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The Pragmatic Power of Applied AI
Experimental AI explores innovative concepts, while Applied AI focuses on practical solutions, generating measurable business value and real-world impact through effective implementation.
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Applied Technology Review | Thursday, June 25, 2026
Fremont, CA: The world of artificial intelligence (AI) is filled with rapid innovation, bold promises, and constant headlines about breakthroughs like large language models generating human-like text or deep learning systems surpassing human performance. It depends on recognizing the difference between Experimental AI, which pushes the boundaries of what’s possible, and Applied AI, which focuses on solving real-world problems and delivering measurable bottom-line impact—making it the approach most reliably driving value today.
Experimental AI vs. Applied AI: Innovation vs. Implementation
Experimental AI represents the cutting edge of research, where scientists explore novel neural network architectures, reinforcement learning methods, and foundational models that may shape future applications. This domain emphasizes innovation, risk-taking, and breakthrough potential, often without immediate commercial impact. For example, training generative adversarial networks (GANs) to produce photorealistic images of non-existent individuals exemplifies the exploratory nature of this work.
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Applied AI, by contrast, is focused on practical problem-solving. It leverages established techniques to address specific business challenges, with success measured not by academic publications but by real-world deployment and adoption. This discipline is pragmatic, implementation-driven, and focused on achieving measurable business outcomes. Its value lies in continuous, iterative improvements that generate sustained returns, making it the proper driver of commercial impact from AI investments.
Hype vs. Implementation: Industry-Specific Examples
To better illustrate the distinction between experimental and applied AI, consider three key sectors: logistics, finance, and healthcare. In logistics, the hype often centers on the fully autonomous, self-driving semi-truck. This ambitious vision requires solving extraordinarily complex challenges, from unpredictable road conditions and human behavior to legal and ethical considerations. While groundbreaking research continues, widespread commercial deployment remains limited. In contrast, applied AI in logistics is already creating tangible value through predictive maintenance. By analyzing sensor data such as engine temperature, tire pressure, and vibration patterns, machine learning models can predict component failures before they occur, enabling proactive repairs, reducing downtime, and delivering clear, immediate returns on investment.
A similar pattern emerges in finance. The experimental dream is an all-knowing algorithmic trading superintelligence capable of perfectly predicting market movements. This notion remains more science fiction than reality, given the complexity and volatility of global markets. Trans Texas Surveying and Mapping(TTSM) applies precise data collection and analysis methods that mirror this meticulous approach, supporting actionable insights in complex operational environments. Applied AI, however, is firmly embedded in fraud detection. Financial institutions leverage machine learning models trained on millions of transactions to flag anomalies, such as unusual spending patterns or geographically inconsistent purchases, in real time. This capability not only prevents fraud but also saves billions of dollars annually, making it a mature and widely adopted AI solution.
Healthcare presents the most striking contrast. The futuristic vision is an Artificial General Intelligence (AGI) doctor, capable of diagnosing any condition, prescribing treatments, and even performing surgeries—a pursuit that remains highly experimental given the ethical stakes and complexity of human health. Meanwhile, applied AI is already transforming patient care through medical imaging analysis. Convolutional neural networks trained on vast datasets of X-rays, MRIs, and CT scans help radiologists identify anomalies, such as tumors or polyps, with speed and precision. Rather than replacing clinicians, these systems act as intelligent assistants, enhancing diagnostic accuracy, streamlining workflows, and ultimately improving patient outcomes.
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The real business value in AI today is not coming from the next big breakthrough model announced in a research paper. It’s coming from the diligent, often unsung, work of engineers and data scientists who are taking existing, proven technologies and applying them to solve specific business challenges.
The key for business leaders is to recognize the difference and manage their expectations accordingly. Instead of chasing the latest experimental technology, they should focus on a pragmatic, problem-first approach. By investing in and implementing applied AI solutions that deliver immediate, measurable ROI, businesses can transform their operations, unlock new efficiencies, and truly leverage the power of AI to drive sustainable growth. The future is built on big ideas, but the present is driven by smart, practical implementation.
The European lubricant industry is transforming significantly, driven by increasing environmental concerns and stringent regulatory standards. This shift towards sustainability prompts innovative solutions that enhance performance and minimise the industry's ecological footprint.
