( September 15, 2025, 10:44 GMT | Official Statement) — MLex Summary: The EU aims to enhance the prospects of digitalization and artificial intelligence in research, the European Commission said on Monday in a strategy for a stronger European research and technology ecosystem. Among the five actions, it also wants to ensure data access for researchers. The EU seeks to remain a global leader in research, innovation and critical technologies and is encouraging scientists to choose Europe, the commission said.Statement follows. …
AI Research
AI Appreciation Day: How Artificial Intelligence Is Reinventing Home Security
In the age of convenience, AI has quietly but fundamentally rewritten the rules of home security. AI Appreciation Day is not just about marveling at robots doing backflips or ChatGPT spinning haikus. It is also about recognising the quiet behind-the-scenes AI technologies that are watching over our domains.
What was once the domain of clunky motion sensors, grainy CCTV footage and false alarms has rapidly been shaped into a streamlined, proactive ecosystem. One that can learn, adapt and protects in real-time.
From Passive to Proactive
Traditional security systems are reactive. A door opens, an alarm sounds. A camera records, and hopefully capture useful footage.
AI has flipped this script entirely. Modern AI-powered security systems like Google Nest, Arlo, Swann, Ring, and countless others use machine learning to differentiate between a possum on the porch and a person at your door. They can tell the difference between your dog and a potential intruder, reducing false alarms and increasing reliability.
Biometrics, such as facial recognition, people detection and license plate reading, were once expensive and niche tech. They are now embedded into affordable home security gear. This shift does not just make homes safer; it makes them smarter.
Simply put, cameras no longer just see. They understand.
Real-Time, Remote, and Always-On
Whether you are at work, on holiday, or lying in bed, your AI-driven security has no down time, does not operate in shifts. The data filtering is done in real-time and notifications delivered to your device almost immediately and often with rich summary.
And with thanks to cloud integration, you can opt out of DVR systems as well. Your entire system’s worth of security footage can be managed from your phone, complete with searchable, time-stamped footage made possible by AI’s brilliant pattern recognition to tag events.
Privacy vs. Protection
But of course with great capability comes great responsibility. The rise of AI in home security also raises legitimate concerns about privacy, data ownership and ethical use. There is a thin line between being secure and being watched.
This is a conversation that needs to be ongoing. Just because AI can see everything doesn’t mean it should.
Smarter Than Human Instinct
It is hardly earth shattering news. AI is outperforms humans in several key areas. It doesn’t:
- get tired
- doesn’t ignore a suspicious noise
- miss a face just because the conditions is less than ideal.
And when it comes to coordinating multiple devices, motion sensors, smart locks, cameras, lights, AI can choreograph them into a symphony.
What is the Future? Predictive Protection
We are beginning to see systems that don’t just react, they are predictive. Imagine AI that learns your daily patterns and flags deviations: a window opened at an unusual time, a delivery left in an odd spot, someone hanging around your property just a little too long.
Some of these anomaly detection is already being tested and is will be exciting to see how it pans out.
AI-powered home security increasingly plays well with smart assistants, climate control, lighting and even appliances. The future home security may not just know something’s wrong. It might lock the doors, call for help and turn on every light while playing your “Angry Dog Barking” playlist on loop.
A Word from Reolink
Nick Nigro, Vice President of Sales Australasia says:
“AI is fundamentally reshaping the way we approach and experience home security. It has moved us beyond legacy security cameras that are limited to basic recording, motion reaction, and alert spam, towards intelligent systems that deliver smart, context-aware detection capabilities that reduce false alarms and focus on alerting users to meaningful activity.”
“Advances in artificial intelligence are transforming every aspect of security cameras, improving both their core technology and everyday usability. The development of new AI features, including intelligent detection, virtual boundaries and AI video search are just some examples of how AI is beginning to be adopted into security cameras. With intelligent detection that accurately distinguishes between people, vehicles, animals, and objects, AI greatly reduces the likelihood of false alarms and ensures users receive only the most relevant alerts. Another advantage of AI is customisable perimeter protection, which allows virtual boundaries, monitoring zones, and linger alerts to be tailored to the specific security needs of any site. This, paired with advanced features, such as AI video search, makes it simple to quickly locate important moments, eliminating the need to sift through hours of footage.”
