What are digital twins?

Digital twins, originally developed to aid in the design of complex machinery have evolved significantly over the last two decades. They track and analyse live systems in real-time by processing device telemetry, detecting shifting conditions, and enhancing situational awareness for operational managers. Powered by in-memory computing, they enable fast, actionable alerts. Beyond real-time monitoring, digital twins also can simulate intricate systems like those for use in airlines and logistics, supporting strategic planning and operational decisions through predictive analytics.

Integrating digital twins with generative AI creates new opportunities for both technologies: The synergy can boost the prediction accuracy of generative AI, and can enhance the value of digital twins for system monitoring and development.

Proactively identifying anomalies with AI-powered digital twins

Continuous, real-time monitoring is a strategic necessity for organisations that manage complex live systems, like transportation networks, cybersecurity systems, and smart cities. Emerging problems must never be overlooked because delayed responses can cause small problems to become large ones.

Enhancing digital twins with generative AI reshapes how real-time monitoring interprets massive volumes of live data, enabling the reliable and immediate detection of anomalies that impact operations. Generative AI can continuously examine analytics results produced by digital twins to uncover emerging trends and mitigate disruptions before they escalate. While AI enhances situational awareness for managers, it can also pinpoint new opportunities for optimising operations and boosting efficiency.

At the same time, real-time data supplied by digital twins constrains the output of generative AI to avoid erratic results, like hallucinations. In a process called retrieval augmented generation, AI always uses the most up-to-date information about a live system to analyse behaviour and create recommendations.

Transforming data interaction with AI-driven visualisations

Unlocking insights from digital twin analytics should be intuitive, not technical. Generative AI is redefining how teams interact with massive datasets by enabling natural language-driven queries and visualisations. Instead of manually constructing intricate queries, users can simply describe their needs, and generative AI immediately visualises relevant charts and query results that provide new insights. This capability simplifies interactions and gives decision-makers the data they need. As organisations handle increasingly complex live systems, AI-powered intelligence allows them to efficiently sift through vast data pools, extract meaningful trends, and optimise operations with greater precision. It eliminates technical barriers, enabling faster, data-driven decisions that have a strategic impact.

Incorporating machine learning with automatic retraining

Digital twins can track numerous individual data streams and look for issues with the corresponding physical data sources. Working together, thousands or even millions of digital twins can monitor very large, complex systems. As messages flow in, each digital twin combines them with known information about a particular data source and analyses the data in a few milliseconds. It can incorporate a machine learning algorithm to assist in the analysis and find subtle issues that would be difficult to describe in hand-coded algorithms. After training with data from live operations, ML algorithms can identify anomalies and generate alerts for operational managers immediately.

Once deployed to analyse live telemetry, an ML algorithm will likely encounter new situations not covered by its initial training set. It may either fail to detect anomalies or generate false positives. Automatic retraining lets the algorithm learn as it gains experience so it can improve its performance and adapt to changing conditions. Digital twins can work together to detect invalid ML responses and build new training sets that feed automatic retraining. By incorporating automatic retraining, businesses gain a competitive edge with real-time monitoring that reliably delivers actionable insights as it learns over time.

Looking forward

Integrating digital twin technology with generative AI and ML can transform how industries monitor complex, live systems by empowering better real-time insights and enabling managers to make faster, more informed decisions. ScaleOut Software’s newly-released Digital Twins Version 4 adds generative AI using OpenAI’s large language model and automatic ML retraining to move real-time monitoring towards the goal of fully-autonomous operations. 

(Image source: Unsplash)

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The tool allows businesses to create customised AI agents through a conversational interface, making it easier to monitor and act on video data from physical settings without the need for technical expertise.

Designed for industries like manufacturing, logistics, retail, construction, and healthcare, Iris builds on Spot AI’s earlier launch of out-of-the-box Video AI Agents for safety, security, and operations. While those prebuilt agents focus on common use cases, Iris gives organisations the flexibility to train agents for more specific, business-critical scenarios.

According to Spot AI, users can build video agents in a matter of minutes. The system allows training through reinforcement—using examples of what the AI should and shouldn’t detect—and can be configured to trigger real-world responses like shutting down equipment, locking doors, or generating alerts.

CEO and Co-Founder Rish Gupta said the tool dramatically shortens the time required to create specialised video detection systems.

“What used to take months of development now happens in minutes,” Gupta explained. Before Iris, creating specialised video detection required dedicated AI/ML teams with advanced degrees, thousands of annotated images, and 8 weeks of complex development,” he explained. “Iris puts that same power in the hands of any business leader through simple conversation with 8 minutes and 20 training images.”

Examples from real-world settings

Spot AI highlighted a variety of industry-specific use cases that Iris could support:

• Manufacturing: Detecting product backups or fluid leaks, with automatic responses based on severity.
• Warehousing: Spotting unsafe stacking of boxes or pallets to prevent accidents.
• Retail: Monitoring shelf stock levels and generating alerts for restocking.
• Healthcare: Distinguishing between staff and patients wearing similar uniforms to optimise traffic flow and safety.
• Security: Identifying tools like bolt cutters in parking areas to address evolving security threats.
• Safety compliance: Verifying whether workers are wearing required safety gear on-site.

Video AI agents continuously monitor critical areas and help teams respond quickly to safety hazards, operational inefficiencies, and security issues. With Iris, those agents can be developed and modified through natural language interaction, reducing the need for engineering support and making video insights more accessible across departments.

Looking ahead

Iris is part of Spot AI’s broader effort to make video data more actionable in physical environments. The company plans to discuss the tool and its capabilities at Google Cloud Next, where Rish Gupta is scheduled to speak during a media roundtable on April 9.

