| By: Julian Dawkins, Principal Product Marketing Manager at Infobip
A few years ago, many boardroom discussions about Generative AI, GenAI, centred on possibility. Today, those conversations are becoming far more practical.
Business leaders are asking tougher questions: Where is GenAI delivering measurable value? Which use cases should be scaled? And how do organisations move from experimentation to execution without losing sight of business outcomes?
These are the conversations I am having most often with customers. The excitement around GenAI has not disappeared, but it is increasingly matched by a demand for evidence. Organisations want to know what is working, what is not, and where investment is translating into tangible results.
What’s becoming clear is that the strongest results do not come from standalone AI tools, but from GenAI embedded directly into customer and operational workflows, especially in conversational environments.
When GenAI is embedded into omnichannel customer communication, across chat, messaging, and contact centre experiences – it moves from a promising experiment to a driver of real business outcomes.
From customer interactions to business outcomes
One of the biggest misconceptions about GenAI is that its primary value lies in answering questions. In reality, its value lies in helping customers and employees complete tasks more efficiently.
The most successful deployments are moving beyond information retrieval and into action. Customers can resolve issues, track orders, update services, make bookings, or complete transactions within a single interaction.
This reduces friction while delivering measurable operational gains for businesses. GenAI makes this possible by turning conversations into spaces where intent can be understood, context can be maintained, and tasks can be completed seamlessly.
In customer service, this is already having a clear impact. AI handles a high volume of routine queries, complaints, and service requests simultaneously. This not only reduces resolution times and lowers cost-to-serve but also allows human agents to focus on complex or emotionally sensitive interactions.
The benefits go beyond efficiency. Organisations are also using GenAI to provide always-on support, improve personalisation, and reduce friction in self-service journeys.
Instead of forcing customers through rigid menus, AI can interpret intent and guide them toward outcomes more naturally.
Why the best GenAI strategies start small
There is often a temptation to view GenAI as a company-wide transformation project from day one. In my experience, organisations achieve far better outcomes when they start with a specific challenge that is clearly defined and measurable.
Whether it is reducing contact centre volumes, improving order fulfilment, or streamlining onboarding, focused use cases allow teams to demonstrate value quickly and build confidence before expanding into other areas of the business.
A key lesson from deployments is that GenAI is only as good as the data and structure behind it. Organisations that invest in clear knowledge bases, structured policies, and well-maintained customer data consistently see better outcomes than those expecting AI to “figure it out” on its own.
Trust will determine who succeeds
As GenAI becomes embedded in more customer and business processes, trust is emerging as the defining factor in long-term success. Customers are willing to engage with AI, but only when interactions are accurate, relevant, and transparent. That is why governance cannot be treated as an afterthought. It must be built into every deployment from the beginning.
It starts with clarity of purpose and an understanding of the real customer or business problem being solved. From there, organisations need clear guidelines: defined knowledge boundaries, escalation paths, tone of voice guidelines, and safeguards for sensitive topics.
It’s also important to know when AI should step aside. Not every interaction should be automated. Customers still expect human help when issues become complex, emotional, or high stakes, and the strongest systems are those that recognise this and transition seamlessly to human support when needed.
Measuring what actually matters
As GenAI moves into production, organisations are becoming more disciplined about how they measure success. Early metrics focused mainly on automation rates and cost savings. While these remain important, they don’t capture the full picture.
The strongest GenAI programmes are measured across three dimensions: operational efficiency, customer outcomes, and commercial performance. Looking at only one of these areas rarely provides a complete picture of success.
Operationally, they track resolution times, containment rates, and cost-to-serve reductions. From a customer perspective, they measure satisfaction, journey completion, repeat contacts, and escalation frequency. On the commercial side, they measure conversion, retention, onboarding success, and revenue impact.
In many customer service deployments, for example, organisations have used GenAI to reduce average handling times and increase containment rates, while maintaining or improving customer satisfaction scores.
One area that is often underestimated is human productivity. When AI handles repetitive queries, employees can focus on higher-value work, whether that’s resolving complex issues or strengthening customer relationships. In many cases, this is where the most meaningful long-term value emerges.
The era of AI pilots is ending
For many organisations, 2024 and 2025 were years of exploration. For many, 2026 is increasingly becoming a year of execution.
Businesses are under pressure to demonstrate that AI investments can generate measurable returns, improve customer experiences, and create operational efficiencies. Those that cannot will struggle to justify continued investment.
However, the organisations that see real value are not those chasing the most advanced models. They are the ones applying AI in practical ways, measuring outcomes carefully, and staying close to customer needs.
Ultimately, the business case for GenAI is not about replacing people or reinventing every process. It is about improving how organisations work, making them more responsive, efficient, and better able to serve customers at scale.
Organisations that succeed with GenAI will not be the ones using the most AI. They will be the ones using it with the clearest purpose.
Technology alone rarely creates competitive advantage. The real advantage comes from applying it to solve meaningful business problems, improve customer outcomes, and enable people to focus on work that creates lasting value.
At that point, the business case for GenAI is no longer theoretical; it is visible in everyday customer interactions and measurable in the organisation’s performance.
