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The growth of the global population, which is projected to reach 10 billion by 2050, is placing significant pressure on the agricultural sector to increase crop production and maximize yields. To address looming food shortages, two potential approaches have emerged: expanding land use and adopting large-scale farming, or embracing innovative practices and leveraging technological advancements to enhance productivity on existing farmland
Pushed by many obstacles to achieving desired farming productivity — limited land holdings, labor shortages, climate change, environmental issues, and diminishing soil fertility, to name a few, — the modern agricultural landscape is evolving, branching out in various innovative directions. Farming has certainly come a long way since hand plows or horse-drawn machinery. Each season brings new technologies designed to improve efficiency and capitalize on the harvest. However, both individual farmers and global agribusinesses often miss out on the opportunities that artificial intelligence in agriculture can offer to their farming methods.
At Intellias, we’ve worked with the agricultural sector for over 20 years, successfully implementing real-life technological solutions. Our focus has been on developing innovative systems for quality control, traceability, compliance practices, and more. Now, we will dive deeper into how new technologies can help your farming business move forward.
Benefits of AI in agriculture
Until recently, using the words AI and agriculture in the same sentence may have seemed like a strange combination. After all, agriculture has been the backbone of human civilization for millennia, providing sustenance as well as contributing to economic development, while even the most primitive AI only emerged several decades ago. Nevertheless, innovative ideas are being introduced in every industry, and agriculture is no exception. In recent years, the world has witnessed rapid advancements in agricultural technology, revolutionizing farming practices. These innovations are becoming increasingly essential as global challenges such as climate change, population growth together with resource scarcity threaten the sustainability of our food system. Introducing AI solves many challenges and helps to diminish many disadvantages of traditional farming.
Data-based decisions
The modern world is all about data. Organizations in the agricultural sector use data to obtain meticulous insights into every detail of the farming process, from understanding each acre of a field to monitoring the entire produce supply chain to gaining deep inputs on yields generation process. AI-powered predictive analytics is already paving the way into agribusinesses. Farmers can gather, then process more data in less time with AI. Additionally, AI can analyze market demand, forecast prices as well as determine optimal times for sowing and harvesting.
Artificial intelligence in agriculture can help explore the soil health to collect insights, monitor weather conditions, and recommend the application of fertilizer and pesticides. Farm management software boosts production together with profitability, enabling farmers to make better decisions at every stage of the crop cultivation process.
Cost savings
Improving farm yields is a constant goal for farmers. Combined with AI, precision agriculture can help farmers grow more crops with fewer resources. AI in farming combines the best soil management practices, variable rate technology, and the most effective data management practices to maximize yields while minimizing minimize spending.
Application of AI in agriculture provides farmers with real-time crop insights, helping them to identify which areas need irrigation, fertilization, or pesticide treatment. Innovative farming practices such as vertical agriculture can also increase food production while minimizing resource usage. Resulting in reduced use of herbicides, better harvest quality, higher profits alongside significant cost savings.
Automation impact
Agricultural work is hard, so labor shortages are nothing new. Thankfully, automation provides a solution without the need to hire more people. While mechanization transformed agricultural activities that demanded super-human sweat and draft animal labor into jobs that took just a few hours, a new wave of digital automation is once more revolutionizing the sector.
Automated farm machinery like driverless tractors, smart irrigation, fertilization systems, IoT-powered agricultural drones, smart spraying, vertical farming software, and AI-based greenhouse robots for harvesting are just some examples. Compared with any human farm worker, AI-driven tools are far more efficient and accurate.
Applications of artificial intelligence in agriculture
The AI in agriculture market is expected to grow from USD 1.7 billion in 2023 to USD 4.7 billion by 2028, according to MarketsandMarkets.