(Image by Spot AI)

See also: ChatGPT hits record usage after viral Ghibli feature—Here are four risks to know first

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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However despite the benefits of the technology, many businesses are slow to adopt the technology, with recent statistics showing only one in ten of SME’s regularly use AI technology, indicating companies and employees are still not operating at their full potential, thus missing out on opportunities for growth and optimisation. 

Transforming the supply chain through AI

The potential that AI has in the supply chain is undeniable, with some estimating that AI helps businesses reduce logistics costs by 15%, reduce inventory levels by 35% and raise service levels by 65%. In contrast, failure to implement AI tools could set companies back, leave employees feeling unmotivated and unproductive and result in a weak supply chain and poor staff retention.

Now, more than ever, it’s time for businesses to not just pay lip service to AI – they must start using it within their supply chains to truly enhance operations. Due to the evolving market dynamics, AI is not just a competitive advantage; it’s essential for business agility and profitability. Here are two ways in which organisations can use AI to improve their supply chains.

Automating the supply chain & harnessing the power of AI for resilience

AI allows businesses to tackle supply chain challenges head-on by automating time-consuming manual processes, such as data-logging whilst reducing errors. By taking over repetitive and potentially hazardous tasks, AI frees up employees to focus on strategic initiatives that drive business value. For example, a recent report highlighted that nearly three quarters of warehouse staff surveyed are excited about the possibilities of generative AI and robotics improving their job roles.

Needless to say, a supply chain still can’t operate at its peak without resilience – which is the capacity of a supply chain to withstand and recover from disruptions – ensuring uninterrupted operations and minimal impact to businesses and customers.

As global markets continue to evolve & expand, businesses are challenged to adapt swiftly to unforeseen disruptions. AI enables businesses to provide real time data analysis, providing unprecedented insights into the web of supply chain dynamics and acting as the eyes and ears of a supply chain. This empowers each component with the ability to make informed decisions quickly to meet supply chain demands. Allowing insights into every aspect of their warehouse operations, real time data enables visibility which permits precise monitoring, enhanced customer service and reduced downtime – identifying potential issues before they become a major problem.

At the heart of the supply chain is communication between all stakeholders, with technology such as AI providing real time data, seamless collaboration is enabled by providing a shared platform where suppliers, manufacturers, and distributors can exchange information instantaneously. Enhanced communication leads to quicker issue resolution, enabling the supply chain to adapt rapidly to changing circumstances. Robotics, AI and real-time data introduce an all-encompassing visibility of the good’s journey, which leads to resilience.

Human expertise with robot precision

Building on the theme of resilience, in the next couple of years the industry will witness AI-integrated robots becoming collaborative partners to their human co-workers. Particularly in environments requiring vast coverage and extensive data capture, robots that are equipped with groundbreaking sensor technologies will navigate, adapt and work with greater levels of autonomy along with other machinery and people in busy environments. This will result in speed of data acquisition and most importantly, allowing companies to make decisions based on actionable insights a lot faster than ever before. 

These advancements will transform robots into true cobots and will take human-robot teamwork to an unprecedented level. We will also see that robots will become better with understanding nuanced human gestures and intentions. This evolution in collaboration with technology will redefine what humans and machines can accomplish together.

What’s next for the industry?

In theory implementing AI and advanced technology in the supply chain has the potential to bring significant benefits. However, we will only begin to see substantial results once these innovations are widely adopted in practice. By automating the supply chain and using data to fuel predictions, these technologies are the foundations for a new industrial revolution that will shape the future of the industries for years to come. Those that delay starting their journeys will risk being left behind.

Photo by Miltiadis Fragkidis on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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While the benefits web3 technology can bring to artificial intelligence are well documented – transparency, P2P economies, tokenisation, censorship resistance, and so on – this is a reciprocal arrangement. In return, AI is fortifying blockchain projects in different ways, enhancing the ability to process vast datasets, and automating on-chain processes. The relationship may have taken a while to get started, but blockchain and AI are now entwined.

Trust meets efficiency

While AI brings intelligent automation and data-driven decision-making, blockchain offers security, decentralisation, and transparency. Together, they can address each other’s limitations, offering new opportunities in digital and real-world industries. Blockchain provides a tamper-proof foundation and AI brings adaptability, plus the ability to optimise complex systems.

Together, the two promise to enhance scalability, security, and privacy – key pillars for modern finance and supply chain applications.

AI’s ability to analyse large amounts of data is a natural fit for blockchain networks, allowing data archives to be processed in real time. Machine learning algorithms can predict network congestion – as seen with tools like Chainlink’s off-chain computation, which offers dynamic fee adjustments or transaction prioritisation.

Security also gains: AI can monitor blockchain activity in real-time to identify anomalies more quickly than manual scans, so teams can move to mitigate attacks. Privacy is improved, with AI managing zero-knowledge proofs and other cryptographic techniques to shield user data; methods explored by projects like Zcash. These types of enhancements make blockchain more robust and attractive to the enterprise.

In DeFi, Giza‘s agent-driven markets embody the convergence of web3 and artificial intelligence. Its protocol runs autonomous agents like ARMA, which manage yield strategies across protocols and offer real-time adaptation. Secured by smart accounts and decentralised execution, agents can deliver positive yields, and currently manage hundreds of thousands of dollars in on-chain assets. Giza shows how AI can optimise decentralised finance and is a project that uses the two technologies to good effect.

