Introduction
The future sounds impressive, but how do we get there?
In Part II – The AI Enabled Finance Organization of 2028 we looked into the future on how AI will reshape financial strategy, reporting, ESG, risk, and workforce planning by 2028. We explored the possibilities. The promises. The edge that early adopters are already starting to realize.
However, a bold vision means nothing without the ability to execute.
This final part of the series is where the rubber meets the road. Because I’ve seen far too many finance leaders get excited about AI, only to watch their initiatives stall, spiral, or quietly disappear into a “pilot graveyard.” Not because they didn’t try but because they didn’t start with the right mindset, structure, or leadership approach.
AI is a transformation, and like any transformation, it demands intention, discipline, and clarity.
In Part III, I’ll share the biggest reasons AI initiatives fail. More importantly, I’ll show you how to avoid those traps. You’ll get a field-tested strategy to drive real outcomes: how to start with value, design for augmentation (not elimination), and scale successfully through fast, focused pilots.
Because the future we talked about in Part II? You won’t get there by buying tools.
You’ll get there by building trust, alignment, and momentum.
The 5 Reasons Enterprises Fail with AI
Even though AI has never been more accessible, success is still elusive. Despite the hype, the headlines, and the explosion of tools, most enterprises are still struggling to extract meaningful value from their AI investments. It’s not because the technology doesn’t work. It’s because the mindset does.
I’ve worked with dozens of companies (from scrappy startups to Fortune 50 giants), and I keep seeing the same five patterns holding them back. If you’re serious about making AI work in your organization, you need to recognize and actively avoid these traps.
1. Treating AI Like Software, Not Like a High-Energy Intern
This is mistaking number one, and it’s the most common. Too many companies assume AI works like traditional software: deterministic, rule-based, consistent every time. However, AI doesn’t operate that way. It learns. It adapts. It can surprise you. Which means you can’t just “plug it in and walk away.” You must supervise it, coach it, and correct it, just like you would a talented but inexperienced intern.
When financial executives deploy AI to generate financial summaries or analyze vendor contracts, you need someone who knows the business to review, refine, and train the system over time. If you expect perfection on day one, you’re setting yourself (and the AI) up to fail.
2. Chasing the “Right Tool” Instead of Customizing for Value
There’s no silver bullet AI product that magically works out of the box for your business, yet I constantly see leaders shopping for AI like it’s an off-the-shelf solution. AI doesn’t work that way. It thrives on your data, your workflows, your customers, and your strategic goals. This means customization is the unlock.
I recently saw a mid-size insurer invest in a top-rated underwriting AI engine. It failed within six weeks, not because the engine was bad, but because no one aligned it to their unique risk model or claim adjudication process. Tools are enablers, not solutions.
3. Asking Technologists to Define the Business Use Cases
I’ve got all the respect in the world for data scientists, engineers, and DevOps pros. But if they don’t understand the financial pain points, regulatory hurdles, or customer realities of your business, they’re not going to design the right AI solution.
And it’s not their fault. It’s yours.
As a leader, you have the domain knowledge. You know where the friction is. Where the bottlenecks are. Where you’re spending too much time, money, or risk. Your technologists can build the system, but only you can define the problem worth solving.
When I help companies bridge that gap (pairing finance leads with AI engineers), the results change instantly. Insight meets implementation. That’s when value is created.
4. Expecting AI to Behave Like a Computer: Flawlessly, Every Time
AI is not a calculator. It’s not Excel. And it’s definitely not deterministic.
It’s probabilistic. Meaning, it will make mistakes. It will hallucinate. It will surprise you; however, it will also find patterns no human could ever detect, offer suggestions you didn’t think of, and adapt to new inputs faster than any rules-based system.
The mistake I see :
Leaders treat AI’s occasional error like a dealbreaker, while tolerating massive inefficiencies in their legacy systems every day.
Here’s the smarter move:
Design guardrails, monitor behavior, and learn from the edge cases. If you try to force AI into old-school precision boxes, you’ll neuter its potential before it even starts.
5. Automating Flawed Workflows Instead of Rethinking the Process
This one’s the silent killer. Too many AI projects are framed like this:
“Let’s use AI to do what we’re already doing but faster.”
The problem? You’re still doing the wrong thing, just faster. You’re still following a flawed workflow. You’re still basing decisions on outdated logic. And now, you’ve layered AI on top of it.
That’s not innovation. That’s optimization theater.
The real opportunity with AI is to rethink the process entirely. Maybe you don’t need to do monthly closes the same way. Maybe fraud detection shouldn’t just happen post-payment. Maybe your pricing strategy needs to shift from competitor benchmarking to demand sensing.