Traditional farming involves various manual processes. Implementing AI models can have many advantages in this respect. By complementing already adopted technologies, an intelligent agriculture system can facilitate many tasks. AI can collect and process big data, while determining and initiating the best course of action. Here are some common use cases for AI in agriculture:
Optimizing automated irrigation systems
AI algorithms enable autonomous crop management. When combined with IoT (Internet of Things) sensors that monitor soil moisture levels and weather conditions, algorithms can decide in real-time how much water to provide to crops. An autonomous crop irrigation system is designed to conserve water while promoting sustainable agriculture and farming practices. AI in smart greenhouses optimizes plant growth by automatically adjusting temperature, humidity, and light levels based on real-time data.
Detecting leaks or damage to irrigation systems
AI plays a crucial role in detecting leaks in irrigation systems. By analyzing data, algorithms can identify patterns and anomalies that indicate potential leaks. Machine learning (ML) models can be trained to recognize specific signatures of leaks, such as changes in water flow or pressure. Real-time monitoring and analysis enable early detection, preventing water waste together with potential crop damage.
AI also incorporates weather data alongside crop water requirements to identify areas with excessive water usage. By automating leak detection and providing alerts, AI technology enhances water efficiency helping farmers conserve resources.
Crop and soil monitoring
The wrong combination of nutrients in soil can seriously affect the health and growth of crops. Identifying these nutrients and determining their effects on crop yield with AI allows farmers to easily make the necessary adjustments.
While human observation is limited in its accuracy, computer vision models can monitor soil conditions to gather accurate data necessary for combatting crop diseases. This plant science data is then used to determine crop health, predict yields while flagging any particular issues. Plants start AI systems through sensors that detect their growth conditions, triggering automated adjustments to the environment.
In practice, AI in agriculture and farming has been able to accurately track the stages of wheat growth and the ripeness of tomatoes with a degree of speed and accuracy no human can match.
Detecting disease and pests
As well as detecting soil quality and crop growth, computer vision can detect the presence of pests or diseases. This works by using AI in agriculture projects to scan images to find mold, rot, insects, or other threats to crop health. In conjunction with alert systems, this helps farmers to act quickly in order to exterminate pests or isolate crops to prevent the spread of disease.
AI technology in agriculture has been used to detect apple black rot with an accuracy of over 90%. It can also identify insects like flies, bees, moths, etc., with the same degree of accuracy. However, researchers first needed to collect images of these insects to have the necessary size of the training data set to train the algorithm with.
Monitoring livestock health
It may seem easier to detect health problems in livestock than in crops, in fact, it’s particularly challenging. Thankfully, AI for farming can help with this. For example, a company called CattleEye has developed a solution that uses drones, cameras together with computer vision to monitor cattle health remotely. It detects atypical cattle behavior and identifies activities such as birthing.
CattleEye uses AI and ML solutions to determine the impact of diet alongside environmental conditions on livestock and provide valuable insights. This knowledge can help farmers improve the well-being of cattle to increase milk production.
Intelligent pesticide application
By now, farmers are well aware that the application of pesticides is ripe for optimization. Unfortunately, both manual and automated application processes have notable limitations. Applying pesticides manually offers increased precision in targeting specific areas, though it might be slow and difficult work. Automated pesticide spraying is quicker and less labor-intensive, but often lacks accuracy leading to environment contamination.
AI-powered drones provide the best advantages of each approach while avoiding their drawbacks. Drones use computer vision to determine the amount of pesticide to be sprayed on each area. While still in infancy, this technology is rapidly becoming more precise.
Yield mapping and predictive analytics
Yield mapping uses ML algorithms to analyze large datasets in real time. This helps farmers understand the patterns and characteristics of their crops, allowing for better planning. By combining techniques like 3D mapping, data from sensors and drones, farmers can predict soil yields for specific crops. Data is collected on multiple drone flights, enabling increasingly precise analysis with the use of algorithms.
These methods permit the accurate prediction of future yields for specific crops, helping farmers know where and when to sow seeds as well as how to allocate resources for the best return on investment.
