

I lead solutions development for industrial firms in the US and Latin America at RapidCanvas. I often have conversations with business leaders eager to transform their business processes using AI technologies. These executives recognize the essential nature of AI adoption, but frequently find themselves frustrated by slow progress and elusive results.
The enthusiasm for AI's potential is universal across industries and company sizes. Every company needs a clear, practical AI game plan that delivers tangible business outcomes from their digital transformation without excessive delays.
In all those discussions with business leaders, three questions come up again and again:
In other words, “How can I achieve AI transformation without the wait for results?”
The urgency behind these questions is palpable–AI adoption isn’t optional. Further, your company’s AI adoption journey must happen at breakneck speed. That means weeks and months, not years. Yet the traditional routes to AI implementation just don’t work for most companies.
The traditional management consulting model works better for some things than others. Consultants are great at transforming business needs to processes. But for orchestrating AI initiatives, the model faces certain challenges:
Time to change your mindset. In the future, every CEO will lead a team which includes both people and AI agents. So let’s revisit those three universal questions:
If you’re looking for the right first project, why not start by identifying your top business priority? The most successful AI implementations begin not with technology reviews but rather with clarity about business objectives. Your AI initiatives should be about finding new ways to address your most pressing business goals for this year. This alignment ensures executive sponsorship remains strong and ROI is clearly measurable.
Start with your business priorities and ask how AI can help you grow the business or reduce cost. The more direct the relationship between an AI project and these two vectors, the more support and impact it will have.
For example:
The key is identifying suitable use cases where AI directly addresses problems that are meaningful to your P&L.
The per-seat SaaS applications model often can’t fully address business needs, or it leads to data silos and disconnection across departments. By contrast, the custom applications development model often takes years to build and are extremely expensive. I advocate for what I call "productized managed AI services," which:
This approach significantly compresses time to value by reducing discovery to days and leveraging established architectures with an expert in the loop to deliver exactly the results needed. It’s the new 80/20 rule. 80% of the work streamlined by a platform and 20% customized by an AI/ML expert in the loop. This model also adapts more efficiently to technological advances, as new AI capabilities can be integrated into your existing framework without requiring complete redesigns or extending new consulting engagements.
With the right approach, AI can begin delivering meaningful business impact in weeks, not months or years. The most effective AI initiatives produce results that:
Those three answers are critical to achieving AI transformation without the wait for results.
It’s always exciting when companies achieve results on remarkably short timelines. What makes that possible is an AI product customized by experts that focus on internal team empowerment and speed to value. One of my favorite success stories is for a semiconductor company that wanted to improve output and enhance quality control metrics. Within three months, a solution developed by my company drove:
All of these gains were made with a year-one ROI that exceeded 9X.
Every business, of any size, deserves the transformative power of AI. If you’re interested in how this model could help your business, my company RapidCanvas would love to help. Get in touch now, and we’ll connect you to a data science expert with experience in your industry. We’ll work with you to identify the right roadmap and first projects, ensuring scalability and comprehensive understanding to thrive in the AI-driven era.