I helped a retail company do exactly that by ditching their manual discount waterfall logic and moving to an AI elasticity model. They reduced discounting errors. More importantly, they increased margins by 5.4%… because they didn’t automate the old. Rather, they designed the new.
What This Means for You
- You must lead the mindset shift. AI success doesn’t start in the IT department. It starts in the business, and leaders must model the shift from “tech-first” to “value-first” thinking.
- You are the translator. Your job isn’t to write Python code but rather to articulate the problem, shape the outcomes, and guide your teams in how AI augments business goals.
- You need a realistic roadmap. AI is a journey. Like any journey, it needs planning, pilots, feedback loops, and recalibration.
- You need to build for the future, not digitize the past. If your only goal is to replicate what you already do, you’ll never unlock the exponential value AI can provide. Challenge yourself to ask: Is there a better way to do this entirely?
- You don’t have to do this alone. This space evolves fast. Don’t be afraid to bring in outside help, not only to build the tech for you but also to help you see what’s possible, align it to your goals, and create a realistic plan that sticks.
Here’s the bottom line: AI success is not about tools. It’s about transformation.
And that transformation starts with you.
Stop asking, “Which AI should we buy?”
Start asking, “What’s the smartest thing we can do better today?”
Consequently, I always remind my clients that AI success isn’t a technology issue but rather a leadership challenge. It requires mindset shifts, process redesign, and cultural readiness.
Here are three principles I use when guiding financial executives through AI transformation:
Start With Value, Not Technology
Too many organizations fall into the trap of chasing shiny objects. A new AI tool hits the market. A vendor makes big promises. A board member asks, “Why aren’t we doing this ChatGPT thing?” And suddenly, your finance team is demoing tech they don’t need for problems they haven’t defined. This is the fastest way to waste budget, stall momentum, and frustrate stakeholders.
AI is not a strategy. It’s really a set of tools, and tools are only useful if you know what job you’re trying to do. That’s why the most successful AI initiatives start with a business problem or opportunity, not the technology.
For example, a mid-sized specialty chemicals company wanted to explore AI use cases in finance. They had heard about GenAI, were intrigued by fraud detection models, and were considering hiring a full-time data scientist without a clear ROI path.
So, I had them step back and ask: “What’s your biggest pain point in the next 12 months?”
The CFO replied, “We’re growing through M&A, and I can’t get consistent reporting across our new acquisitions. It takes weeks to consolidate and normalize data. Our board’s frustrated.”
Boom, there it was. The problem wasn’t lack of AI. It was a lack of standardization, slow data processing, and too much manual effort. The opportunity was to streamline the reporting pipeline and create AI board summaries that adjusted for legacy system differences.
We implemented a solution using RPA + GPT-based summarization that cleaned incoming data, reconciled differences, and produced a consistent executive narrative in under 30 minutes per acquisition. The outcome? Reporting cycle time dropped from 17 days to 5. The CFO introduced AI. More importantly, they got peace of mind and credibility with the board.
That’s the key: Start with the value. Then pick the tool.
It sounds obvious, but you’d be surprised how many leaders do it backwards. They chase automation without defining what success looks like. They implement AI to “keep up” without measuring whether it’s solving anything. Worse, they sometimes deploy tech, and then realize their culture, data quality, or workflows weren’t ready.
What this means for you:
- You’ll solve the right problem the first time. When you focus on business value, like reducing close cycle time, improving working capital forecasting, or boosting employee productivity, you avoid overengineering and underdelivering.
- You’ll align your team around a shared goal. Instead of chasing vague “AI innovation,” your finance, IT, and business units rally around a clear outcome. That accelerates buy-in, funding, and adoption.
- You’ll speak the board’s language. Board members don’t want to hear about vector embeddings and transformers. They want to know how AI will reduce risk, increase EBITDA, or open up new markets. Value-first framing gets you heard.
- You’ll de-risk implementation. When you start with a tightly scoped, high impact use case, you can prove success quickly. That builds credibility and clears the path for broader AI initiatives.
- You’ll future proof your AI roadmap. The right first step leads to the next. One solved problem gives you momentum, data infrastructure, and team confidence to tackle larger transformations down the road.
When an AI vendor comes knocking, ask: “What pain does this solve?” If they can’t answer that clearly (or if your team can’t tie the answer to your strategic priorities) step away.
Remember, AI doesn’t create value by itself. You create the value, and AI just helps you capture it faster, smarter, and at scale. Start with that, and everything else falls into place.
Design for Augmentation, Not Replacement
AI is not here to replace your people. It’s here to make them better.
However, that’s not the message most teams hear when the words “automation,” “efficiency,” or “cost reduction” enter the boardroom. That’s where things go wrong. Leaders frame AI initiatives as a threat rather than an opportunity. This leads to resistance, anxiety, poor adoption, and, ultimately, wasted investment.