Automatic weeding and harvesting
Similar to how computer vision can detect pests and diseases, it can also be used to detect weeds and invasive plant species. When combined with machine learning, computer vision analyzes the size, shape, and color of leaves to distinguish weeds from crops. Such solutions can be used to program robots that carry out robotic process automation (RPA) tasks, such as automatic weeding. In fact, such a robot has already been used effectively. As these technologies become more accessible, both weeding and harvesting crops could be carried out entirely by smart bots.
Sorting harvested produce
AI is not only useful for identifying potential issues with crops while they’re growing. It also has a role to play after produce has been harvested. Most sorting processes are traditionally carried out manually however AI can sort produce more accurately.
Computer vision can detect pests as well as disease in harvested crops. What’s more, it can grade produce based on its shape, size, and color. This enables farmers to quickly separate produce into categories — for example, to sell to different customers at different prices. In comparison, traditional manual sorting methods can be painstakingly labor-intensive.
Surveillance
Security is an important part of farm management. Farms are common targets for burglars, as it’s hard for farmers to monitor their fields around the clock. Animals are another threat — whether it’s foxes breaking into the chicken coop or a farmer’s own livestock damaging crops or equipment. When combined with video surveillance systems, computer vision and ML can quickly identify security breaches. Some systems are even advanced enough to distinguish employees from unauthorized visitors.
Role of AI in the agriculture information management cycle
Managing agricultural data with AI can be beneficial in many ways:
Risk management
Predictive analytics reduces errors in farming processes.
Plant breeding
AI utilized plant growth data to further advise on crops that are more resilient to extreme weather, disease or harmful pests.
Soil and crop health analysis
AI algorithms can analyze the chemical composition of soil samples to determine which nutrients may be lacking. AI can also identify or even predict crop diseases.
Crop feeding
AI in irrigation is useful for identifying optimal patterns and nutrient application times, while predicting the optimal mix of agronomic products.
Harvesting
AI is useful for enhancing crop yields and can even predict the best time to harvest crops.
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As teachers, we spend a considerable amount of time observing children, tracking progress, and planning developmentally appropriate activities that help scaffold learning for our children. This article explores how we could use AI to help us save time on administrative work and thus focus on more meaningful activities, such as taking care of ourselves and fostering deeper connections with our young learners.
As you read through this article, remember that the goal is not to replace the human touch that is so essential to teaching, but rather to support and enhance it.
Learning Analytics in Early Childhood Education
Observation is a fundamental skill for early childhood educators, as it allows them to closely monitor each child’s development and tailor their teaching approach to best support individual needs.
Teachers invest significant time and effort into learning how to observe without judgment, avoiding labels, and becoming skilled investigators who can connect their observations to established theories of child development. Renowned theorists such as Piaget, Vygotsky, and others have provided invaluable frameworks for understanding how children learn and grow, and these frameworks serve as essential guides for educators in their everyday practice.
Learning analytics is an area that builds upon this tradition of observation by using data to systematically analyze and understand students’ learning processes. By collecting, measuring, and analyzing data related to students’ interactions and performance, learning analytics can help educators identify patterns and trends, providing valuable insights into each child’s learning journey. In early childhood education, the benefits of learning analytics are particularly significant, as they allow teachers to make informed decisions about their instruction, identify potential areas of concern, and support each child’s unique developmental path.
Empowering Educators With AI-Powered Learning Analytics: A Step-by-Step Example
AI-powered learning analytics might be a game-changer for early childhood educators, providing them with actionable insights to support individual students’ learning and development. Let’s explore a step-by-step example of how a teacher might use AI-powered learning analytics to assess a 3-year-old boy named Ravi.
- Identifying strengths and weaknesses: The teacher first inputs various data points, such as Ravi’s performance on tasks, engagement level during activities, and social interactions, into an AI-driven learning analytics tool. The system quickly analyzes the data and identifies Ravi’s strengths (e.g., strong fine motor skills) and areas for improvement (e.g., difficulty with verbal communication).
- Tracking progress over time: As the teacher continues to input data regularly, the AI tool tracks Ravi’s progress over weeks and months, highlighting his growth and areas where he might need additional support.
