Insights from NFON’s own journey and advisory experience in AI-driven business communication

At the CIO Summit, the invite-only technology leadership track at Bits & Pretzels 2025, one of Europe’s leading events for tech founders, corporate innovators, and digital decision makers, we shared a simple idea: AI success doesn’t come from hype, it comes from execution. Drawing on our own transformation at NFON and our work with customers and partners, we put together a practical ‘cheat sheet’ for moving AI from experiments to measurable impact. Here are the eight lessons that matter most.

At NFON, we’re not just talking about AI, we live it.

We’ve gone through our own transformation and continue to help customers embed AI into the heart of their business communication, from chatbots and voicebots to AI-driven contact center solutions. This cheat sheet reflects that practical experience.

What follows are eight strategic items to help companies move from experimentation to execution, and from hype to measurable results.

1. Elevate AI to a board-level priority.

AI must be treated as a strategic imperative from day one. It cannot sit in a silo or operate as a technology-only initiative. To be successful, it requires top-level ownership and visibility.

Secure executive sponsorship and make AI a recurring item in leadership conversations. Assign a cross-functional team that combines the right expertise across business, IT, legal, data privacy, and AI or data specialists. Ensure there is a clearly defined lead who has the mandate to bridge strategic goals with operational delivery.

AI is not just a tool. It is a driver of change across every function. That change starts with clear, committed leadership.

2. Embed AI in core business objectives.

AI must be directly tied to your most critical business goals. It should not sit on top of your strategy as a standalone initiative. Instead, it must be woven into the outcomes you are already pursuing.

Be clear about the problem you want to solve. Whether it is reducing operational costs, accelerating time-to-resolution, increasing revenue, improving product quality, or expanding into new customer segments, tie your AI initiative to a quantifiable baseline and a clearly defined target.

Establish a clear link between your business KPIs and the AI capabilities you are building. Ensure these are reviewed regularly. AI is not a side project. It is part of the value engine.

3. Enable and transform the organization, don't treat it as a checkmark.

Treat AI as a transformation effort, not as a checklist item. It changes how teams work, how decisions are made, and how technology supports the business.

This will affect every function over time. So make sure your organization is ready to grow with it. Plan step by step. Map out which teams will be involved, in what order, and what level of enablement they need. Define who will act as ambassadors and change agents.

Transformation is not a communication campaign. It is a process. Build internal momentum. Equip teams not just to use AI tools but to understand how those tools affect their workflows and responsibilities.

4. Don't wait for the silver bullet – go for the lighthouse project.

One project will not solve every challenge. You are unlikely to find a single AI solution that fixes everything. That is not the goal. Start by solving one important problem well.

Focus on a use case with high relevance and visibility. Be clear on what you are trying to achieve, and equally clear on what you are not trying to do. Choose a problem with measurable business impact. Ensure you have data access and process ownership. Prioritize feasibility and learning potential over perfection.

Your first project is your lighthouse. It sets direction. It allows you to learn quickly and build confidence across the business. It helps create the foundations for a scalable AI portfolio over time.

5. Explain more than you think you need to.

AI transformation creates uncertainty. It also creates hype. Both can be dangerous.

Without the right guidance, teams may misunderstand what AI can and cannot do. Others may fear job losses or lose trust in new processes. You will need to explain the reasoning, goals, and limitations of every AI capability more than once.

Offer practical training sessions. Share ongoing updates about the objectives and progress. Include both technical and non-technical explanations to minimize ambiguity. Help people understand how to use the new tools, and how not to. This is especially true with generative AI, where input and interpretation matter deeply.

Clarity builds confidence. Transparency builds trust. Under-communication slows progress. Take the time to get this right.

6. Make your first win visible.

The road to AI maturity is complex. It requires experimentation, iteration, and resilience. The first success makes the journey real.

Set up short feedback cycles and agile milestones that are visible and measurable. Once you reach a meaningful result, share it. Let employees experience the impact. Let partners and customers benefit, even if indirectly. This could be through smoother processes, faster response times, or better service quality.

When people can see or feel the effect of AI, belief grows. And belief is essential to scale.

Do not wait for perfection before you show progress. A solid step forward, clearly communicated, is worth more than months of unseen background work.

7. Establish an AI Strategy – and in this context, readjust your overall company strategy.

AI is not just an optimization layer. It is a force that changes how companies operate at a fundamental level.

It will influence your product offerings, your go-to-market strategy, your partnerships, your pricing, and your internal ways of working. Building a coherent AI strategy helps you stay intentional rather than reactive.

This is also a good time to revisit your overall company strategy. Ensure alignment between your AI ambitions and how you position your brand, how you engage customers, and how you define success. AI is not just a tool for the backend. It reshapes what value you deliver and how you deliver it.

Use this opportunity to modernize your messaging and reframe your market positioning. Your AI roadmap and your business roadmap should not be separate conversations.

8. Keep going – don't treat your AI solution like a black box.

Launching an MVP is only the beginning. AI capabilities must evolve based on usage, outcomes, and feedback.

Build a system that helps you understand what works and what doesn’t. Collect explicit feedback through voting, comments, surveys or benchmarking. Combine this with implicit signals like usage statistics, drop-off points or adoption metrics.

Create a rhythm of improvement. Build the muscle for continuous learning and delivery. Stay close to your users. Keep asking what needs to change, what needs to scale, and what needs to be simplified.

This is not just about agility. It is about staying grounded in real-world outcomes. The first version is never the final version. Treat every release as a starting point.

From Potential to Impact

AI success is not accidental. It is the result of clear ownership, focused execution and continuous alignment between technology and business strategy.

These eight cheat sheet items are not theoretical. They are built from experience and grounded in practical application. Use them to guide conversations, shape decisions, and chart a path that leads to meaningful results.

If you're ready to start your lighthouse project or want to explore how these principles apply to your organization, let’s talk. We are here to help you move from AI potential to business impact.