Blockchain as AI’s backbone

Blockchain offers AI a decentralised infrastructure to foster trust and collaboration. AI models, often opaque and centralised, face scrutiny over data integrity and bias – issues blockchain counters with transparent, immutable records. Platforms like Ocean Protocol use blockchain to log AI training data, providing traceability without compromising ownership. That can be a boon for sectors like healthcare, where the need for verifiable analytics is important.

Decentralisation also enables secure multi-party computation, where AI agents collaborate across organisations – think federated learning for drug discovery – without a central authority, as demonstrated in 2024 by IBM’s blockchain AI pilots. The trustless framework reduces reliance on big tech, helping to democratise AI.

While AI can enhance blockchain performance, blockchain itself can provide a foundation for ethical and secure AI deployment. The transparency and immutability with which blockchain is associated can mitigate AI-related risks by ensuring AI model integrity, for example. AI algorithms and training datasets can be recorded on-chain so they’re auditable. Web3 technology helps in governance models for AI, as stakeholders can oversee and regulate project development, reducing the risks of biased or unethical AI.

Digital technologies with real-world impact

The synergy between blockchain and AI exists now. In supply chains, AI helps to optimise logistics while blockchain can track item provenance. In energy, blockchain-based smart grids paired with AI can predict demand; Siemens reported a 15% efficiency gain in a 2024 trial of such a system in Germany. These cases highlight how AI scales blockchain’s utility, while the latter’s security can realise AI’s potential. Together, they create smart, reliable systems.

The relationship between AI and blockchain is less a merger than a mutual enhancement. Blockchain’s trust and decentralisation ground AI’s adaptability, while AI’s optimisation unlocks blockchain’s potential beyond that of a static ledger. From supply chain transparency to DeFi’s capital efficiency, their combined impact is tangible, yet their relationship is just beginning.

(Image source: Unsplash)

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“Elements of our three-pillar strategy have been around for quite some time, but what’s revolutionising the frontline today is intelligent automation,” Tom Bianculli, Chief Technology Officer at Zebra Technologies, told reporters at a briefing during Zebra’s 2025 Kickoff in Perth, Australia last week. “We’re not just digitising workflows – we’re connecting wearable technology with robotic workflows, enabling frontline workers to seamlessly interact with automation in ways that were impossible just five years ago.”

Practical applications driving change

The real-world impact of enterprise AI transformation is already evident in Zebra’s recent collaboration with a major North American retailer. The solution combines traditional AI with generative AI capabilities, enabling fast shelf analysis and automated task generation.

“You snap a picture of a shelf, [and] within one second, the traditional AI identifies all the products on the shelf, identifies where there’s missing product, maybe misplaced product… and then it makes that information available to a Gen AI agent that then decides what should you do,” Bianculli explains.

This level of automation has demonstrated significant operational improvements, reducing staffing requirements at the retailer by 25%. When it detects missing stock, the system automatically generates tasks for the right personnel, streamlining what was previously a multi-step manual process.

APAC leading AI adoption

The Asia Pacific region is emerging as a frontrunner in enterprise AI transformation. IBM research presented at the briefing indicates that 54% of APAC enterprises now expect AI to deliver longer-term innovation and revenue generation benefits. The region’s AI investment priorities for 2025 are clearly defined:

– 21% focused on enhancing customer experiences

– 18% directed toward business process automation

– 16% invested in sales automation and customer lifecycle management

Ryan Goh, Senior Vice President and General Manager of Asia Pacific at Zebra Technologies, points to practical implementations that are already driving results: “We have customers in e-commerce using ring scanners to scan packages, significantly improving their productivity compared to traditional scanning methods.”

Innovation at the edge

Zebra’s approach to AI deployment encompasses:

– AI devices with native neural architecture for on-device processing

– Multimodal experiences that mirror human cognitive capabilities

– Gen AI agents optimising workload distribution between edge and cloud

The company is advancing its activities in edge computing, with Bianculli revealing plans for on-device language models. This innovation mainly targets environments where internet connectivity is restricted or prohibited, ensuring AI capabilities remain accessible regardless of network conditions.

Regional market dynamics

The enterprise AI transformation journey varies significantly across APAC markets. India’s landscape is particularly dynamic, with the country’s GDP projected to grow 6.6% and manufacturing expected to surge by 7% YOY. Its commitment to AI is evident, with 96% of organisations surveyed by WEF actively running AI programmes.

Japan presents a different scenario, with 1.2% projected GDP growth and some unique challenges to automation adoption. “We used to think that tablets are for retail, but the Bay Area proved us wrong,” Goh notes, highlighting unexpected applications in manufacturing and customer self-service solutions.

Future trajectory

Gartner’s projections indicate that by 2027, 25% of CIOs will implement augmented connected workforce initiatives that will halve the time required for competency development. Zebra is already moving in this direction with its Z word companion, which uses generative AI and large language models and is scheduled for pilot deployment with select customers in Q2 of this year.

With a global presence spanning 120+ offices in 55 countries and 10,000+ channel partners across 185 countries, Zebra is positioned play strongly in the enterprise AI transformation across APAC. As the region moves from AI experimentation to full-scale deployment, the focus remains on delivering practical innovations that drive measurable business outcomes and operational efficiency.

(Photo by )

See also: Walmart and Amazon drive retail transformation with AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here

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However, asset management is not immune to the disruptive pressure of artificial intelligence (AI) currently revolutionising numerous industries. The manner in which corporations manage their tangible and intangible assets is undergoing a profound transformation due to the evolving technology of AI. This blog will discover how AI-driven fixed asset software softwares transform asset management and what the future holds for businesses embedding those innovations.