- Personalizing instruction and learning experiences: Based on the insights provided by the AI tool, the teacher can tailor her instruction and learning activities to address Ravi’s unique needs. For example, she might create more opportunities for Ravi to practice verbal communication skills in small-group settings.
By leveraging the power of AI-powered learning analytics, early childhood educators can gain a deeper understanding of each child’s unique learning journey, allowing them to provide personalized support and optimize instruction for maximum impact.
While AI-powered learning analytics offer numerous benefits, it’s important to consider potential dangers and challenges associated with their use in early childhood education.
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More and better AI agents
In 2025, we’ll begin to see a shift from chatbots and image generators toward “agentic” systems that can act autonomously to complete tasks, rather than simply answer questions, says AI futurist Ray Kurzweil. In October, Anthropic gave its AI model Claude the ability to use computers—clicking, scrolling, and typing—but this may be just the start. Agents will be able to handle complex tasks like scheduling appointments and writing software, experts say. “These systems are going to get more and more sophisticated,” says Ahmad Al-Dahle, Meta’s VP of generative AI. Jaime Sevilla, director of AI forecasting nonprofit Epoch AI, envisions a future where AI agents function as virtual co-workers, but says that in 2025 AI agents will be mostly about their novelty. Melanie Mitchell, a professor at the Santa Fe Institute, warns that agents’ mistakes could have “big consequences,” particularly if they have access to personal or financial information.
A national-security priority
Governments will increasingly view AI through the lens of national security, says Dan Hendrycks, director of the Center for AI Safety: “It’s how many of the big decisions about AI will be made.” The U.S. has curbed China’s access to critical chips, while Meta and Anthropic have forged closer ties with U.S. intelligence agencies by allowing them to use their AI models. “Political developments around the world are pointing us in the direction of continued competition,” says the U.N. Secretary-General’s envoy on technology, Amandeep Singh Gill, emphasizing the need to preserve “pockets of collaboration” between the U.S. and China.
Governance races to catch up
While developers compete to build ever-smarter systems, governments around the world are racing to regulate them. The E.U. leads with its AI Act. Its Code of Practice, set to be finalized by April and enforced from August, is one of the first laws targeting frontier AI developers, and many of the E.U. requirements will likely have global impact on how companies operate, unless they opt to take distinct approaches in different markets, says Markus Anderljung at the Centre for the Governance of AI. In the U.S., where more than 100 bills have been brought to Congress, Anderljung predicts “very little will happen” federally this year, though states may act independently.
Facing the investment test
The year ahead “will be a year of reckoning,” Rumman Chowdhury, CEO of Humane Intelligence, tells TIME in an email. “With billions invested, companies now have to show consumer value.” In health care, that value seems clear—for example, additional AI diagnostic tools are expected to gain FDA approval, and AI may also prove useful in discovering and monitoring the long-term impact of various drugs. But elsewhere, the pressure to demonstrate returns may create problems. “Because of the pressure to make money back from all these investments, there might be some imposition of flawed models on the Global South,” says Jai Vipra, an AI policy researcher, noting these markets face less scrutiny than Western ones. In India, she points to trends in automating already exploitative jobs like call-center work as a source of concern.
AI video goes mainstream
In December, Google and OpenAI released impressive video models. OpenAI’s Sora launch was plagued by access delay, while Google’s Veo 2 was released to select users. Sevilla expects video-generation tools to become more widely accessible as developers find ways to make them cheaper to run. Meta’s Al-Dahle predicts video will also become a key input for AI, envisioning a not-too-distant future in which systems analyze video from smart glasses to offer real-time assistance across various tasks, like fixing a bike.
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2025 and the Next Chapter(s) of AI
AI becomes multimodal and agentic, optimizing experiences and driving breakthroughs across industries. Expect wider access, silo-busting, and solutions to global challenges as AI evolves.
