AI in everyday life: my day in 2030

How could an AI assistant revolutionise your daily life?

I wake up before my alarm clock even goes off. The ambient lighting has already begun to gradually brighten, mimicking the sunrise. ‘Good morning, Frédéric,’ whispers my AI assistant, directly integrated into the home ecosystem via the universal MCP protocol. Its voice is familiar, almost reassuring.

— Did you sleep well?

I nod my head at the room's optical sensors, still half asleep. As I stretch, the artificial intelligence analyses my personal data: heart rate, sleep phases, body temperature. A slight anomaly is detected: the AI discreetly adjusts my breakfast, favouring foods rich in magnesium.

Can AI assistants simplify your life first thing in the morning?

In the morning, everything flows smoothly.

In the shower, I already receive a summary of my day:

• The team meeting has been rescheduled to 10:15 a.m. to avoid a scheduling conflict.
• A reminder that my transport pass is about to expire: AI has automatically renewed it by analysing the data and choosing the most advantageous offer, thanks to its machine learning capabilities.
• The shopping for the week has been prepared: the fridge has been analysed by camera (facial recognition to identify certain perishable foods), and the order is ready to be sent to three different retailers to optimise prices and delivery times, as soon as I give my confirmation. I add a bar of chocolate, after receiving an ironic comment from my AI assistant about my nutritional shortcomings.

As I leave the bathroom, my watch vibrates: ‘Your journey to the office will be smoother today if you leave in 12 minutes.’

Can AI organise your travel and leisure activities in an instant?

At noon, a trip in your pocket

The morning flew by. Between two emails drafted by my AI assistant (which I only had to approve), I received a suggestion: ‘You mentioned a long weekend... Lisbon fits your budget and preferences. Would you like me to suggest an itinerary?’

I smiled. In less than five seconds, the artificial intelligence automatically generated a tailor-made programme: flights, eco-friendly boutique hotel, local restaurants, uncrowded museums. Everything was pre-booked, awaiting my confirmation. I lightened the cultural programme a little to give myself some free time before handing it over.

The AI even takes into account my interactions on social media to identify my recent tastes, adjusting its recommendations accordingly.

I lighten the cultural programme a little to give myself some free time before confirming it.

These technological advances show how AI plays an essential role in everyday life, simplifying leisure activities and personalising experiences.

How can AI manage your finances and give you greater peace of mind? 

Afternoon, finances and serenity

At around 4 p.m., I receive an alert: my savings account has been automatically rebalanced. The assistant has moved part of my savings into a sustainable fund, taking advantage of a market peak. I can ask it for a clear explanation at any time, or even a simulation: ‘What if I wanted to finance a property project in three years' time?’

The AI then analyses my income and expenditure data to help me make informed decisions. In a matter of seconds, it projects several investment scenarios, indicating the risks, potential returns and opportunities.

This proactive decision-making support demonstrates the crucial role of AI in everyday life, where it becomes a truly personalised, reliable financial advisor, available at any time.

Can AI help you in your daily life, from choosing what to buy to managing your health?

In the evening, assistance with choices, personal care, etc.

When I get home, the flat is already at the ideal temperature. My shopping is waiting for me outside the door. The AI has adjusted the music playlist to suit my mood, detected via my voice and gestures throughout the day.

I start searching for a new drone to buy. Rather than poring over comparisons on the internet, the AI assistant automatically generates a video from the opinions of several experts, merging them into a dynamic round table discussion.

This approach perfectly illustrates the ability of AI in everyday life to simplify decision-making.

Before dinner, it suggests a 20-minute workout tailored to my level of fatigue, then invites me to a guided breathing session. In the background, it also manages my exchanges with the building manager, schedules the repair of the dishwasher, and adjusts my schedule for the next day.

And when the day ends...

As I slip under the duvet, I realise how much this assistant is no longer just a tool, but an omnipresent artificial intelligence infrastructure. The MCP protocol has eliminated the barriers between my apps, my services, my connected objects and, perhaps tomorrow, my autonomous vehicles.

Is this AI-assisted day already a reality?

From fiction to reality: why is this day already possible?

This fictional day is not some distant utopia: most of the technological building blocks already exist today.

Advances in artificial intelligence and machine learning have transformed the way we interact with technology and live with AI in our daily lives.

Shopping and logistics: Conversational assistants (ChatGPT, Gemini, Copilot) can already create personalised shopping lists, compare prices and even place orders via plugins connected to services such as Instacart. Today, it is already possible to connect an AI assistant to services such as Carrefour, Amazon Fresh, etc. via plugins or APIs.

Analysing the contents of the fridge using a camera, combining facial recognition and computer vision, is technically feasible, although not yet widely available.

Analysing the contents of the fridge using a camera is technically feasible (computer vision + AI), although not yet widely available.

Travel and leisure: Tools such as Wonderplan and Trip Planner AI generate tailor-made itineraries, while Expedia and Hopper use AI to book and optimise budgets. What is missing is seamless orchestration and complete, frictionless management from start to finish.

