AI is moving faster than most companies can keep up. Leadership teams are spending more time and budget, yet very few have a concrete view of where it will drive financial impact, or how to roll out solutions without introducing operational risk.
Across the companies we work with, the pattern is the same: AI usage is up, but ROI is stagnant. Not because the technology isn't capable, but because teams are investing in scattered experiments instead of the right problems.
A real AI strategy forces discipline. It clarifies your business constraints, quantifies expected return, and evaluates feasibility and risk before you commit resources. It keeps teams focused on initiatives that expand capacity, improve margins, and scale without risk.
This guide breaks down how to avoid wasted spend, find high-value opportunities, and add AI safely. By the end, you'll have the structure to build a board-ready AI roadmap for the next 12 months, following the same process we've used to uncover more than $5M in efficiency gains for companies like yours.
The Problem
AI is overwhelming and leading an uncertain future, especially for service-based businesses, where the core delivery mechanism is people.
We see the same patterns in nearly every company we work with:
- "We know AI is our biggest threat but don't know how to move forward"
- "Our team uses AI every day, but we haven't seen meaningful financial impact"
- "We have tried AI projects, but the results were not strong enough to scale"
All of these point to the same issue - teams are adding AI without a plan that ties back to business results.
Most companies start by testing tools, running small pilots, or trying ideas that sound useful. Very few connect those efforts to real outcomes, which leads to disjointed projects that never make business impact.
What an AI Strategy Is (and Is Not)
Many leadership teams are doing the right things - exploring AI, testing tools, and encouraging innovation. But even with all that activity, very few can point to a clear return on investment.
That gap isn't due to a lack of effort, it's because teams are missing a structured plan that ties AI decisions directly to business goals.
That's what an AI strategy solves.
| Not an AI Strategy | A Real AI Strategy |
|---|---|
| "Our team uses ChatGPT internally." | "We understand where AI will improve margins." |
| "The team buys AI tools." | "We have a roadmap tied to financial outcomes." |
| "We told the team to experiment." | "We score every idea on impact, feasibility, and risk." |
| "We hired someone to add AI to the business" | "We analyzed the core constraints to solve first." |
A real strategy gives clarity on what to invest in, how much value it can create, and how risky it is. It turns scattered ideas into a concrete plan that leaders can trust.
1. It Quantifies ROI and Reduces Risk
Our strategy work always starts with measuring the size of the opportunity. In most engagements we find around $500k/year of work that can be automated or improved. The outcomes are measured and ranked before building any solutions.
A strategy should answer simple questions.
- What should we fix, and why?
- How will we measure the return?
- What steps reduce risk before we scale a solution?
2. It Brings Clarity to What You Should Build, Buy, or Ignore
A strategy acts as a filter, it helps you separate high-value projects from distracting ideas. It gives you a way to evaluate which efforts will move the business forward and which ones are "cool" but not worth the time.
It helps you answer questions like:
- Which processes affect profitability the most?
- Which ideas sound good but will not change outcomes?
- Where better data, better tools, or better processes are needed before AI can help?
Once everything is framed this way, the right priorities become obvious.
3. It Helps You Prioritize by Business Impact
Every opportunity should be scored on impact, feasibility, time to value, and risk. This takes emotion out of the conversation. Leaders can see the tradeoffs and understand where to focus.
It brings the same discipline to AI that you expect in financial planning.
4. It Translates Vision Into a Technical and Operational Roadmap
A strategy is not complete until you know how it will be built and adopted.
That means creating:
- A technical plan showing how each system will connect
- A rollout plan for the teams involved
- A simple measurement system to track results
This ensures the plan survives outside the slide deck.
5. It Protects You From Bad Investments
Most failed AI projects fall into a few categories.
- Tools were chosen before problems were defined.
- ROI was never measured.
- Projects stayed stuck in pilot mode.
A strategy prevents those mistakes. It sets success criteria at the beginning, aligns all stakeholders, and ensures every project holds up under scrutiny.
How to Build Your AI Strategy
Start With Constraints, Not Ideas
Now to begin on building your AI strategy. First, we need to set the frame for our approach and ask the right questions. When most teams begin exploring AI, the first question they ask is:
"How can we use AI in our business?"
That question leads straight to tool-chasing. Everyone brings up agents, copilots, or tasks they want to automate. The conversation sounds exciting, but none of it is tied to business outcomes.
A better question is:
"What metric matters most right now, and what is holding it back?"
That shift from "how" to "what" is the foundation of every successful AI strategy.
1. Focus on Business Constraints, Not Technology
Every company has a few core constraints that limit growth or profitability. They usually fall into these categories.
| Common Constraint | Impact | Example Questions to Ask |
|---|---|---|
| Capacity | You're limited by people-hours | "Where is the team stretched the most?" |
| Margins | Costs rise faster than revenue | "What work consumes time without adding value?" |
| Client Retention | Losing accounts due to service gaps | "How do we deliver more value with the same team?" |
| Speed & Responsiveness | Deliverables take too long | "Where are we waiting for data or manual checks?" |
| Risk & Compliance | Manual checks or reviews slow progress | "What communication is slowing progress?" |
Once you know the constraint, you can map how time and money flow through the business and find where technology can meaningfully help.
2. Work Backward From the Outcome
Use a simple structure to keep the focus on outcomes instead of novelty.
- Identify the constraint
- Quantify its cost
- Map the existing workflow
- Ask how technology can reduce or remove the issue
3. Why This Lowers Risk
When you anchor everything to a clear constraint, the risk drops.