In many ways, the current state of AI feels like living in the space between what we can imagine and what tools we have available to us, at work and in our personal lives, to make those dreams a reality.
But the gap is closing quickly.
Remember in 2023 when we thought only humans could write software, design games, draft marketing content, create a video ad, resolve a customer service issue, or summarize a set of documents? Pick nearly any consumer or enterprise scenario, and it’s a pretty safe bet that AI is already playing a role in making it more efficient, improving quality, or completely redefining how it gets done.
We’re only two years into the commercialization of generative AI, but it’s clear these technologies and capabilities will eventually form the frontend and possibly even the backend of nearly every application.
As a CTO, I share predictions every year, knowing that I might be wrong, understate (or overstate) many things, and will likely get lucky a few times and even nail a couple. I do this — not because I believe I’m correct — but as a necessary exercise to share what I’ve learned from the actual implementations I work on with our customers and teams. My hope is that some of these reflections will spur your own creativity, skepticism, and thoughtfulness around AI.
Last year, I shared a few predictions on how gen AI adoption would increase business utility and ultimately drive innovation. My thinking was that organizations should focus on sustainable costs, broad access, and trust and security to get gen AI right in 2024. These were largely based on the fact that many companies last year were developing the foundations for scaled experimentation, rigorous evaluation, and the constant refinement of AI. Nothing stops a promising project faster than runaway costs, siloed efforts, and a lack of trust in what’s being built.
I’m happy to report that we saw a surge of companies moving their AI prototypes into production — a significant step, demonstrating the growing confidence in AI capabilities and their potential to deliver tangible value. I encourage you to check out this list of over 300 real-world examples of AI in action to inspire your own efforts.
As of January 2025, it’s still us humans writing the prompts, defining the reasoning flows, putting guardrails in place to manage agentic action, and supplying the policies and KPIs that will determine the success or failure of our AI projects. The more we can think and reason up front, the better we’ll design the requisite AI levers.
Against this backdrop, let’s get to what I see as the four key trends emerging that will shape how we collectively move forward this year.
1. Multimodal AI as the new standard
Multimodal AI, which integrates diverse data sources like images, video, code, and audio, alongside text, will become increasingly prevalent. This will enable organizations to provide more sophisticated and personalized customer experiences: Imagine searching for information using a combination of text, images, and voice commands. Or, interacting with AI-powered chatbots that can understand and respond to your visual cues, or accurately triage your health concerns based on shared audio, video, or images and immediately provide a personalized medical analysis. This is the power of multimodal AI and the new expectation for state-of-the-art models.
One of my favorite stories from last year was how the world’s largest advertising holding company, WPP, expanded its WPP Open operating system by leveraging the native multimodality of Gemini. These capabilities empower creatives to take an idea expressed via voice, an image, or a web link and generate social media ad copy that includes draft images and video clips, in minutes.
Another standout example is how Mercedes-Benz is implementing Automotive AI Agent into its MBUX Virtual Assistant to create a highly personalized multimodal experience for drivers and passengers alike. Riders will be able to use voice commands like, “Is there a good restaurant nearby?” "Does it have good reviews?" "Who is the chef?" and “Can you direct me there?” This is just the beginning of how multimodality and agentic capabilities can transform industries.
2. Agentic platforms for scale
AI agents emerged in 2024 as an abstraction for the grounding, reasoning, and augmentation tasks necessary to convert models into value. As organizations gain more experience with combining AI tools with their own intellectual property, data, and expertise, they will want a way to scale the experimentation and deployment of their AI agents. This will generally follow a pattern of discovery, connection, and automation, with agents acting as the critical bridge between the promise of AI in workflows and the realization of that value.
One of the first to put agentic platforms in action is Banco BV, one of Brazil’s largest private banks. Banco BV is using Google Agentspace — which brings together Gemini’s advanced reasoning, Google-quality search, and enterprise data — to enable its employees to discover, connect, and automate with AI agents across its broad set of data and critical systems, in both a secure and compliant manner.