What is still missing is seamless orchestration capable of unifying all these services without human intervention.

Personal finance: Robo-advisors (e.g. Nalo, Yomoni, Wealthfront) already offer automated management. Banks and fintechs are testing advanced chatbots capable of analysing accounts in real time, suggesting suitable investments and even detecting fraud. What remains limited is the level of trust and regulation (there is hesitation to delegate arbitration entirely to AI).

Decision support and generated content: Creating a summary video such as a ‘round table’ of YouTubers is already possible by orchestrating several existing AI building blocks. First, a search AI identifies relevant videos on YouTube. Next, a transcription model (such as OpenAI's Whisper) converts the audio into text. A large language model (LLM) analyses these transcripts to extract the arguments.

Is the MCP protocol the key to truly agentic AI?

This open source standard acts as a universal port for AI: it allows an intelligent assistant to be connected to all kinds of external systems (Google Calendar, Notion, databases, design tools, search engines, etc.). Thanks to MCP, an assistant can aggregate, analyse and act in different environments as if they were a single interface.

In short, what we describe as the fluidity of life in 2030 is already in the making:

• The connectors exist (plugins, APIs, MCP),
• Specialised AI agents exist (shopping, travel, finance, health),
• The cloud and domestic infrastructure is ready (connected objects, voice assistants, centralised applications).

Everything is therefore feasible; the above fiction is not impossible.

Today, these building blocks are still scattered and require human intervention to be orchestrated. Tomorrow, thanks to protocols such as MCP, they will be seamlessly integrated into a single, truly agentic assistant capable of managing our daily lives from start to finish.

The current limitation is less about raw technology than:

• The fragmentation of ecosystems (each service keeps its API closed),
• Security and trust issues (e.g. letting AI manage your money or health data),
• The maturity of the user experience (fluidity, absence of bugs).

When will such a day become commonplace for the general public?

Realistic timeframe

2025-2026 :

• MCP or a similar protocol will already be widely adopted (initial tests are underway).
• AI assistants will routinely manage calendars, emails, travel bookings, content writing and basic errands via integrations, but users will still need to provide a lot of manual validation.

2027-2028 :

• Assistants will be able to orchestrate multiple services in sequence (e.g. analyse a fridge + order + adjust menus).
• Banks will offer regulated AI assistants for personal finance management.
• True autonomy (acting without each validation click) will begin to appear in controlled environments (smart homes, mobility, preventive healthcare).

2030 :

• A unified, transparent assistant that manages all the tasks described in the fiction will be plausible.
• Technically, it is realistic. The obstacles will be legal, ethical, and cultural (accepting to delegate so much to an AI).

Integration via MCP and related standards is currently underway.

Realistic deadline: 2028–2030 for this to become seamless and mainstream, provided that issues of security, trust and interoperability are resolved.

Smile feedback: first experience with MCP

An initial MCP (Model Context Protocol) implementation test with a major hotel group allowed us to assess the full potential of this approach.

Although the project is still in the exploratory phase, it already demonstrates the protocol's ability to connect multiple artificial intelligence systems in real time and analyze guest data to provide a seamless and personalized experience.

The idea: to enable travelers to book a room, check prices and availability, or interact with the hotel catalog via an integrated AI agent.

This integration leverages Drupal APIs and modules related to artificial intelligence and MCP, allowing prices, availability, and marketing information to be exposed to AI agents in a structured manner.

Initial findings highlight:

The ease of integration with various ecosystems,

The opportunity to streamline the booking process,

And the promise of more responsive and contextualized customer service thanks to automated interaction analysis.

Ultimately, this will offer travelers the ability to interact with hotel providers through their preferred channels (chatbot, voice assistant, AI integrated with IoT, etc.), access key information in real time, and finalize a booking in seconds.

This feedback illustrates the important role of the MCP protocol in the democratization of AI in everyday life, by making interactions more natural, decisions faster, and services smarter.

Everything you need to know about open source artificial intelligence

How does open source artificial intelligence fit into existing architectures?

Open source facilitates modularity and transparency in artificial intelligence projects. Protocols like MCP make it possible to connect different systems and orchestrate AI agents in heterogeneous environments, while maintaining control over the data.

How can open source machine learning accelerate enterprise AI projects?

Open source machine learning libraries (TensorFlow, PyTorch, etc.) provide technical teams with the flexibility to experiment, train, and deploy custom AI models, while reducing reliance on proprietary platforms.

What role does AI play in the constant evolution of digital services?

Artificial intelligence is a key driver of digital transformation today. By automating complex tasks, improving personalization, and strengthening security, it contributes to the constant evolution of open source digital products and services.

How do AI users perceive its integration into concrete cases such as autonomous cars or connected systems?

Users are looking for reliability and transparency above all else. Use cases such as autonomous cars, predictive maintenance, and smart energy management demonstrate the potential of AI when integrated with robust and auditable open source solutions.