization projects (reducing time or cost), quantification is straightforward:
- Current weekly hours spent on task X → hours saved with AI
- Current error rate causing rework → errors prevented with AI
- Current cost per transaction → cost reduction with AI
For growth projects (new capabilities, new revenue), quantification is harder but still necessary, For example:
- Estimated customer acquisition from new capability
- Projected conversion rate improvements
- Competitive advantage that protects existing revenue
The key steps:
- Interview stakeholders who actually do the work or manage the process
- State your assumptions explicitly so they can be validated or challenged
- Quantify in terms of money and time whenever possible
- Document the risks that could prevent you from achieving these returns
When documenting assumptions, note the accuracy level you need. An AI system that saves time but still requires human review only needs 80% accuracy. One that operates autonomously might need 99%+. Higher accuracy requirements mean more effort, which affects your ranking.
Optimization vs. Growth: Current Economic Reality
Economic conditions matter when prioritizing AI projects. Right now, most businesses are in optimization mode.
Optimization projects focus on:
- Reducing operational costs
- Eliminating wasted time
- Solving painful friction points
- Improving efficiency of existing processes
Growth projects focus on:
- New revenue sources
- Market differentiation
- Competitive advantage
- Unmet customer demand
Optimization projects are generally easier to implement because you’re improving something you already understand. The processes exist, the data exists (or can be easily captured), and ROI is straightforward to calculate.
Growth projects carry more risk. You’re building new capabilities or entering new markets. The expertise might not exist in-house. You need market validation to know if the opportunity is real.
Companies that only optimize will fall behind when the economy shifts. Just be realistic about which projects fit your current business environment and risk tolerance.
Calculating Effort and Impact
To create an objective ranking, you need to score both the effort required and the impact delivered.
Effort factors to evaluate:
- Accuracy requirements: Higher accuracy = more effort
- Data availability: Need to create data from scratch? High effort. Just need to organize existing data? Medium effort.
- Process definition: Well-documented processes are easier to automate than tribal knowledge requiring extensive interviews
- Multiple roles or functions: Each additional role the AI needs to handle increases complexity
- Multi-modal steps: If your process switches between different types of tasks (web scraping, then document processing, then calculations), each mode switch adds effort
- Risk boundaries: The safeguards needed to prevent problems
- Change management: How much training and adoption support is required
- Security and compliance: Regulatory requirements can add significant effort
- Integrations: Each API or system connection increases complexity
- Dependencies: Prerequisites that must be completed first
Impact factors to evaluate:
- Financial impact: Money saved or earned
- Time impact: Hours freed up
- Problems solved: Pain points eliminated
- Innovation value: Competitive differentiation created
- Culture impact: Employee satisfaction improvements
- Knowledge democratization: Information made accessible
- Priority alignment: How well it fits your stated objectives
- Risk reduction: Risks mitigated or eliminated
- Strategic value: Long-term positioning benefits
- Time to return: How quickly you see results
- Emergent value: Whether this project powers future initiatives
When you plot projects on an Effort vs. Return graph, you’ll see which ones deliver the most value relative to the work required.
Projects that fall above the trend line offer better returns than their effort would suggest. Projects below the line require disproportionate effort for the value they deliver. For quick wins, focus on low-effort projects above the line.
Successful AI Projects: The Four Requirements
Regardless of how a project scores on your ranking system, it won’t succeed unless it meets four fundamental requirements:
- Solves one problem and does one job: Don’t try to build a Swiss Army knife AI. Each project should have a clear, focused purpose.
- Has clear process or logical framework: Either the process is already documented, or you can define it as part of the project. AI works best when decision logic is explicit.
- Has supporting data: Either data exists, or you have a plan to create it as part of the implementation.
- Has known technical limitations: You understand what technical challenges exist and how you’ll address them.
Missing any of these four requirements dramatically increases project risk.
Managing the Human Side
Technical frameworks help you prioritize projects. Actually implementing them requires addressing the human side.
I wrote a comprehensive guide on AI change management that covers this in depth, but the core principle is simple: the most sophisticated AI system fails if people won’t use it or actively resist it.
Key change management considerations when prioritizing:
Education is your most powerful tool against resistance. Research shows that people who understand AI (what it can do AND what it cannot do) are significantly less afraid of job replacement, less worried about AI takeover, and more willing to adopt AI systems. If your organization is AI-resistant, education projects might deliver higher returns than implementation projects. When people learn AI’s limitations, they feel empowered rather than threatened.