Introduction to fixed asset management and AI

Fixed asset management is a critical feature for organisations to manage, control, and optimise the value of their physical assets. Assets can include everything from equipment and vehicles to home computer systems. Traditionally, manual asset management systems entail manual report maintenance and periodic audits, which can be time-consuming and susceptible to human error.

AI-driven fixed assets software offers a modern solution by automating diverse asset control factors. This guarantees accuracy, reduces administrative overhead, and increases an asset’s useful life, ultimately contributing to significant cost savings. AI, blended with the Internet of Things (IoT), machine learning (ML), and predictive analytics, is the primary method to develop smart, efficient, and scalable asset management solutions.

The predictive capacities of AI revolutionise proactive asset management. AI can predict when a piece of hardware is likely to fail or spot chances for optimisation by evaluating patterns and trends in data. The proactive strategy not only helps with strategic planning but also ensures the reliability of operations by preventing system outages that can cause serious disruptions to business operations and financial losses. Businesses may use AI to ensure their assets operate at peak efficiency, quickly adopt new technologies, and match operations to corporate goals.

AI’s advantages for fixed asset software

AI-driven fixed asset software has numerous advantages for businesses, particularly in sectors where asset management is vital to daily operations, like production, healthcare, and logistics.

• Greater effectiveness: Automation significantly speeds up asset tracking, control, and upkeep. As AI can assess huge amounts of information in real time, managers can respond immediately to determine the state of their assets.

• Cost savings: Ongoing asset utilisation and predictive analysis can result in lower operating costs. AI is capable of identifying underutilised or poorly functioning items, which may assist corporations in saving money by reallocating or disposal schedules.

• Enhanced compliance and reporting: Staying compliant can be challenging with increasingly stringent regulatory governance. AI ensures that compliance reports are generated accurately and on time. Moreover, the software can routinely modify asset data to mirror regulatory changes, ensuring that companies consistently comply with laws.

• Improved decision-making: With AI’s analytics capabilities, managers can make better choices about which assets to invest in, when to repair, and when to retire an asset. Selections are based on real-time information and predictive models instead of guesswork or manual calculations.

Case study: Predictive portfolio management precision issue:

Predicting market trends and real-time portfolio optimisation was complicated for a top asset management company. Conventional approaches could not keep up with market demands, resulting in lost opportunities and less-than-ideal results.

Solution:

The company was able to quickly evaluate large datasets by implementing an AI-powered predictive analytics system. The AI algorithms examined market patterns, assessed risk factors, and dynamically altered the portfolio. The end result was a notable improvement in portfolio performance and increased forecasting accuracy.

Findings:

• A 20% boost in portfolio returns was attained.
• Real-time market trend information improved decision-making.

The future of AI in asset management

The future of asset management will revolutionise customer satisfaction, operational effectiveness, and decision-making. Below are the important elements that will transform asset management operations:

1) Elevated decision making

By revealing hidden patterns from huge datasets, AI will permit asset managers to make better decisions. AI can evaluate the whole portfolio, compiling financial statistics and market news, which together will improve risk posture and portfolio formulation. AI will also make real-time adaptation feasible, preparing managers for future predictions and staying ahead of marketplace swings.

2) Automation and operational efficiency

Robo-advisors will become necessary tools, autonomously managing tasks like portfolio rebalancing and standard operations. AI’s algorithmic training will execute decisions quickly, decreasing human intervention and cutting costs. AI will automate tedious back-office operations, including data entry and regulatory compliance procedures, ensuring smooth, streamlined workflows.

3) Client experience transformation

In the future, client interactions will become customised and more responsive. AI will analyse purchaser information to provide tailored funding recommendations, and AI-powered chatbots will be available 24/7 to answer queries. The technology can even simplify reporting, turning complex economic information into easily digestible, jargon-free insights, building trust and transparency in customer relationships.

Conclusion:

The future of asset management is undeniably tied to improvements in AI technology. AI-driven fixed asset software is already impacting asset monitoring, predictive analytics, and risk management by optimisation and automation. As hyper automation and IoT continue to adapt, the possibilities for remodeling asset management are limitless.

(Photo source)

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Machine learning is a subset of artificial intelligence used to develop algorithms and statistical models to enable computers to perform specific tasks without the need for instructions.

Businesses have started to incorporate machine learning app development services and functionality into their processes, applications, and practices to ensure optimal efficiency. By leveraging these services, companies can integrate advanced machine learning capabilities in their operations, enabling automation, data-driven decision-making, and performance optimisation. Integration empowers businesses to stay competitive in today’s fast-paced digital landscape by unlocking new insights and streamlining processes for smarter, more efficient operations.

Below we discuss machine learning innovation that transforms business applications.

Five innovative ways to use machine learning in businesses

Machine-learning statistics have shown that different industries can benefit from using innovative machine-learning methods to get ahead in business processes.

1. Enhancing customer experience through machine learning

Businesses must enhance their customer experiences to build loyalty and drive engagement. Two effective ML strategies can be used to help enhance the overall experience of customers.

Chatbots and virtual assistants: These can help transform customer services by providing round-the-clock support for customers who need assistance. They can handle various tasks like answering questions and assisting with inquiries.

Some of the benefits included in using these machine learning technologies include:

• 24/7 availability: Chatbots are available any time, so employees do not need to work extra shifts or at night to be available. Unsupervised machine learning systems use artificial neural networks to continue interacting with customers and retain existing customers.
• Speed and efficiency: Chatbots and virtual assistants can process information quicker than humans and eliminate wait times for customers. Providing training data, and using data science will allow chatbots to communicate with customers.
• Scalability: Chatbots can be changed and will evolve to handle more than one task, like dealing with multiple inquiries at the same time, and provide businesses with the opportunity to use technology without needing to increase their staff.
• Personalisation: Based on customer data, chatbots and virtual assistants can personalise their interactions with customers like using real names, remembering past interactions and providing responses that are tailored to what the customer is requesting.