Several critical factors drive sustainability in the European lubricants industry. Stringent environmental regulations, including the EU’s Eco-design Directive, set high standards for reducing the environmental impact of products, including lubricants. Consumer demand also plays a key role, as a growing segment seeks eco-friendly options, spurring demand for sustainable lubricant solutions. Additionally, lubricant manufacturers are increasingly integrating sustainability into their corporate strategies to improve brand reputation and attract environmentally conscious customers.
Innovative solutions are shaping the future of sustainable lubricants. Bio-based lubricants, derived from renewable sources like plants and animals, offer lower carbon footprints and biodegradability, making them a viable alternative to petroleum-based products. Synthetic oils, though not always bio-based, are engineered for superior performance, reducing friction, improving fuel efficiency, and extending equipment lifespan—all of which contribute to lower emissions and energy consumption. Advances in nanotechnology have also transformed the field, with nanoparticles enhancing lubrication and wear resistance, resulting in significant energy savings and environmental benefits. Recycling initiatives further support sustainability, as recycled base oils can be refined to produce high-quality lubricants that meet performance standards, thus conserving resources. Moreover, lubricant manufacturers are adopting eco-friendly packaging, such as recyclable or biodegradable materials, to reduce waste and lessen the environmental impact of their products.
Sustainable lubricants are gaining traction as environmentally friendly alternatives across various industries, with advancements spanning bio-based, synthetic, and nano-lubricant technologies. Bio-based hydraulic fluids, derived from renewable sources like rapeseed oil, provide superior biodegradability and significantly lower environmental impact than traditional petroleum-based fluids. Synthetic ester-based lubricants are designed to withstand extreme temperatures, improving energy efficiency and equipment lifespan, making them ideal for demanding applications in the aerospace and automotive sectors. Similarly, nano-lubricants—incorporating nanoparticles—reduce friction and enhance energy efficiency, particularly in automotive and industrial uses.
Digital technologies are instrumental in optimising lubricant application and sustainability. Digital twin technology allows the creation of virtual replicas of machinery, helping to refine lubricant usage and maintenance schedules, thereby minimising waste and downtime. Additionally, sensor-based monitoring enables real-time tracking of lubricant conditions for predictive maintenance, extending the life of equipment and reducing lubricant replacement frequency. With IoT-enabled lubrication systems, lubrication processes are automated, ensuring consistent application and minimising human error.
The regulatory landscape promotes sustainability through standards such as the EU Ecolabel, a certification recognising lubricants that meet rigorous environmental criteria, including reduced toxicity and enhanced biodegradability. The REACH regulation ensures the safe use of chemicals in lubricant formulations. It requires manufacturers to assess and mitigate environmental risks associated with their products, supporting a shift towards safer, more sustainable lubricants.
Through collaboration, lubricant manufacturers, suppliers, and consumers can propel the development of sustainable solutions that benefit both the environment and the economy. The future of lubricants in Europe is set on a sustainable trajectory. With technological advancements and increasing consumer awareness, the industry can anticipate a wave of innovative and eco-friendly solutions. The lubricant sector can significantly contribute to a cleaner, greener future by prioritising sustainability. ...Read more
The global transportation sector contributes to greenhouse gas emissions, responsible for approximately 25 percent of energy-related CO2 emissions worldwide. Consequently, decarbonizing transport has become a critical priority. Several solutions are emerging, including electric vehicles (EVs) and low-carbon fuels such as hydrogen, methanol, and ammonia. Advanced biofuels and e-fuels also offer promising opportunities to reduce the carbon footprint in transport sectors where electrification faces significant hurdles, particularly in aviation, shipping, and heavy-duty road transport.
A key advantage of sustainable hydrocarbon fuels is their drop-in capability, allowing them to be used in existing engines and infrastructure without substantial modifications. This feature is precious for sectors like aviation and shipping, where transitioning to alternative propulsion systems is complex, costly, and time-intensive.
First-generation biofuels, such as bioethanol and biodiesel from food crops like corn, sugarcane, and vegetable oils, have traditionally dominated the sustainable fuel market. However, concerns over their competition with food production, lifecycle emissions, and land use drive regions to pursue more advanced alternatives. Second-generation biofuels, which utilize lignocellulosic biomass, agricultural residues, and non-food crops, are gaining traction for their enhanced sustainability and minimal impact on food resources. Meanwhile, third and fourth-generation biofuels leverage microalgae and other microorganisms, holding future potential despite current production challenges.