The best-performing companies take a different path. They’re designing AI implementations that amplify human strengths instead of trying to eliminate headcount. They’re building copilots, not robots.
Consider, a leading consumer finance company seeks to improve loan review throughput. Their first instinct was to build an AI model that would fully automate underwriting decisions for loans under $50,000. However, this approach ran into regulatory pushback, internal mistrust, and low model explainability.
So, they pivoted to an augmentation model. We used AI to pre-score applications, flag anomalies, and generate decision support summaries. Loan officers still made the final call, but with better, faster insight. The result? A 47% increase in throughput, a measurable improvement in consistency, and higher job satisfaction among underwriters, who now had time to focus on complex edge cases and customer experience.
That’s the power of augmentation.
At PwC, they’ve rolled out an AI assistant for their auditors that reads contracts, extracts relevant clauses, and identifies risks. It hasn’t replaced auditors, but instead, freed them up to spend more time on judgment, analysis, and client strategy.
Likewise, Microsoft’s Copilot is now embedded in Excel, PowerPoint, and Outlook to reduce the low-value grunt work that consumes your day. Even in law, firms are deploying AI to draft summaries, conduct discovery, and find precedents, but lawyers still own the judgment, interpretation, and courtroom strategy.
In every case, the ROI is about elevating human capital. This is freeing people to do what only people can do: think critically, solve creatively, and connect empathetically.
What this means for you:
- You’ll increase productivity without triggering fear. When you position AI as a helper, not a threat, you get faster adoption, stronger results, and higher morale. People lean in instead of bracing for impact.
- You’ll retain institutional knowledge. Augmentation keeps your experienced people in the loop by helping train models, validate insights, and mentor younger talent who will soon rely on AI workflows.
- You’ll improve decision quality. AI can surface insights, but humans still interpret nuance. By combining machine intelligence with human judgment, you reduce blind spots and improve outcomes.
- You’ll create upskilling pathways. When AI takes over low-value tasks, you can retrain staff for higher-value roles in data analytics, strategic finance, or digital transformation.
- You’ll shape the future of work. Leaders have the chance to set the tone: AI isn’t here to erase jobs but rather redefine them. Moreover, the finance leaders who embrace this will build more resilient, adaptable, and innovative teams.
If your AI strategy begins with “how do we replace people?” You will fail to unlock real value.
If you start with “how do we help our people do more of what they’re best at?” You will see faster adoption, better performance, and long-term cultural alignment.
So next time someone asks if AI will take their job, tell them the truth: Not if we do this right.
Pilot, Prove, Scale
A common AI misconception is that you need to “go big or go home.” I can’t tell you how many organizations I’ve seen stall their AI journey because they tried to roll out a massive transformation all at once and got crushed by complexity, misalignment, or simply not knowing where to start.
The smartest financial leaders take a different approach. They start small, focus on quick wins, and build momentum. They pilot a targeted use case, prove measurable value, and then scale with confidence.
Because when you try to transform too much too fast, the business pushes back. Teams get overwhelmed. Systems buckle. Skepticism spreads. However, when you deliver a fast, meaningful win that improves a real process, you turn AI from an abstract buzzword into a trusted solution.
Let’s look at a mid-size private equity firm that wanted to bring AI into their portfolio operations playbook. They were intrigued by predictive analytics, automated reporting, and GenAI, but all at once. (Not realizing that starting everywhere meant finishing nowhere.)
After failing spectacularly, they decided to try again with a better approach. So, we picked one high-friction pain point: fraud detection in a healthcare portfolio company that had suffered recurring issues with expense abuse. We implemented a lightweight anomaly detection model using internal AP data, historical T&E patterns, and third-party benchmarks. Within 21 days, the AI flagged suspicious invoice clustering behavior tied to a single procurement agent. Thais uncovered $540K in vendor fraud over six months.
Moreover, the PE firm immediately scaled the solution across three more portfolio companies. Eventually, they layered it into a broader AI ops platform across compliance, pricing, and customer churn analysis. All this started with one well-chosen pilot that delivered ROI fast.
This approach works across industries and functions:
- A Fortune 100 CPG firm started with AI demand forecasting in one region. When it beat the baseline forecast accuracy by 18%, they scaled it globally.
- A healthcare system used GenAI to summarize physician notes into billing codes for one specialty. When it reduced claim denials by 22%, they rolled it out to all departments.
- A mid-market logistics company tested predictive maintenance AI on a single warehouse’s fleet. When it extended equipment uptime by 11%, they expanded it fleetwide.
Notice the pattern? Pilot. Prove. Scale.
That’s how you derisk innovation and build a culture that embraces change.
What this means for you:
- You’ll reduce risk and build trust. Starting small allows you to test AI in a controlled environment, build internal confidence, and address cultural or data readiness issues before scaling.