Personalised recommendations: Using data analysis and machine learning can tailor personalised suggestions to customers based on past interactions, behaviours, and preferences.

Here are some of the benefits of using personalised recommendations to improve the overall shopping experience.

• Data utilisation: Businesses can identify patterns and preferences by analysing customer data. For example, an e-commerce platform can use a customer’s browser history to track their interests.
• Increased engagement: Creating personalised recommendations will increase user engagement. Customised suggestions will lead to customers making more purchases, and this will increase an individual customer’s time on-platform, helping you understand what the customer needs and wants.
• Customer loyalty: Understanding what customers want and prefer will create customer loyalty because customers will feel that their needs and wants are being recognised and will continue to purchase from the business.

2. Machine learning optimising operations

Businesses need to optimise business processes to enhance efficiency, improve overall performance and reduce costs. For optimisation processes, there are two key areas of focus: managing the supply chain and predictive maintenance.

Supply chain management: This strategy focuses on improving the effectiveness and efficiency of the supply chain from the purchase of raw materials to the finished products. These are some key strategies that can be used in supply chain optimisation:

• Forecasting demand: Advanced analytics can be used to predict customer demand more accurately. This will help business operations by reducing the costs associated with excess stock and align inventory levels with sales.
• Inventory management: Implementing inventory management practices will help minimise expenses to the business and ensure that it has enough stock on hand when needed. Management can be achieved by using automated inventory tracking systems.
• Supplier collaboration: Collaborating with suppliers can help improve communication and build stronger relationships.
• Logistics optimisation: Transportation routes and methods can be analysed to improve delivery times for businesses and increase productivity. Businesses can use software development to help optimise and consider alternative transportation modes.
• Technology integration: Using technology in the business can help with business decisions like whether to use blockchain for transparency, automation for efficiency and AI for predictive analytics.

Predictive maintenance: This process involves using machine learning and data analytics to predict when a machine or equipment is likely to require maintenance or fail. Here are some of the key factors of predictive maintenance:

• Data analysis: Machine learning algorithms analyse collected data to help predict outcomes like machine failure. This can help businesses schedule maintenance ahead of time to avoid loss of production.
• Data collection: Processes of data collection use sensors and IoT devices to collect data in real-time from machines like operational performance metrics, vibration and temperature.
• Implementation: Businesses can schedule maintenance of machinery during non-peak hours or when equipment is least used to reduce the delay in production.
• Continuous improvement: Using machine learning systems can lead to making more accurate predictions and help improve maintenance strategy for business processes.

3. Data-driven decision making

Using data-driven decision-making for business decision-making is a strategic approach which will help guide business decisions. Companies can use business intelligence, marketing innovations, analytics and risk management to enhance the operational efficiency of their business applications.

Here is how each component will advance the company’s processes.

Business intelligence and analytics: These refer to the practices and technologies that are used to provide analysis, collect and present business data. The key aspects of this approach include:

• Data visualisation: Business intelligence can help employees understand complex data points of the business in visual reports and by providing dashboards where this data is easily accessible.
• Descriptive analytics: Using historical data to understand the past performance of the business can influence future decisions by creating a machine learning model, and businesses can collect data to have analytics on hand.
• Predictive analytics: Using machine learning for business techniques and statistical models can help predict outcomes for the business.
• Prescriptive analytics: Prescriptive analytics will recommend actions based on predictive insights.

Risk management: Using data-driven decision-making can be effective for managing risk in the business. The following methods can be used to identify, mitigate and assess risks in the business.

• Scenario analysis: Machine learning models can represent scenarios to prepare for any risks that could affect the business.
• Risk assessment models: Businesses can use machine learning capabilities to help develop models to predict and analyse potential risks.
• Real-time monitoring: Machine learning applications can help monitor any risks in real-time to be able to manage any risks to the business.
• Compliance and regulatory monitoring: Machine learning systems can be used to help businesses stay compliant with regulations by constantly monitoring business activities.

Marketing innovations have specific key insights into how businesses can manage risks to the business. These key innovations include:

• Customer segmentation: Businesses can segment their audience based on their preferences, behaviors and demographics.
• Personalisation: Data analytics can help businesses deliver personalised customer experiences by tailoring offers or messages to enhance customer engagement.
• A/B testing: Businesses can use a machine learning algorithm to conduct A/B testing of marketing campaigns, product offerings and website designs.
• Predictive customer analytics: This can help businesses predict future purchasing patterns using product recommendations and targeted promotions.

4. Human resources transformation

Businesses can transform human resources as a strategy to enhance HR functions and ensure that they align with their business goals and adapt to the evolving workplace. Talent acquisition and employee engagement are two of the critical components used in this transformation.

Employee engagement has key elements that can foster a committed workforce. These key elements include:

• Continuous feedback and communication: Businesses can use machine learning models to get feedback from employees and put systems in place to help regular check-ins with staff.
• Employee well-being: Wellness programmes can be implemented to enhance the well-being of employees.
• Career development opportunities: Using machine learning, businesses can provide training programmes for employees to advance their skills.

Talent acquisition can incorporate artificial intelligence tools to scan for the best candidates to fill any vacancies. Using a machine learning system to find the most suitable candidates will eliminate the need for traditional recruitment practices, ensure that the candidate has the correct job experience, and help keep track of the applicants by staying in communication and improving the hiring process.

