Cos moving from just exploring AI to actually putting it into production

We’re seeing more companies move from just exploring AI to actually putting it into production. From your experience, what’s usually the turning point that helps organizations take that leap?

In my experience, the shift from exploring AI to putting it into production happens when companies start seeing real, measurable results. A small pilot, like using AI to automate document classification and reduce manual review time, can lead to a 30% efficiency gain. That kind of outcome gets decision-makers interested. When the return is visible and practical, the next step to scaling feels more natural.

Equally important is having a strong internal advocate. A senior leader, such as a CTO or VP, can break down barriers, secure budgets, and create a sense of direction. That leadership helps teams move from scattered experiments to more strategic adoption.

Another sign of progress is when AI tools get embedded into existing systems, like customer platforms or operations dashboards. Once teams start depending on AI for everyday tasks, it becomes business-critical. External triggers also help. For instance, new regulations or a competitor launching an AI-powered feature can accelerate the timeline.

In short, it’s a mix of proven results, leadership support, daily usage, and market pressure. When those elements line up, organizations stop seeing AI as an experiment and start viewing it as a core part of their strategy.

There’s a lot of excitement around Generative AI, but also a bit of confusion. What should businesses be clear about before they start investing in GenAI solutions?

Before investing in Generative AI, businesses need to be clear on what they want to achieve. It’s easy to get excited by the technology, but results only come when the use case is tied to a real need. Whether it’s faster content creation, better customer service, or improved product development, the goal must be specific.

Strong data foundations are critical. Generative models rely on large volumes of high-quality data. If your data is incomplete, the output will reflect that. Businesses should also understand the cost structure. These models can be resource-heavy, so building a clear view of usage patterns and expected returns is essential.

Governance should not be an afterthought. Bias, hallucinations, and misuse are real concerns. Guardrails, validation processes, and accountability need to be built in from the start. GenAI also works best when integrated into existing tools. Teams should be trained to understand how to use and review AI-generated content.

Finally, it’s important to treat GenAI as a long-term investment. Choose the right partners, define success early, and iterate as you go. When used thoughtfully, Generative AI can shift from a pilot tool to a serious driver of innovation.

There’s growing buzz around AI agents, RAG, and GPT integrations. What kind of real-world use cases are you seeing in this space, especially from a business impact point of view?

We’re seeing strong momentum across different functions. In customer support, AI agents handle routine questions, cutting the load on human agents by over 50%. That improves both speed and quality. Similarly, internal support teams use RAG-enhanced systems to pull accurate answers from internal documents, improving onboarding and resolution times.

Sales and marketing teams are also seeing real gains. GPT helps create personalized outreach content and provides real-time suggestions during calls. This shortens sales cycles and improves conversion rates. On the technical side, developers are using AI assistants to write cleaner code faster and fix issues early.

Legal and compliance teams are leveraging AI to review contracts and documents, cutting turnaround time significantly. Even executive teams are benefiting from automated summaries, reports, and presentations that used to take hours or days.

What’s important is that these tools are not just saving time. They’re helping people make decisions faster and with better context. As a result, businesses are seeing a real shift in productivity and decision-making. AI is no longer just about automation. It’s enabling smarter, more agile operations across the board.

Mobisoft works across sectors like healthcare, logistics, and fintech. Which industries are moving fastest with AI adoption, and what’s setting them apart?

Industries with strong digital systems and clear business goals are leading in AI adoption. Technology, media, telecom, and professional services are moving quickly. They’re using generative AI for research, support, and content creation. These sectors often have teams familiar with AI, and their systems are built to scale new technologies quickly.

Industries like automotive, healthcare, and logistics are following closely. In manufacturing, AI is used for design and simulation. Healthcare providers are adopting AI to streamline clinical notes and claims processing. Logistics and e-commerce companies are applying AI to improve recommendations, pricing, and delivery schedules.

The industries that move fastest are usually those that can turn AI into measurable gains. They build the right infrastructure, make data easy to access, and focus on practical applications with clear returns. These could include fraud detection, contract review, or customer service automation.

Another thing they get right is coordination. Their teams work across departments, and they iterate quickly. These industries don’t treat AI as a side project. They treat it as a key part of their growth strategy, and that’s what sets them apart.

From chatbot development to more complex areas like LLM tuning and computer vision, how do you see the future of custom AI engineering shaping up over the next few years?

Custom AI engineering is moving into a more mature, scalable phase. We’re moving past general-purpose models and focusing more on domain-specific solutions. For example, healthcare or legal teams need models trained with the right language and knowledge from the start.

Rather than building everything from scratch, we’ll rely more on reusable components. These could include plug-and-play features for tasks like summarization, image detection, or sentiment analysis. That makes development faster and more focused.

AI agents will become smarter and more efficient, learning to balance performance and cost as they operate. The tools we use to build AI will also change. More attention will be given to building systems that are explainable, auditable, and aligned with compliance standards from day one.

Perhaps the most interesting change is how humans and AI will work together. In the future, AI tools will support engineers by reviewing code, suggesting fixes, or even co-creating designs. This kind of collaboration will speed up development, reduce errors, and allow teams to focus more on strategy and innovation.

We’re heading toward a future where custom AI is faster to build, easier to manage, and more tailored to real business needs.

Mobisoft has worked with clients in over 30 countries, building advanced digital and AI solutions. What’s been your focus lately when it comes to helping clients scale faster and smarter with AI?

At Mobisoft, our focus is on helping clients scale AI in a structured, cost-effective, and reliable way. The first step is setting up a strong technical foundation. We build cloud-native platforms that support continuous integration and deployment for AI models. These platforms also include real-time dashboards that help track performance, cost, and accuracy.

We speed up execution by bringing in ready-to-use AI components. These include industry-specific templates, pre-configured workflows, and tools that integrate with existing systems. For example, in healthcare or logistics, we offer AI pipelines that clients can test and adjust quickly.

Cost control is built into the process. For generative AI, we track usage at a detailed level, so teams can see what’s working and adjust infrastructure or tools as needed. This avoids unexpected costs and supports smarter scaling.

We also guide clients through each stage of adoption. From early pilots to full production, we use playbooks and A/B testing to ensure that each phase delivers value. The goal is not just to deploy AI, but to create systems that evolve and stay aligned with business goals. That’s how we help clients turn AI from a pilot into a long-term advantage.