Guest Blog: Becoming an Agent of Change in the Age of AI

PPS
3 days ago
4 min read

For most business leaders, the phrase “AI project” paints images of a multi-year, multi-million-dollar marathon. We imagine a long journey that begins with a massive data clean-up, requires an army of specialists, and only delivers value somewhere near a finish line we can barely see. It is a vision that is intimidating, complex, and often, completely wrong.

The reality is that the starting line for AI is much closer than we think, and the path to real benefits is paved not with massive leaps, but with small, rapid, and iterative steps. The secret to becoming an “AI Champion” is not having perfect data or a five-year roadmap. It is about demystifying the process, managing change with agility, and focusing on generating immediate, tangible value.

Inspiring the Vision

The first and most important step is to change the conversation. Too often, we pitch AI to stakeholders as a technology. Instead, we need to inspire them with a vision of what it enables. Stop talking about algorithms and start talking about outcomes.

Imagine giving every single one of your field sales reps a personal strategy expert—an AI agent who sits with them in the car, telling them insights like, “Your next client is at risk for churn, here’s why,” or, “There’s a local festival next month near this store; they’ll need to increase their stock of these three products.”

This is an example of a practical AI application deployed by Revenue.AI for one of the world’s leading tobacco companies. Frame it this way and AI changes its image from an intimidating IT project into an intuitive tool for empowerment. The objective is not to implement AI, but to improve the capabilities of your people.

With a clear vision on the business impact, you can turn skeptics into stakeholders and resistors into champions.

Demystifying the Data Dragon: Start with What You Have

The single biggest myth that paralyzes AI initiatives is the belief that “our data isn’t ready.” Leaders are convinced they must spend years cleaning and consolidating data before they can even begin. This is no longer true.

The key is to demystify data complexity by breaking it down:

1. Start with your current data. You can derive incredible value from the internal data you already possess, whether it is in your CRM, ERP, or even spreadsheets. Modern AI tools are brilliant at finding patterns in imperfect, siloed information. The goal of the first step is to solve a specific business problem and demonstrate a clear win. And a data ecosystem will evolve over time.

2. Iteratively add layers of intelligence. Once you have proven value with an internal or proprietary dataset, you can begin layering in new, external sources. This could be competitor pricing, local event calendars, weather forecasts, or even sentiment from online news. This iterative approach allows your data strategy to evolve with your business needs, rather than trying to boil the ocean from day one.

Do not wait for perfect data. Use the data you already have to get the results you need. Let those initial successes fund your journey toward a more sophisticated data landscape.

The Agile Imperative: Manage Change with Monthly Wins

The traditional “big bang” approach to business applications development is the enemy of innovation in the age of AI. The technology is evolving so rapidly that a two-year project plan is obsolete before the first year is over. This is why we must shift to an iterative deployment model that embraces change.

Think in months, not years.

Instead of a long, drawn-out implementation, focus on deploying an pilot to a small group in a matter of weeks. From there, release new features and improvements every month. This agile cadence does two things:

● It makes change manageable. Gradual, continuous improvement is far less disruptive than a massive, one-time overhaul. Users have time to adapt, provide feedback, and see the tool get better before their eyes.

● And it builds momentum. You can turn every monthly release into a new success story. The consistent wins and communication build trust across the organization. It creates a pull effect where other departments start asking when it will be their turn.

Crossing the Starting Line

The real race for AI leadership is not about who has the cleanest data or the longest roadmap—it is about who starts first and learns fastest. The organizations winning today are not those waiting for perfect conditions; they are the ones testing, iterating, and scaling what works. Every quick pilot, every micro-success compounds into a competitive edge that is hard to replicate.

Your data is already good enough to start. Your people are ready to experiment. What is missing in most companies is not technology—it is momentum. So cut the marathon mindset. Launch the sprint. The sooner you turn AI from a distant ambition into a daily business AI Agent the sooner you will realize that the “starting line” was never as far away as it seemed.

About the Author

István Czilik is a business entrepreneur, Executive Data & Analytics Leader. He has more than 15 years of leading analytics disruption in different fields with global teams. Enabling business transformations in CPG, Retail, Distribution, Commodity trading, Lifesciences and other industries. He’s a CEO at Revenue.AI: an Agentic AI Platform for pricing and revenue management.