5. Industry specific applications

Using machine learning in applications will enhance efficiency, compliance and service delivery in industries like financial institutions and healthcare.

For healthcare applications, machine learning algorithms are used in the following ways:

• Electronic health records: Patient care can be streamlined using machine learning models to provide healthcare workers with access to patient information quickly.
• Telemedicine: Allowing remote consultations for those patients who are unable to leave their homes or those who live in rural areas where doctors are not easily accessible.
• Health information exchange: Allows patient information to be shared among colleagues and different healthcare providers to improve patient treatment.

For finance applications:

• Automated trading systems: Machine learning systems can help analyse market data and trends to help businesses and customers make informed decisions when trading.
• Blockchain technology: Machine learning algorithmn offer a transparent and tamper-proof ledger, reducing the cost of transactional data and enhancing security.
• Robo-advisors: These are automated investment management services which allow users to get advice on how to set investment goals and minimise their risk.
• Fraud detection systems: To assist in fraud detection, machine learning systems prevent financial losses and protect customer data.

Future trends in machine learning

The advancement of AI technologies like deep learning, natural language processing, and reinforcement learning will lead to significant advancements in machine learning.

Advances will also increase use by businesses of all sizes by allowing new tools to be incorporated into existing business practices, like using cloud-based platforms or open-source frameworks to leverage machine learning systems without requiring extensive technical expertise.

For innovation across various industries, machine learning systems can be implemented to optimise processes, develop new services and products, and identify trends.

Conclusion

Machine learning will evolve as technology advances and the future of machine learning applications will arrive rapidly. Businesses will have increased productivity by using AI to unlock new opportunities to enhance their operations.

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Law firms are seen as traditional, not as eager adopters of new technology, but most have used machine learning (ML) for years. Embedded in popular platforms like Westlaw, ML is often incorporated into core operations. 

Now, generative AI is spreading through law firms faster than class-action claims over a stock fraud. Individual lawyers have learned to use ChatGPT-like AI models, and entire law practices have harnessed large language models. 

Those in the business of law see remarkable gains from AI in efficiency, accuracy, speed and client results in their day-to-day processes. Three points help explain those results. 

In legal operations, AI-driven time and cost savings are typically very high. The gains are not incremental.

• AI is applicable to potentially most work processes at law firms.
• Once law firms implement AI, it grows steadily more powerful as they personalize it. This is basically customisation; adapting AI to their preferred work methods takes the return on investment (ROI) higher: 

Meet the AI-native law firm

These benefits have led to the emergence of AI-centric (aka AI-native) law firms, a new breed that is significantly more efficient and competitive than its rivals. At AI-native firms, most support staff and attorneys already leverage AI extensively for intake, research, drafting motions, briefs, objections, analysing judges’ opinions, and more.

A law practice becomes AI-native, in part, by personalising the behaviour of AI solutions to mesh with the firm’s existing processes and strategic guidelines. This makes their AI more capable and valuable.

Personalisation takes various forms, like creating case evaluations that follow a firm’s established standards. AI can consider potential claims and create follow-ups according to an attorney’s criteria. It can be taught to follow an existing process, mimic sequences of events, ask or answer key questions along the same pattern, and write in the style of previous case work. 

Once trained to emulate an attorney’s approach, an AI model makes life easier for support staff. Even if a paralegal hasn’t worked with specific lawyers, AI will help them with case preparation and client interactions, risk assessment, and even strategy. 

AI-native law firms increasingly use generative AI to service clients who require individualised treatment. AI contributes throughout the case lifecycle, from brainstorming pre-litigation case strategy, to handling discovery. Gen AI-based models also help prepare depositions, analyse their results, and plot litigation strategies.

Why is AI extraordinarily useful to law firms? 

It’s been said that the legal world is made of six-minute increments. Often, AI can often do in seconds what takes hours or days for a junior associate. Time reductions of up to 99% drive major cost savings, and in the intellect-intensive field of law, they are common. 

Every day, lawyers must evaluate, analyse and weigh tradeoffs, draft documents, and make decisions. Paralegals and junior associates need to work fast and accurately, yet never overlook anything important. With volumes of data and minutiae to wade through, the work can exhaust them, leading to mistakes.

Overall, speed, scale, and personalisation contribute to make AI a massive accelerator in the legal field, with productivity gains well beyond the “traditional” 10 to 20 percent.

Costs come down and move around in AI-native law firms

Lawyers are learning first-hand that AI systems can minimise the associate hours it takes to complete a process. By engaging AI across the life cycle of cases, they can reshape individual workloads for greater profitability. Upfront work on cases is sometimes undercompensated, and AI lets the team concentrate billable hours on later, fully-compensated stages. 

AI-centric firms can also grow without expanding the headcount of support staff. Instead, existing staff can assist more associates, who bill at higher hourly levels, increasing profitability. 

They can also market themselves and drive growth more vigorously. Wherever AI reduces operational costs, it frees up funds for marketing and business development. Generative AI makes marketing communications faster and easier for law firms, as it does for other businesses.

Employee experience: AI happiness 

AI often does not get the credit it deserves for its positive impact on employee experience. In practice, lawyers and paralegals can offload most so-called grunt work and repetitive tasks to AI. This boosts job satisfaction and — by implication — retention. Support staff and junior associates become, in effect, supervisors of AI.

They can customise the firm’s AI by teaching procedures to an LLM, and then share them across a team. This means lawyers can operate in familiar ways but at a larger scale, and delegate more comfortably to support staff without lengthy explanations of “Here’s my way of doing this.”