- You’ll generate real, measurable ROI fast. By selecting a high-impact use case with clear KPIs, you can demonstrate value quickly, whether it’s hours saved, errors avoided, dollars recovered, or decisions accelerated.
- You’ll unlock internal champions. Successful pilots create believers. When your teams experience the benefit firsthand, they become your biggest advocates and help drive adoption from the inside out.
- You’ll establish executive alignment. A fast, high-ROI win makes it easier to justify investment, secure board buy-in and move AI from “experiment” to “strategic initiative.”
- You’ll create a repeatable playbook. Once you’ve proven a use case, you can replicate and scale it across teams, regions, or portfolio companies without reinventing the wheel.
AI success is not a moonshot. It’s a staircase.
And the first step is everything.
Start with a pilot that matters. Prove that it works. Scale what delivers. That’s how you move from theory to transformation without blowing your budget or your credibility.
So, the next time someone in your organization says, “Let’s do AI,” reply with this:
“Great. What’s the first win we can deliver in the next 60 days?”
Now that you’ve got the mindset, let’s make it real. That’s why I created a tool specifically for you.
The AI Pilot Scorecard is a simple, battle-tested framework to help you cut through the noise and identify the best place to begin. It’s designed to help prioritize high-impact, low-risk AI opportunities, so you can launch with purpose, show quick wins, and build internal momentum fast. Whether you’re just starting out or trying to course-correct an existing initiative, this tool gives you a clear, structured way to evaluate your options and move forward with confidence.
AI Pilot Scorecard
Identify, Prioritize, and Launch Your First (or Next) AI Pilot with Confidence
Step 1: Define the Candidate Use Case
Briefly describe the process, problem, or opportunity you’re evaluating for an AI pilot.
Example: Automating the monthly close variance commentary
Example: Flagging expense fraud through anomaly detection
Example: Predicting customer churn in subscription billing
Step 2: Score Your Use Case (Rate each category 1–5)
| Category | Guiding Question | Score (1–5) |
| Business Impact | Will this pilot move the needle on margin, cost, risk, or strategic visibility? | |
| Pain Point Visibility | Is this a process that’s currently painful, slow, error-prone, or frustrating to the team? | |
| Data Availability & Quality | Do you already have access to clean, relevant data (even in spreadsheets)? | |
| Cultural Readiness | Will your team likely embrace this pilot, or will they see it as threatening? | |
| Ease of Integration | Can the pilot plug into existing systems with minimal IT disruption or cost? | |
| Time to Value | Can you show measurable results in under 90 days? | |
| Scalability | If it works, can this pilot expand across departments, regions, or business units? | |
| Executive Sponsorship | Do you have a champion (CFO, COO, CTO) who will back and protect this effort? | |
| Model Explainability | Will the AI solution be transparent enough for internal trust and external audit? |
🧮 Total Score (out of 45): _______
Step 3: Interpret Your Score
| Score Range | Interpretation | Action |
| 36–45 | High-Value, High-Readiness Pilot | Greenlight immediately. Define success metrics and engage partners. |
| 26–35 | Promising Use Case | Refine data, prepare team, or secure clearer executive sponsorship. |
| 15–25 | Needs Development | Identify blockers (e.g. data, trust, alignment) and resolve before launching. |
| <15 | Not Pilot-Ready | Archive for future consideration. Focus on more mature use cases first. |
Step 4: Set Your Pilot Plan
- Owner: _______________________________
- Goal (Quantifiable): ____________________
- Timeline (Max 90 Days): ________________
- Key Metrics to Track:
- Hours saved
- Errors reduced
- Revenue gained
- Costs avoided
- Risk exposure decreased
- Budget / Tools Needed: ___________________
Pilot Principles
- Keep it focused. Choose one clear outcome. Don’t try to fix five things at once.
- Don’t overbuild. A scrappy proof of concept beats a perfect prototype.
- Validate early. Define what success looks like before you start.
- Tell the story. Capture wins, testimonials, and momentum for the next rollout.
Conclusion
The role of the financial executive is constantly evolving. You’re the guardian of capital. You’re also becoming the steward of digital intelligence. That’s a profound change.
My advice? Embrace it.
Become the AI champion your organization needs. Ask the hard questions. Push your teams. When you hit roadblocks, reach out for help.
AI is not a fad. It’s a force. In the right hands… your hands… it’s a game-changer.
About the Author
Neil Sahota (萨冠军) is an AI expert, United Nations advisor, and author of the best-selling book Own the A.I. Revolution and contributor to Innovative Leadership in the Age of AI. He helps enterprises discover value-driven AI opportunities, develop scalable strategies, and achieve measurable transformation. Learn more at www.neilsahota.com or connect with him on LinkedIn.
