Successfully Starting with AI
With these tips, you’ll be better prepared. Lees hier de Nederlandse versie
Over the past seven years, I have been part of multiple corporate (Conversational) AI teams and have managed dozens of chat and voice projects. Here are four key points that always prove to be important in an AI project.
1. Ensure an Organization-Wide Vision for AI
For a successful transition to AI, it’s essential that your teams can work in a structured and focused manner on managing and improving your AI environments. A clear vision is indispensable to make your project a success.
Therefore, start by discussing AI within the management framework and then with your employees. What does the organization expect from AI? Where are the biggest opportunities, and what are the potential pitfalls? Are expectations realistic? How do we find the right talent? By discussing these questions, you can proceed with much more confidence.
Without a vision, AI is just a hype, not a strategy. Take the time for this and seek advice from experienced professionals where possible. This (external) knowledge and insights help to quickly eliminate uncertainties and gain momentum from day one.
2. Define a Roadmap but Stay Flexible
We all know the pressure of short-term goals. The numbers must be right, and patience is limited. A quick win is therefore not only pleasant but also essential to guarantee long-term support.
Starting something new requires three essential elements:
- Knowledge building – Develop expertise within your organization.
- Attention – Ensure that you truly understand what is happening, both in technology and with people.
- Patience – Mistakes are part of the process.
Once these three aspects are in place, your MVP with quick wins can begin to grow.
Start an AI project by:
- Developing customer personas
- Creating customer journeys
- Identifying the right moments when AI can truly make a difference
Only then should technology come into play.
In short, define a roadmap that evolves with your insights. Start with your customer, not the technology. This approach to fast prototyping helps organizations develop their best products.
3. Tools That Everyone Can Work With
Make AI accessible to everyone in your organization. Without ease of use, there is no trust, and without trust, there is no success.
Ensure end-user platforms that are intuitive and invite both employees and customers to observe and engage with the AI models you implement. Transparency and user-friendliness increase trust and strengthen involvement in your AI strategy.
An AI solution is only truly successful if it is widely accepted within your organization. This means:
- Providing training and support for employees
- Clearly communicating AI decisions and outcomes
- Encouraging a culture of continuous improvement
4. Ensure Measurable Results
AI should not just be a promise but also deliver demonstrable value. Therefore, set KPIs from the outset to provide insights into AI’s performance and impact. Consider:
- Efficiency improvement – How much time do teams save?
- Cost reduction – What savings does AI achieve?
- User experience – How is AI perceived by employees and customers?
With dashboards and reports, you ensure that AI makes a tangible contribution to business goals. This makes it easier to build support and justify future investments.
Start Small and Think Big
AI is not a ready-made solution but a growth process with exponential impact. A successful AI implementation begins with a clear plan, a flexible approach, and a strong focus on the end user.
The most successful AI implementations are quickly and effectively integrated into existing processes. By starting small – with concrete, achievable applications – you gather valuable insights and create buy-in within your organization. At the same time, you maintain the flexibility to scale and adjust as needed.
This way, your organization moves beyond the AI hype and delivers real value to employees and customers alike.
Wouter Sligter Principal AI Consultant @ KODIFY
Wouter helps organizations bridge the gap between humans and computers. He has been designing and developing high-end conversational and generative AI solutions for over 7 years.