Business models shift for AI-native law firms

 AI-native law firms can uplevel their business to increase capacity and support revenue growth. Specifically, they can structure internally to handle more complex cases and lucrative contingency work. AI enables smaller firms to handle larger, tougher cases by whipping through much of the research and analysis.

In contingency litigation, productivity gains stemming from AI can even exceed those seen in other legal categories like contracts, intellectual property, and family law. AI can handle much of the upfront evaluation of contingency cases. Taking on well-researched contingency cases can significantly increase profitability.

Those who get AI versus those who don’t

Given the benefits, are law firms jumping on board and going AI-native in droves? Surprisingly no, according to a 2023 Thomson Reuters survey that found 60% had no plans to use generative AI. That’s good news for the other 40%. Law firms that leverage AI effectively have a marked advantage over competitors that do not.

The legal profession ranks among the industries achieving the best gains from use of AI. Law firms that “get it” will continue to personalise AI systems and push towards their potential, and grow more profitably. As it becomes increasingly obvious that AI-native law firms enjoy greater growth and profitability, other intellect-based professions may well follow their example. 

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Claude 3.5 Sonnet is now accessible for free on Claude.ai and the Claude iOS app, with higher rate limits for Claude Pro and Team plan subscribers. It’s also available through the Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI. The model is priced at $3 per million input tokens and $15 per million output tokens, featuring a 200K token context window.

Anthropic claims that Claude 3.5 Sonnet “sets new industry benchmarks for graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval).” The model demonstrates enhanced capabilities in understanding nuance, humour, and complex instructions, while excelling at producing high-quality content with a natural tone.

Operating at twice the speed of Claude 3 Opus, Claude 3.5 Sonnet is well-suited for complex tasks such as context-sensitive customer support and multi-step workflow orchestration. In an internal agentic coding evaluation, it solved 64% of problems, significantly outperforming Claude 3 Opus at 38%.

The model also showcases improved vision capabilities, surpassing Claude 3 Opus on standard vision benchmarks. This advancement is particularly noticeable in tasks requiring visual reasoning, such as interpreting charts and graphs. Claude 3.5 Sonnet can accurately transcribe text from imperfect images, a valuable feature for industries like retail, logistics, and financial services.

Alongside the model launch, Anthropic introduced Artifacts on Claude.ai, a new feature that enhances user interaction with the AI. This feature allows users to view, edit, and build upon Claude’s generated content in real-time, creating a more collaborative work environment.

Despite its significant intelligence leap, Claude 3.5 Sonnet maintains Anthropic’s commitment to safety and privacy. The company states, “Our models are subjected to rigorous testing and have been trained to reduce misuse.”

External experts, including the UK’s AI Safety Institute (UK AISI) and child safety experts at Thorn, have been involved in testing and refining the model’s safety mechanisms.

Anthropic emphasises its dedication to user privacy, stating, “We do not train our generative models on user-submitted data unless a user gives us explicit permission to do so. To date we have not used any customer or user-submitted data to train our generative models.”

Looking ahead, Anthropic plans to release Claude 3.5 Haiku and Claude 3.5 Opus later this year to complete the Claude 3.5 model family. The company is also developing new modalities and features to support more business use cases, including integrations with enterprise applications and a memory feature for more personalised user experiences.

(Image Credit: Anthropic)

See also: OpenAI co-founder Ilya Sutskever’s new startup aims for ‘safe superintelligence’

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Project P.I. leverages generative AI and computer vision technologies to detect issues such as damaged products or incorrect colours and sizes before they reach customers. The AI model not only identifies defects but also helps uncover the root causes, enabling Amazon to implement preventative measures upstream. This system has proven highly effective in the sites where it has been deployed, accurately identifying product issues among the vast number of items processed each month.

Before any item is dispatched, it passes through an imaging tunnel where Project P.I. evaluates its condition. If a defect is detected, the item is isolated and further investigated to determine if similar products are affected.

Amazon associates review the flagged items and decide whether to resell them at a discount via Amazon’s Second Chance site, donate them, or find alternative uses. This technology aims to act as an extra pair of eyes, enhancing manual inspections at several North American fulfilment centres, with plans for expansion throughout 2024.

Dharmesh Mehta, Amazon’s VP of Worldwide Selling Partner Services, said: “We want to get the experience right for customers every time they shop in our store.

“By leveraging AI and product imaging within our operations facilities, we are able to efficiently detect potentially damaged products and address more of those issues before they ever reach a customer, which is a win for the customer, our selling partners, and the environment.”

Project P.I. also plays a crucial role in Amazon’s sustainability initiatives. By preventing damaged or defective items from reaching customers, the system helps reduce unwanted returns, wasted packaging, and unnecessary carbon emissions from additional transportation.

Kara Hurst, Amazon’s VP of Worldwide Sustainability, commented: “AI is helping Amazon ensure that we’re not just delighting customers with high-quality items, but we’re extending that customer obsession to our sustainability work by preventing less-than-perfect items from leaving our facilities, and helping us avoid unnecessary carbon emissions due to transportation, packaging, and other steps in the returns process.”

In parallel, Amazon is utilising a generative AI system equipped with a Multi-Modal LLM (MLLM) to investigate the root causes of negative customer experiences.

When defects reported by customers slip through initial checks, this system reviews customer feedback and analyses images from fulfilment centres to understand what went wrong. For example, if a customer receives the wrong size of a product, the system examines the product labels in fulfilment centre images to pinpoint the error.