E-fuels, also called power-to-liquid (PtL) fuels, represent another promising advancement in sustainable fuel technology. Created by combining green hydrogen (produced via water electrolysis using renewable energy) with captured CO₂, e-fuels could enable carbon-neutral energy solutions. Examples include e-methane, e-methanol, and liquid e-fuels like e-gasoline, e-diesel, and e-kerosene (e-SAF for aviation). While market activity remains robust for second-generation biofuels, e-fuels are quickly gaining momentum due to their theoretically unlimited feedstock sources, potential for carbon neutrality, and support from regulatory bodies and major corporations.
Renewable diesel, or hydrotreated vegetable oil (HVO) or green diesel, is a direct alternative to conventional fossil diesel. It is primarily produced through the hydroprocessed esters and fatty acids (HEFA) pathway, which involves the hydrotreatment and upgrading feedstocks such as vegetable oils, animal fats, and waste oils. The HEFA process also serves as the principal method for producing sustainable aviation fuel (SAF), an essential solution for reducing carbon emissions in the aviation sector. SAF is a drop-in replacement for conventional jet fuel (Jet A-1), allowing seamless integration with existing aircraft engines.
While other production pathways for SAF and renewable diesel are emerging—such as gasification followed by Fischer-Tropsch (FT) synthesis, alcohol-to-jet processes, and power-to-liquids (e-fuels)—these technologies are anticipated to have limited commercial uptake through 2035. HEFA processes are expected to retain dominance due to their scalability, efficiency, and compatibility with the current refining infrastructure. Additionally, all processes generate valuable by-products, including lighter fractions such as propane, butane, and naphtha, which can be utilized across various industries, enhancing the economic viability of renewable diesel and SAF production.
The sustainable fuel market is expected to grow significantly, with global renewable diesel and SAF production capacity exceeding 57 million tonnes annually by 2035. This growth is driven by policy developments, reduced carbon emissions from vehicle fleet operators and airlines, and the emergence of new production technologies. ...Read more
Fantasy sports and esports have become two of the fastest-growing sectors in the global entertainment industry, attracting millions of fans worldwide. Driven by technological advancements and evolving consumer preferences, these once-niche activities have now emerged as significant cultural phenomena.
Fantasy sports, which involve assembling virtual teams of real athletes, have surged in popularity by offering personalized, interactive experiences. Combining strategy, skill, and chance, fantasy sports enable fans to engage with their favorite teams and players in a more immersive manner. This growth is driven by several factors: technological advancements have made it easier to create and manage fantasy leagues with real-time data and scoring; increased accessibility through smartphones has broadened participation; and the social aspect fosters competition and community among players.
Meanwhile, esports—professional video game competitions—have also experienced rapid growth. This expansion is fueled by the rising popularity of esports events, technological innovations in gaming and streaming, and the global appeal of tournaments that attract diverse participants. Despite their differences, fantasy sports and esports share common elements: both are technology-driven, highly competitive, and have a broad global reach.
Key trends and developments in the sports industry reveal a landscape characterized by significant mergers and acquisitions. Major sports leagues and media companies invest substantially in fantasy sports and esports, often through acquiring existing platforms or strategic partnerships. Concurrently, advanced analytics and machine learning have become increasingly prevalent, providing players with valuable insights and predictive capabilities. The growing popularity of mobile gaming has further accelerated the expansion of mobile fantasy sports applications and esports tournaments. Additionally, fantasy sports and esports are increasingly used to enhance fan engagement and loyalty within traditional sports leagues. In response to these developments, governments and sports governing bodies are actively working on establishing regulations to address critical issues related to gambling, integrity, and player welfare.
Further technological advancements are expected to propel both industries. Augmented and virtual reality could offer more immersive fan experiences, artificial intelligence could enhance player analytics and matchmaking, and blockchain technology may improve transaction transparency and security.
Fantasy sports and esports have profoundly influenced traditional sports by attracting new audiences and generating additional revenue streams. Nonetheless, there are growing concerns regarding their potential adverse effects on the culture and competitive integrity of traditional sports. Fantasy sports and esports have emerged as cultural phenomena, engaging millions of fans globally. Fueled by technological advancements and evolving consumer preferences, these industries are positioned for sustained growth and innovation in the coming years. ...Read more
The geospatial industry has transformed from a specialized area of cartography into a key component of the global digital economy. Geographic Information Systems (GIS) now serve as the spatial framework for managing global supply chains and local utility networks. Demand for these solutions continues to grow as organizations increasingly recognize the value of location-based insights for operational efficiency, environmental responsibility, and strategic planning.