“At Reolink, we are harnessing the power of AI to create security cameras that set a new standard for protection and convenience. By continuing to integrate advanced AI technology, our cameras will be able to perform tasks in seconds that once took our customers considerable time, streamlining everything from real-time alerts to intelligent monitoring. We’re committed to expanding our AI capabilities so that we are able to continue supporting busy parents, pet owners, homeowners, and travellers in protecting what matters the most.”
“With AI at the heart of modern security cameras, home protection has become more intelligent, intuitive, and personalised than ever before. Today’s systems do more than just watch—they anticipate, adapt, and empower individuals to take control of their safety. As technology evolves, so too does our ability to safeguard what matters most, making security a seamless part of modern living.”
Final Word
On AI Appreciation Day, let’s give a nod to the unsung algorithms silently safeguarding our homes. While the tech still has room to grow, and guardrails to refine, there’s no denying that artificial intelligence has taken home security from reactive protection to proactive peace of mind.
If AI is the new neighbourhood watch, it’s the one that never sleeps, never blinks, and definitely doesn’t gossip (that we know of).
Continue Reading
To realize AI’s full potential, organizations must be in it for the long game; a pursuit that requires patience, persistence, and strategic alignment. While quick wins are important, they won’t stand alone in delivering meaningful value; agile experimentation is a necessity, execution requires iteration, and early challenges are inevitable.
Protiviti’s inaugural global AI Pulse Survey highlights a compelling correlation between AI maturity and return on investment (ROI) as well as a disconnect between expectations and performance for many organizations in the early stages of AI adoption. The survey, which had more than 1,000 respondents, categorizes organizations from more than a dozen industry sectors into five maturity stages:
-
Stage 1: Initial — Recognizing AI’s potential but lacking strategic initiatives.
-
Stage 2: Experimentation — Running small-scale pilots to assess feasibility.
-
Stage 3: Defined — Integrating AI into business processes.
-
Stage 4: Optimization — Enhancing performance and scalability with data feedback.
-
Stage 5: Transformation — AI drives significant business transformation.
Expectations from AI Investments
As organizations progress through these stages, their satisfaction with AI investments improves. In fact, of the 50% of survey respondents who indicated that they are in the early stages (initial or experimentation) of AI adoption, about 26% reported that AI investment returns fell below expectations.
Of course, not all AI experimenters are experiencing poor returns. Indeed, a majority report ROI meeting expectations, but the results showed a higher concentration of slightly exceeded or significantly exceeded ROI expectations among groups in the middle to advanced stages of AI adoption.
In reviewing what differentiates successful experimenters — those in the experimentation stage of AI adoption who reported exceeding ROI expectations — from those that did not, we find three compelling attributes:
-
Focus on balanced key performance indicators (KPIs) and measuring success using a mix of financial and operational indicators, such as employee productivity, cost savings and revenue growth;
-
Report fewer challenges with skills and integration, as they tend to invest in training, upskilling and cross-functional collaboration;
-
Seek diverse support, including strategic planning assistance and data management tools, not just training.
One more thing: These successful experimenters also emphasized financial and operational outcomes more evenly, while others focused more narrowly on cost savings.
Challenges AI Experimenters Face
Many AI experimenters are struggling not because of unrealistic expectations, but more likely due to unclear objectives or misunderstood value potential. This challenge and difficulties with integrating AI into existing systems are the two biggest hurdles faced by organizations in the early stages of adoption (stages 1 and 2).
Integration issues peak in the middle stages of AI adoption, but they begin in the early stages. Interestingly, the challenge related to understanding the most impactful use cases is most acute in the earliest stage, dips in the middle stages, and resurfaces even at the highest levels of maturity, albeit for different reasons.