At Deloitte, knowledge workers utilize Agentspace to bridge data sources quickly, fostering rapid experimentation and collaboration. In one case, NotebookLM, available as an out-of-the-box agent in Agentspace, even found a connection between topics across uploaded reports that Deloitte employees hadn’t caught themselves, which would have been difficult to spot under traditional silos of analysis.
3. Optimization of the AI stack
2025 will be the year of optimization. Companies will begin to shift their focus from simply experimenting with or implementing AI to optimizing its performance and maximizing its value. More than 70 percent of organizations are already seeing return on investment (ROI) from gen AI — and that number will only continue to rise as more companies move from production to optimization.
This increased focus on optimization reflects a deeper level of understanding of AI and a growing emphasis on extracting maximum value from these technologies. While optimization will continue at the hardware level, organizations will also move up the technology stack with emergent intelligence that selects the right model for a given user query across a number of attributes including cost, quality, and other important business value metrics. For instance, using a combination of our TPUs and GPUs, LG AI Research was able to reduce inference processing time for its multimodal model by more than 50% and operating costs by 72%.
For organizations to make the most of their AI investments in the future, they will need to invest in identifying the best AI models for their specific use cases, optimizing infrastructure for training and inference, and ensuring they have the ability to measure and optimize models for long-term relevance and effectiveness.
4. Silo busting
The rise of gen AI is helping break down the walls between departments and democratize access to AI tools. This new paradigm is empowering a wider range of users within organizations to participate in AI-driven innovation, fostering collaboration and accelerating the creation of novel customer experiences like never before.
As AI technologies and tools become more widely-adopted, they free up time previously dedicated to routine tasks, allowing individuals to focus on more creative and strategic endeavors. This increased capacity for creativity will most certainly drive innovation and lead to unexpected and unimagined breakthroughs.
To help enable that innovation and breakthrough, Workspace Business and Enterprise customers will now have the best of Google AI embedded directly into the tools they use every day. With Gemini for Google Workspace, teams around the world will be able to work faster and more efficiently right where they’re already spending their time — in Gmail, Docs, Sheets, Meet, Chat, Vids, and so much more.
Beyond 2025
This year will be a big one for AI and for us as humans; perhaps, the biggest to date. Aside from the instances I’ve already mentioned, I expect we’ll see AI used to address some of the world’s most pressing problems in ways we can’t even imagine.
Already, my colleagues at Google Deepmind shared how they are using AlphaFold to predict the structure and interactions of all of life’s molecules, which has the potential to transform our understanding of the biological world and drug discovery. Another incredible source of inspiration is the work our partners at the Asteroid Institute are doing with Google’s AI technologies to take what would be 130 years of research down to just three months, bringing space that much closer to our fingertips. In 2025, I’m also excited to see what AI will do for students around the world, with the opportunity to make education more personalized and accessible, bringing new possibilities to uplift an entire generation.
On a more personal note, this year will also mark my ten-year anniversary at Google. Back in 2015, it would have been impossible to predict the nature and extent of the AI disruption we are experiencing today. Still, the safest prediction with the highest ROI now, as it was back then, is to surround yourself with people who are curious, humble, and action-oriented. If you do that, you’ll always be able to navigate ambiguity and complexity successfully and satisfyingly — regardless of your industry, role, or the technology at hand.
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- Israel’s AI sector emerging as a pillar of the country’s tech ecosystem. Currently, approximately 25% of Israel’s tech startups are dedicated to artificial intelligence, according to The Jerusalem Post, with these companies attracting 47% of the total investments in the tech sector (Startup Nation Finder). This strong presence highlights Israel’s focus on AI-driven innovation and entrepreneurs’ belief in the growth opportunities related to AI. The Israeli AI market is expected to grow at a compound annual growth rate of 28.33% from 2024 through 2030, reaching a value of $4.6 billion by 2030 (Statista). This growth is driven by increasing demand for AI applications across diverse industries such as health care, cybersecurity, and fintech. Government-backed initiatives, including the National AI Program, play a critical role in supporting startups by providing accessible and non-dilutive funding for research and development (R&D) purposes. Despite facing significant challenges since the start of the war in Gaza, Israel has continued to produce cutting-edge technologies that are getting the attention of global markets. Additionally, Israel’s highly skilled workforce and partnerships with academic institutions provide a steady supply of talent to meet the sector’s demands. With innovation, resilience, and collaboration at its core, the Israeli AI landscape is poised to remain a global force in 2025 and beyond.
