How AI-powered apps can change the way companies do business

Thanks to advances in machine learning, deep learning and generative AI, AI-powered apps are becoming a strategic asset in many industries. Increasingly able to understand natural language, analyse images in real time and support strategic decisions, they are turning the applications we know from simple tools that carry out tasks into truly intelligent systems.

AI-powered apps no longer just “do” what they were programmed to do: they learn, adapt and anticipate needs. Voice assistants that grasp context, interfaces that change dynamically with use, predictive engines that suggest tailored content, instant visual recognition: these are not promises for the future, but features that are already available.

How AI is turning apps into intelligent experiences

With AI turning apps into dynamic entities, we are witnessing a paradigm shift: now it is the tool that adapts to the user’s preferences, context and emotions.

New-generation voice assistants, for example, don’t just answer predefined questions: they understand tone, context and conversation history, making the interaction natural and context-aware. In the same way, dynamic interface personalisation lets apps change in real time, adapting layout and content to user behaviour and creating experiences that are always unique.

Another step forward is emotion detection through audio and video, used in areas such as mental health, gaming and training, where content can adapt to the user’s mood in the moment. Predictive recommendation engines, already central to streaming and fitness, are becoming ever more refined, able to suggest activities or routes before users even know they want them.

Just as important is intelligent image processing, which lets an app enhance photos in real time, remove backgrounds or generate advanced effects without the need for complex software. Finally, thanks to NLP, apps support writing and communication: they adjust the tone of a text, translate instantly or offer suggestions that fit the context.

Every interaction thus becomes more natural, personalised and engaging. For companies, it means offering not just a service but a memorable digital relationship, which sets the app apart from competitors and strengthens user loyalty.

The defining features of an AI-powered app

Beyond the traditional functions we have just seen, a truly AI-powered app must show architectural robustness and trustworthiness, both essential for adoption by businesses. First of all, it needs a unified, reliable data model, with a clean, consistent and contextualised data foundation, so as not to produce wrong answers.

Another feature is built-in explainability: the app shows not only “what it does” but also “why it does it”. Decisions must be traceable and understandable, especially in regulated sectors. Some recent meta-analyses have found that user trust grows significantly when apps explain their decisions clearly: accuracy is not enough, transparency is needed. This also includes human feedback mechanisms, meaning that the user or a supervisor can step in on the app’s actions.

Finally, efficient use of resources is a must: AI-powered apps need to balance local (“on-device”) and cloud processing, for reasons of latency, privacy and cost.

The rise of AI agents in business applications

There is one particular kind of AI integration in apps that will achieve remarkable results in the coming years: AI agents. According to a 2025 Gartner study, by 2026, 40% of enterprise applications will feature task-specific AI agents, up from less than 5% in 2025. The analysis predicts that by 2035 these agents will generate over 30% of application software revenue, exceeding $450 billion.

CIOs will have a crucial window of three to six months to define their AI strategy, as the industry is at a turning point. Organisations that don’t adapt their AI-powered apps to the agentic approach in time risk falling significantly behind their competitors.

Managing strategic priorities calls for a targeted approach to the five stages in the evolution of agentic AI, again as proposed by Gartner.

Stage 1: AI assistants for every application
AI assistants are the precursors of agentic AI. They simplify tasks and interactions for users, but depend on human input and do not operate independently. The most common misconception is to call these AI assistants agents, a misunderstanding fuelled by widespread “agentwashing”, the practice of passing off as AI agents tools that are not really agents, which will lead to a large share of agentic projects being abandoned.

Stage 2: Task-specific agent applications

By 2026, AI assistants will evolve into task-specific AI agents, a significant step towards true agentic capabilities.

Stage 3: Collaborative AI agents within an application
Collaboration between AI agents will redefine enterprise applications, as organisations move beyond single-purpose automation. Gartner predicts that by 2027, one third of agentic AI implementations will combine agents with different skills to manage complex tasks within application and data environments.

Today’s AI agents often focus on individual, specific functions, which can limit their overall impact on the business. Collaborative agents will offer more adaptable and scalable solutions, learning from real-time data and adapting to new conditions. At this stage, technology leaders will need to prioritise standardisation and interoperability and adopt protocols that support seamless communication between agents.

Stage 4: AI agent ecosystems across applications

By 2028, AI agent ecosystems will enable networks of specialised agents to collaborate dynamically across multiple applications, allowing users to achieve goals without having to interact with each application individually. This shift will call for new business models, greater transparency, dynamic pricing and stronger governance to ensure ethical agentic AI operations.

One third of user experiences will move from native applications to agentic front ends, making new business models necessary to avoid disintermediation.

Stage 5: The “new normal” for democratised enterprise apps

Finally, Gartner predicts that by 2029 at least 50% of knowledge workers will develop new skills to manage and create AI agents on demand for complex tasks.

The main problem with AI-powered apps? Trust

An analysis by Venture Capital found that concerns about data accuracy and privacy, together with the overall performance of AI agents, are pushing many decision-makers towards a more cautious approach. More than half of respondents named one of the following trust-related concerns as the main barrier to adoption:

– Data privacy (13%)
– Performance and reliability (13%)
– Accuracy, effectiveness and appropriateness (8%)
– Ethical issues (5%)
– Industry regulations and legal standards (4%)
– Too many unknowns (12%).

For anyone approaching this kind of technology, it is therefore essential to rely on partners whose software guarantees an ethical approach, in line with the AI Act, and careful attention to data privacy.

How AI-powered apps are a real opportunity for SMEs

The great advantage of this new era of strategic AI integration in applications is the democratisation of AI. For Italy’s economy, made up of agile companies that often have limited resources, this is a unique moment. Investing in solutions that integrate AI into your applications means gaining greater efficiency, faster decisions and, above all, the ability to stay competitive in an increasingly global market. Companies that act now will gain a strategic advantage, while those that hesitate risk being left behind.

With Neurally, we offer Italian companies a concrete, scalable and secure tool to make this leap, turning apps from passive tools into truly intelligent allies.

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