Frame projects around team benefits, not just business metrics. Leadership naturally thinks about top-line revenue and bottom-line costs. That’s important. But also frame AI improvements in terms of what your team gains: eliminating 10 hours of repetitive work per week, removing annoying manual tasks, freeing people to do more creative and intellectual work. When you tell someone “this AI will handle the tedious clicking and data entry so you can focus on decision-making,” most people are enthusiastic.
Integration beats disruption. Projects that augment existing workflows face less resistance than those that force complete process changes. If you can run old and new approaches in parallel until people are comfortable, adoption improves dramatically. Phased rollouts where people get to use (not have to use) the new system first build confidence before requiring the switch.
Build coalitions that include resistors and experts. The people most resistant to change often flip into the strongest advocates once their concerns are addressed. They’re frequently speaking for the broader team’s worries. Include them in planning alongside your AI experts. The more knowledgeable people you have in your coalition, the more effective change becomes.
If two projects score similarly on technical merit, choose the one with better change management prospects. A slightly lower-ROI project that succeeds beats a higher-ROI project that fails due to poor adoption.
Risk Categories to Consider
Every AI project carries risks. Document them honestly so you can make informed decisions.
Technical Feasibility & Performance: Can the AI actually do what you need? Do you have the technical skills? Will the model accuracy meet requirements?
Security & Compliance: Data privacy concerns, regulatory requirements, legal constraints.
Adoption & Change Management: Will people use it? Do they have adequate training? Is there organizational resistance?
Project Design & ROI: Can you actually measure the returns? Is this aligned with business priorities? Does the data and process readiness support success?
Business Continuity: What happens if a vendor disappears? Do you have the resources to maintain this long-term?
Ethical & Reputation: Could this create bias issues? Are there transparency concerns? What happens if it makes a harmful mistake?
Quality & Expectations: Will it meet stakeholder expectations? Is the uncertainty around outcomes acceptable?
High-risk projects need stronger expected returns to justify the risk. Make these risk assessments explicit in your ranking so everyone understands the trade-offs.
Bringing It All Together: The Decision Matrix
With all this information gathered, you can create a comprehensive project comparison.
Your decision matrix should include:
- Project name, benefit summary
- Monetary and/or time benefits
- Strategic importance (which includes goal-fit)
- Impact/Effort ratio (from your impact and effort calculation)
- Key assumptions (which you may want to state upfront)
- Risk assessment (the biggest risks)
This gives you an objective basis for discussion. When someone champions their favorite project, you can show exactly why a different project ranks higher based on the criteria your team agreed on.
Building Your AI Roadmap
Once you’ve ranked projects, evaluated change management considerations, and documented risks, translate everything into an execution roadmap.
Your roadmap can show:
- Start and end dates for each project
- Dependencies between projects (Project 2 can’t start until Project 1 completes)
- Measurement points where you’ll assess ROI
- Status tracking (approved, tentative, planned)
We recommend aiming for 6-12 month ROI on initial projects. AI is moving too fast to bet on 3-year returns for your first initiatives. If you have larger projects, break them into phases with measurable returns under 12 months each. (24m for large companies or large projects though can be acceptable if well managed and planned)
The roadmap brings together your strategic objectives, readiness assessment, constraints, project rankings, change management considerations, and risk assessments into a single execution plan. This becomes your guide for systematic AI implementation that aligns with what your business actually needs.
Example roadmap derived as final output, showing here a 3-month delay until the first project releases, followed by monthly ROI check points and a strategic review sessions every 3 months.
Your Next Steps
Strategic prioritization of AI initiatives means finding the right projects for your organization’s current situation, objectives, and constraints. Then building a roadmap that systematically addresses them.
To recap:
- Clarify your strategic objectives and rank them explicitly
- Assess your AI readiness across the seven domains. Your weakest areas might need attention before ambitious projects make sense.
- Identify your constraints so you know which projects are feasible and which projects you need to break down into smaller chunks
- Evaluate potential projects using the effort/impact framework we covered
- Consider change management and risks for each prioritized project
- Build your roadmap that sequences projects logically with clear dependencies, measurement points, and realistic timelines
I/we hope you enjoyed this article and it proves useful to you and/or your organization’s strategic planning! Reach out if you have any questions about applying this process, feedback on this article or requests for future content. Sign up for our newsletter [below] if you want to get our latest posts in your inbox. You can also find and follow sebastian on LinkedIn.