- Mergers and acquisitions to remain a cornerstone of deals. According to IVC Research Center, 47 Israeli AI companies successfully completed exits in 2024, showcasing the global demand for AI-driven innovation. Investors are continually identifying the differences between companies whose foundations were built on AI, versus those leveraging AI to enhance other core elements of their value proposition—sometimes only marginally. Savvy buyers look beyond the “AI label” and seek out companies with genuine, scalable AI solutions rather than superficial integrations, understanding that value lies in robust and transformative applications. AI is also sector agnostic and may disrupt virtually every vertical. From health care and finance to retail and manufacturing and others, numerous industries are increasingly leveraging AI to enhance or even change their core competency to gain competitive advantages. Deals in this space are coming from strategics such as automobile manufacturers, banks, digital marketing companies and life science firms, among others. As AI continues to permeate multiple sectors, Israeli companies are poised to receive increased attention from strategic M&A buyers looking to unlock new technologies and business opportunities in the market.
- Intersection of PropTech and AI to further revolutionize the global real estate industry. Israeli innovation is expected to be at the forefront of this trend. According to IVC Research Center, over 70 PropTech companies headquartered in Israel are leveraging AI to develop cutting-edge technologies that are reshaping the industry on a global scale. We anticipate these companies will continue advancing AI-driven tools and third-party solutions to streamline acquisition strategies, enhance underwriting processes, and drive operational efficiencies. By harnessing AI to identify leasing opportunities, forecast rental trends, and optimize costs, Israeli PropTech firms are set to solidify their position as global leaders in real estate innovation in the year ahead.
- AI to become increasingly important across global industries. Israeli companies have demonstrated genuine thought/R&D leadership in AI innovation. Some of the AI-centric legal trends that may stand out in 2025 include (1) a greater focus on data rights management as Agentic AI continues to carve new learning standards; (2) regulatory advancements in science, highlighted by two AI-related Nobel Prizes in science, that will likely materialize in the U.S. Food and Drug Administration adopting new rules for AI-driven drug approvals, as well as new AI patenting standards and requirements; (3) greater emphasis on responsible AI usage, particularly around ethics, privacy, and transparency; (4) the adoption of quantum AI across many industries, including in the area of securities trading, which will likely challenge securities regulators to address its implications; and(5) turning to AI-powered LegalTech strategies (both in Israel and in other countries). Israeli entrepreneurs are likely to continue working within each of these industries and help drive the AI transformation wave.
- AI-based technology to continue changing how companies handle recruitment and hiring. While targeted advertising enables employers to find strong talent, and AI-assisted resume review facilitates an efficient focus on suitable candidates, the use of AI to identify “ideal” employees and filter out “irrelevant” applicants may actually discriminate (even if unintentionally) against certain groups protected under U.S. law (for example, women, older employees, and/or employees with certain racial profiles). In addition, AI-assisted interview analysis may inadvertently use racial or ethnic bias to eliminate certain candidates. Israeli companies doing business in the United States should not assume their AI-assisted recruitment and hiring tools used in Israel will be permitted to be utilized in the United States. Also, Israeli companies should be mindful of newly enacted legislation in certain U.S. states requiring companies to notify candidates of AI use in hiring, as well as conduct mandatory self-audits of AI-based employee recruitment and hiring systems. AI regulation on the state level in the United States is likely to increase, and Israeli companies that recruit and hire in the United States will be required to balance their use of available technology with applicable U.S. legal constraints.
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