The AI experimenters, of course, are unsure how to apply AI strategically and technical compatibility remains a hurdle, unlike the more mature companies. Compounding these issues are unclear or conflicting regulatory guidance and difficulties with data availability and access, a foundational issue for effective AI deployment.
It is the lack of structured approaches, unclear project objectives, and unreliable data that often lead to underwhelming ROI for these companies in the early stages.
Redefining AI Success
In another interesting finding from the survey, we see that as organizations progress to stages 3 to 5, their success metrics evolve from cost savings and process efficiency to revenue growth, customer satisfaction and innovation.
The good news is that organizations starting out on their AI journey can course-correct by focusing on these success metrics. It starts with redefining AI success, which means moving beyond short-term wins to sustainable transformation.
Having a clear understanding of what you’re trying to accomplish with AI is critical from the outset. Without clarity on what AI is meant to achieve, and how value will be measured, they will struggle to unlock its full potential.
Early experimenters should seek to build a solid foundation by:
Asking Why? Why are you adopting AI? What specific problems are you solving?
Investing in data infrastructure is critical. This step should involve auditing existing data systems and implementing robust data governance frameworks. Organizations will be well served in considering cloud-based platforms for scalability.
Developing a robust integration strategy early. Many existing systems were not originally designed to support AI. To overcome this deficiency, organizations should be proactive in assessing and modernizing infrastructure to handle AI workloads in the initial phases. They are likely to find greater success if IT, data and business teams collaborate and there’s shared ownership of AI initiatives to ensure alignment and adoption.
Aligning AI strategies with business objectives and organizational culture: This is not just a technical step. It involves ensuring organizational readiness and managing cultural and operational changes effectively.
Turning AI Trials into ROI Triumphs
The research is clear: there’s tremendous ROI potential for early-stage companies that can test, learn and scale AI use cases swiftly. Yet, while speed is crucial to capturing value, it’s important to recognize that AI experimentation is ongoing, requiring continuous iteration.
To win, think big, act swiftly, and continuously evolve — never stop.
Prepare for tomorrow’s regulatory change, today
MLex identifies risk to business wherever it emerges, with specialist reporters across the globe providing exclusive news and deep-dive analysis on the proposals, probes, enforcement actions and rulings that matter to your organization and clients, now and in the longer term.
Know what others in the room don’t, with features including:
- Daily newsletters for Antitrust, M&A, Trade, Data Privacy & Security, Technology, AI and more
- Custom alerts on specific filters including geographies, industries, topics and companies to suit your practice needs
- Predictive analysis from expert journalists across North America, the UK and Europe, Latin America and Asia-Pacific
- Curated case files bringing together news, analysis and source documents in a single timeline
Experience MLex today with a 14-day free trial.
-
Business2 weeks agoThe Guardian view on Trump and the Fed: independence is no substitute for accountability | Editorial
-
Tools & Platforms1 month ago
Building Trust in Military AI Starts with Opening the Black Box – War on the Rocks
-
Ethics & Policy2 months agoSDAIA Supports Saudi Arabia’s Leadership in Shaping Global AI Ethics, Policy, and Research – وكالة الأنباء السعودية
-
Events & Conferences4 months agoJourney to 1000 models: Scaling Instagram’s recommendation system
-
Jobs & Careers3 months agoMumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Podcasts & Talks2 months agoHappy 4th of July! 🎆 Made with Veo 3 in Gemini
-
Education3 months agoVEX Robotics launches AI-powered classroom robotics system
-
Education2 months agoMacron says UK and France have duty to tackle illegal migration ‘with humanity, solidarity and firmness’ – UK politics live | Politics
-
Funding & Business3 months agoKayak and Expedia race to build AI travel agents that turn social posts into itineraries
-
Podcasts & Talks2 months agoOpenAI 🤝 @teamganassi