This technology is also beneficial for Amazon’s selling partners, especially the small and medium-sized businesses that make up over 60% of Amazon’s sales. By making defect data more accessible, Amazon helps these sellers rectify issues quickly and reduce future errors.

(Photo by Andrew Stickelman)

See also: X now permits AI-generated adult content

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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A study from SS&C Blue Prism, conducted by Forrester Consulting and published in April, put together a composite organisation representative of five customers interviewed. The conclusion was that, over three years, there were key gains in IA from greater productivity to compliance cost avoidance, to improved employee experience and retention. This represented an overall net present value of $53.4 million (£42.5m) per customer.

Yet this may just be the tip of the iceberg. Dan Segura, enterprise sales manager at SS&C Blue Prism, notes one healthcare client who, in what is described as a conservative estimate, delivered savings of more than $140m overall on cost avoidance and recoup. Another healthcare client delivered a use case with a claimed $43m benefit on its own; a bot which recouped overtime pay for nurses and staff during the pandemic.

“They built it in an afternoon,” Segura explains. “It’s a perfect example of being in the right place at the right time; and having the right skills and technology being ready.”

Many of the technologies which comprise intelligent automation have been around for a long time, such as classic RPA (robotic process automation) or OCR (optical character recognition). SS&C Blue Prism’s document automation, which forms part of the latter, is described as a ‘game-changer’ by Segura. “There’s a lot of these processes, whether it’s going to be executed by a robot or a human,” he says. “First things first, we’ve got to get data off documents.

“Automation is not just doing simple tasks anymore thanks to the introduction of AI and generative AI” he adds. “There’s now more understanding, whether it’s assessing information from documents, information from a message, structuring things that are semi-structured or unstructured, to drive the process or complete the process.”

Segura describes wider business process management (BPM) and process orchestration tool Chorus, meanwhile, as ‘one of the world’s best kept secrets.’ Or, at least, it was; in November analyst Everest Group named the tool as a leader and star performer in its Process Orchestration Products PEAK Matrix.

The tool is now getting leverage outside the traditional finance and insurance fields. “It is how millions and millions of transactions and pieces of work are getting done every day,” says Segura. “We’re now seeing adoption [elsewhere] alongside automation to orchestrate their work and give them that end-to-end work orchestration, visibility, and efficiency gains with whatever they have going on.”

So how does a use case come to life? It is often a mixture of inspiration and perspiration. Where SS&C Blue Prism comes in is to ‘help customers catch lightning’, as Segura puts it. “We’ve all been in that situation where it’s like ‘oh if I were running this place, here’s what I would do’,” he says. “Intelligent automation gives you the opportunity to reimagine your processes and transform how you get work done. Once that light switch turns on, and the initial use case is built, that’s really the secret sauce of SS&C Blue Prism; it’s that realisation and awareness of what intelligent automation can deliver.

“We’re always learning from our customers,” adds Segura. “It’s at their direction because they know their business and processes better than anybody. Combine their business expertise with the transformational power of intelligent automation and its digital workforce, then that’s where the magic happens.”

Any organisation, argues Segura, regardless of the industry, has change agents and citizen builders in waiting. Don’t think that’s a misnomer; the term is definitely ‘builder’.

“I hear about these citizen developer programmes, and they’ll say, ‘here we have 500, 1000 citizen developers.’ What I don’t hear is, ‘and with this army of citizen developers we’ve achieved this’,” says Segura. “Whereas I have customers where two people have basically become citizen builders with more of a robust type of approach.” The $43m healthcare single use case is a case in point. “It is the whole mantra of SS&C Blue Prism,” adds Segura. “We’re designed to go after those higher value chain automations that can have a tangible impact on some of the company’s key objectives.”

So, you have the idea, the value proposition, and the capability to build it out. How do you make it stick?  Every organisation is different; though if your company has a continuous process improvement department then that can be a good place to start. Segura likens it to offshoring processes. “You don’t just wave it goodbye and never think about it again,” he explains. “At the end of the day, it still has to function.

“You’re not just ‘digital-shoring’ [automation] and it will essentially be taken care of by digital. Someone has to continuously improve the process; someone has to mind when something changes with the business rules or regulatory compliance; somebody has to be responsible for making sure that those changes are kept up in an agile way.”

SS&C Blue Prism has a longstanding, large US retail customer that combines that lightning capture with the right internal culture around automation. This is a company that has 72,000 employees, as well as 60 ‘digital workers’ executing more than 150 automations. One such automation, through using OCR technology, lets the company automate the processing of inbound customer orders received by digital fax.

The overall result is 6.2 million transactions processed to date, and 250,000 hours of work returned to the business. But there is one extra ingredient required, particularly for a big company: discipline.

“It took them a while to get to that point in maturity,” explains Segura. “They do have a very central function when it comes to the intelligent automation team, [but] keep in mind one of those processes is in supply chain. That process is regularly reviewing 4.2 million purchase orders; it’s minding 50 million inventory case volume; it’s going through two million SKUs for 8000 suppliers.

“This is highly iterative, but it’s that process of having that lightning rod to capture the requirements and give people who are not necessarily technical a platform and a methodology to iterate very closely with the intelligent automation team,” adds Segura.

Think of what SS&C Blue Prism does therefore as providing a superhero cape for those who don’t otherwise get the chance to step into the limelight. It is a message the company will look to broadcast at the Intelligent Automation event in Santa Clara on 5-6 June.

“SS&C Blue Prism opens up that door to enable your citizen builders really make an impact and deliver strategic benefits to the company,” says Segura. “You’re not just playing with a pilot, not just fooling around with something; you’re really getting into the strategic objectives of the company.”

Photo by Tara Winstead

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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