With hundreds of artificial intelligence initiatives already delivered in Flexa Cloud —generating reduced operational costs, process automation, and increased revenue—it's clear that the difference between successful professionals goes far beyond understanding the latest buzzwords. What separates those who truly deliver results from those who merely promise is the courage to face the hard work: setting clear goals, adjusting processes, and demanding client commitment at every stage.
The Parable of the AbShaper
When I was 16, I saw an ad for a "miracle" abdominal machine and called the much-missed "zeroonze fourteen zeromeia." The images showed men buffed to their bodies effortlessly; I bought the product motivated by the "promise" and the dopamine rush it brought. But when I received it at home, I discovered that I was the one doing the real work—hundreds of daily abdominal exercises. Today, the machine lies forgotten in a corner of my mother's house.
If someone offered me, "Pay for AbShaper, and for three months, we'll do 40 minutes of sit-ups together every day; your six-pack is guaranteed," I'd refuse outright. Experience has taught me that there's no shortcut to disciplined effort—which brings us to a direct parallel with AI projects.
Statistics we cannot ignore
- 70-85% of AI projects fail, according to surveys by Gartner, McKinsey and RAND.
- Less than 40% of companies that have invested in AI in the past three years report measurable financial gains, according to an MIT study.
- Em 2025, 42% of organizations have scrapped most of their AI initiatives, versus 17% in 2024, according to S&P Global.
These figures reveal a scenario in which many companies buy into the "promise of AI" without clarity about business objectives, dedicated budgets, and data governance. The result? Piles of unfinished projects, frustrated expectations, and lower-than-expected returns on investment.
The job nobody wants to do
In almost every success story we've led, we've identified crucial steps our clients have had to go through:
- Data governance and quality: Cleaning, organizing, and labeling legacy data can take months, but it's the foundation for robust models.
- Process redesign: Adapting internal workflows to incorporate automation requires reviewing roles and training teams.
- change management: overcome internal resistance through workshops, controlled pilots and transparent communication.
- Iteration and validation: constant testing, hyperparameter tuning, and continuous validation of results to ensure the model learns effectively.
- Performance measurement: define realistic KPIs (such as reducing service time by X% or increasing sales by Y%) and monitor dashboards in real time.
Without this discipline, any AI feels like an “expensive toy” — like the useless AbShaper.
The courage to be realistic
While many companies expect instant ROIs of 50% or more, the reality often points to modest average returns, in the range of 5–10% in the first year. Successful professionals know how to communicate this clearly to clients: "AI doesn't work miracles on its own; it enhances your operations, but it doesn't replace human labor."
Having courage, in this context, is:
- Reject projects that have no executive sponsor or ongoing budget.
- Reframing expectations: Explain the six- to twelve-month timeline from maturation to significant gain.
- Negotiate contracts based on deliverables and milestones, rather than promising “magic templates” that solve everything.
The road to success
To escape the AbShaper effect and turn hype into value, follow this practical guide:
- Align business objectives before choosing the technology.
- Map processes to identify real bottlenecks.
- Invest in data (engineers, quality tools, automated pipelines).
- Empower teams internal and ensure leadership sponsorship.
- Implement in phases, testing results on a small scale before scaling up.
With this method, you can reduce the risk of failure, accelerate return on investment, and build confidence for future projects.
Conclusion: Beyond the hype, human intelligence
Just as the dusty AbShaper reminds me daily that there's no such thing as a result without effort, AI projects only generate value when everyone involved is dedicated. The real difference lies in the courage to do the "unglamorous" work—data cleansing, process changes, and ongoing monitoring—and the humility to recognize that, no matter how sophisticated the technology, it's just a tool. It's the combination of artificial intelligence and human sweat that creates authentic and sustainable value.