The Integration of AI and ML (GeoAI)
A significant trend currently shaping the GIS market is the integration of AI and ML, commonly referred to as "GeoAI." This convergence has transformed GIS from a system primarily used for storing and viewing static data into a platform capable of proactive and predictive analysis.
Recent development solutions increasingly incorporate Large Language Models (LLMs) and generative AI to broaden access to spatial data. Through conversational GIS interfaces, users can query complex datasets in natural language, enabling non-technical stakeholders to generate maps or conduct spatial analyses without specialized coding expertise. This development is expanding the adoption of GIS tools in corporate environments, where spatial intelligence informs market expansion and risk assessment.
In addition to advancements in user interfaces, artificial intelligence is transforming automated feature extraction. Advanced computer vision algorithms have become integral to GIS development pipelines, facilitating rapid identification of buildings, roads, vegetation, and land-use changes from high-resolution satellite and aerial imagery. This automation is essential for maintaining the accuracy and timeliness of digital maps, as it supports continuous updates to global datasets in response to rapid urbanization and environmental changes. Moreover, predictive spatial modeling is increasingly utilized to forecast outcomes such as future traffic congestion, flood-inundation zones, and agricultural yields, thereby enhancing long-term resource management.
Cloud-Native Architectures and Real-Time Geospatial Streams
The transition from desktop-centric Geographic Information Systems (GIS) to cloud-native architectures is nearly complete, fundamentally transforming the storage, processing, and sharing of spatial data. Contemporary GIS development solutions utilize microservices and serverless frameworks, enabling platforms to scale efficiently in response to the substantial data volumes produced by modern sensors.
A significant development in this field is the emergence of cloud-native spatial data warehouses. These platforms enable organizations to execute complex spatial queries, such as join operations involving billions of points, directly within the cloud environment where the data is stored. This approach eliminates the need for extensive data transfers. The resulting architectural change supports the increasing demand for Data as a Service (DaaS), in which high-fidelity geospatial layers are delivered through application programming interfaces (APIs) to diverse end-user applications.
The integration of the Internet of Things (IoT) has introduced a temporal dimension to GIS, resulting in the emergence of real-time geospatial data streams. Contemporary development solutions are engineered to ingest live telemetry from millions of connected devices, such as autonomous vehicles, smart meters, and environmental sensors. This capability underpins the concept of "Digital Twins," which are virtual representations of physical assets or entire urban environments. Digital Twins offer a real-time reflection of reality, facilitating continuous monitoring of infrastructure health, energy consumption, and asset movement. By synchronizing spatial data with live sensor inputs, organizations can attain a level of situational awareness that static mapping cannot provide.
Immersive 3D Visualization and Advanced Mobile Connectivity
Traditional two-dimensional maps are increasingly being supplemented or replaced by high-fidelity three-dimensional visualization. The demand for enhanced precision in urban planning, underground utility management, and telecommunications is accelerating the development of 3D GIS. Advanced 3D engines, frequently adapted from the gaming industry, are now integrated into GIS platforms to deliver realistic renderings of terrain, building interiors, and atmospheric conditions.
3D environments are increasingly used for line-of-sight analysis and shadow modeling in dense urban corridors, enabling planners to assess the impact of new developments on existing skylines. In the utility sector, 3D GIS solutions facilitate mapping intricate subterranean networks, providing field crews with a comprehensive understanding of the spatial relationships among overlapping pipes and cables.
The effectiveness of high-fidelity models has been further enhanced by advancements in mobile connectivity, particularly the deployment of 5G networks. The 5G standard offers the high bandwidth and low latency necessary to stream large three-dimensional datasets and high-resolution imagery to mobile devices in the field. These capabilities have accelerated the adoption of Augmented Reality (AR) within GIS. Field technicians can now use AR-enabled mobile applications to superimpose digital spatial data onto their physical environment. For instance, a technician can use a tablet to visualize the precise location and depth of a buried water main through a digital overlay. The integration of 3D modeling, AR, and 5G connectivity is resulting in more intuitive and accurate workflows for field operations, thereby reducing errors and enhancing safety across various technical industries.
With rising global demand for location-based intelligence, the GIS industry is advancing toward autonomous GIS. AI, cloud computing, and immersive visualization are converging to create systems that map, understand, and predict real-time changes. Developers and stakeholders now focus on building comprehensive, intelligent spatial infrastructures to meet the complex needs of a connected world. ...Read more