You avoid speculative ideas. You measure progress clearly. You can show the before and after. Leadership support becomes easier because the numbers tell the story.
You are solving business problems with AI, not the other way around.
The ACT Framework (The "How")
Now that you've identified your core business constraints and, we need to structure your AI opportunities into a step by step roadmap. This is where the Hilo Labs ACT framework comes in:
Assess → Create → Transform
This is the same system we've used to help 8-figure SaaS companies, agencies, and service firms find millions in hidden efficiency.
Assess: Find the Real Opportunities
Most companies underestimate how much time is buried in manual work. Assessment brings that to the surface.
The goal: Uncover where time, cost, and risk are trapped inside your workflows and define the business case for fixing it.
Steps:
- Interview leaders and operators
- Map processes across sales, delivery, operations, support, and finance
- Collect data on hours spent, errors, rework, and delays
- Score each workflow on impact, effort, feasibility, and risk
This produces an opportunity map showing where automation, software, or AI can drive measurable results.
Output: A ranked list of opportunities with financial impact, data needs, risk, and ownership.
Create: Build the Roadmap
The Create phase takes the opportunity list and turns it into a plan.
Step 1. Map Each Opportunity
For every item:
- Describe the current process
- Design the future version
- Decide whether to buy or build
- Estimate time and cost
- Identify risks
- Measure expected return
Step 2. Prioritize
Group projects by complexity and value.
- Quick wins (30 to 90 days)
- Strategic builds (3 to 12 months)
- Larger transformations (12 months and beyond)
Start with wins that show clear progress.
Step 3. Assign Owners and KPIs
Every project should have a business owner, a technical owner, and two or three KPIs tied to real outcomes.
Final Output
A complete roadmap that shows projects, costs, return, owners, and timelines.
Transform: Safe Implementation and Rollout
The Transform phase is where solutions get built and rolled out across teams.
Measurement and Success Metrics
Once implementation begins, set up systems to measure:
- Usage metrics – how often the AI or automation runs and what tasks it completes.
- Financial metrics – margin improvement, cost savings, or time reduction compared to baseline.
- Quality and reliability metrics – error rates, response times, and exceptions requiring human review.
Visibility is key - you should always know what the AI is doing, how it's performing, and what it costs to run.
Rolling Out and Training the Team
Rollout depends on the type of system.
- Fully automated systems run quietly in the background
- Human-in-the-loop systems need review steps
- Assistants work directly with staff and require hands-on training
Adoption improves when teams understand how a system helps them and what to do when it needs input. The goal is to make these systems part of normal work, not a separate "AI project" the team resists or forgets.
How to Get Started
The best results happen when business, process, engineering, and AI expertise come together.
If you want guidance, our team runs a complimentary strategy workshop where we help you identify high-ROI opportunities, build your roadmap, and move from ideas to real results.
Making Your AI Transformation A Success
The companies seeing strong results follow a few simple habits. They focus on reliable work, low risk, and clear measurement.
Below are the principles we see across the most effective teams, based on our learnings working with 30+ companies.
1. The Back Office Is King
More than half of AI spending today goes into marketing and sales. Those projects often have the lowest success rate.
The biggest returns usually come from automating repetitive internal tasks that happen the same way every time. These workflows are predictable and easy to measure. Automating them provides clean, dependable savings.
Avoid using AI where quality or accuracy directly affects revenue. An AI SDR that sends the wrong message can burn leads. A simple workflow automation that handles repetitive reporting will never do that.
2. Don't Fixate on Agents
Agents attract attention, but most businesses are not ready for them.
The real value often comes from organized data, better internal tools, and simple automation. Many companies have large savings available without touching advanced AI.
Agents become useful once the foundation is solid.
3. Automation Comes First
Automation has clear rules and stable outputs. It's faster to build, easier to monitor, and produces consistent results.
4. Adoption Depends on the System
Automated systems do not require adoption. The challenge comes with AI assistants, which rely on people using them effectively.
If a tool relies on staff choosing to use it, the experience must be clean and helpful from day one. Involve users early, keep feedback loops open, and make the tool genuinely easier than the old way of doing things.
5. Build Trust Before Full Automation
Before letting AI make decisions on its own, test it in human-in-the-loop mode by letting people confirm outputs.
These checks build confidence in the system and reveal edge cases early. Once you're sure the AI performs reliably, you can automate fully.
Data & Governance
1. Security and Client Privacy
Any system touching client or internal data needs clear rules. Keep information inside secure environments, limit access to the people who truly need it, and avoid sending sensitive material to public tools without proper safeguards.
2. Auditability
Automated systems should never feel opaque. Keep logs of what ran, what data was used, and the output it produced. When something breaks or a client asks how a result was generated, you can trace it quickly. Clear records make it easier to fix issues, explain decisions, and keep teams accountable.
3. Cost and Usage Visibility
AI creates new operating costs. Track run frequency, model usage, and cost per task. Compare those numbers to the time saved or errors reduced.
4. Human Oversight and Accountability
Even strong systems need boundaries. Define when a person should review outputs, approve decisions, or step in when something looks off. Keep humans involved in high-impact steps, especially anything client-facing or compliance-related.
Want Help With Your AI Strategy?
Our AI Strategy Workshop helps leadership teams find high-value opportunities, build a practical roadmap, and create a plan that the board can support. If you want a clear path to measurable results, book a session with our team.