As we enter 2025, renewed optimism is on the radar. Inflation is going down globally, and with it, interest rates. The European Central Bank has cut interest rates several times through the year to 3.25% in October 2024. The Federal Open Market Committee also eased the rates through 2024. Further cuts are expected in 2025. 

As borrowing becomes cheaper, real estate businesses have renewed optimism. Among global CRE investors, 88% expect their company’s revenues to increase in 2025, a major shift from the 60% who expected further declines last year, according to Deloitte. 

With new cash reserves, 81% want to reinvest their profits in data and technology.  Real estate firms have long relied on intuition and retrospective data to make decisions—a tendency that left many vulnerable to unforeseen economic, climate, and compliance risks. 

Data analytics and business intelligence (BI) can help real estate companies better understand the interconnected risks and opportunities to establish more profitable operations. 

6 Data Analytics and BI Use Cases in Real Estate 

The real estate sector has been slow to digitize. Big data analytics is already widely used in healthcare. Yet, 60% of real estate firms still use spreadsheets for time reporting, 51% — for property valuations and cash flow analysis, and 45% — for budgeting and forecasting. 

Not only do legacy technologies create inefficiencies, but they also hamper decision-making. Only 13% of real estate companies can access up-to-date business intelligence and real-time analytics solutions. 

And those who do, gain an edge over the competition. ‘Instant’ home offer platform, Upstix uses a data analytics platform to identify the most profitable properties. “[The tool provides] demographic type, propensity to sell, and property type, all in different regions within the UK. In the first three months of using this tooling, we increased our conversion by 3.5X,” shared Fred Jones, COO at Upstix. 

Real estate companies have vast data reserves lying dormant. Operationalizing these reserves can bring major operational benefits. We’ve lined up the six most promising data analytics use cases for real estate to pursue profit growth. 

Property Valuation 

Real estate valuation is already a data-driven process. But it’s often ridden with inefficiencies as many records remain undigitized and legacy systems—disconnected. Data analytics solutions can help your teams consolidate and cross-validate insights from various sources including historical sales, neighborhood trends, climate insights, and social indicators to create more accurate valuations. 

The new generation of automated valuation models (AVMs) uses machine learning algorithms to produce more accurate comparables and pricing. Compared to traditional, rule-based methods, ML models can identify new data correlations and respond to market signals faster than human evaluators. This increases the speed of decision-making and enables new largely-automated buying experiences like the iBuyer business model, used by Zillow, Opendoor, Trulia, or Zoopla. 

Edvantis has helped KPC Labs launch an ML-powered engine for the US real estate market. Hosted on the AWS infrastructure, the system features real-time data integration pipelines and robust data enhancement services. The pre-cleansed data is then fed to several machine learning and deep learning models to produce: 

• Predictions about likely-to-list (likely-to-sell) off-market properties 
• Home value ranges forecasts and outliers based on competitive market analysis 
• Customer behavior predictions for lead generation
• Similarity analysis across various propers 

Moreover, AVMs are already supporting the majority of mortgage lending and mortgage-backed security risk assessments. 13 of the top 15 mortgage lenders in the UK use AVM as an integral part of their processes. 

Climate Risk Management 

With data analytics, real estate investors and developers can gain richer insights about the property’s potential return and risk. Advanced models can assess diverse data in multiple formats—visual floor plans, geospatial neighborhood data, demographic reports, traffic counts, tenant behavior, and more—to give more accurate ROI estimates and avoid being blindsided by risks. 

Across the US, some $121-$237 billion of properties are over-valued because of the current flood risk. Overvaluations create a negative chain effect. Home insurers pull out of high-risk areas, making property ownership even more expensive and posing a major economic threat to the real estate industry. 

Source: Yale Climate Connections

Data analytics can help real estate companies better understand the real regional risks. For instance, ZestyAI has developed proprietary data models, offering risk and value insights about properties based on climate data. Its system evaluates the property’s vulnerability to hail, wildfire, storm, and wind damage. In addition, ZestyAI continuously monitors the property, detecting changes in roofs and facades, as well as recalibrating climate risks based on the latest data. This way, property owners can stay well-informed about potential risks impacting property value and take proactive loss control measures.

John Rogers, chief innovation officer at CoreLogic, is also a firm proponent of using data analytics for climate risk mitigation. The company recently deployed a Climate Risk Analytics tool, which allows companies to accurately measure and mitigate the impact of climate through every single property across the US up to the year 2050.  The system calculates over 20+ detailed risk measures across various climate scenarios. The metrics can then be used by organizations to understand the physical and economic risks of climate change and the financial impacts on portfolios, so they can more confidently measure, mitigate, and manage risk.

Rental Profitability Analysis

Investors are obsessed with chasing top markets, indicating profitable rental opportunities. 

BI tools can help gain more granular insights into the market trends, occupancy, and vacancy rates to optimize pricing and revenue in real-time. 

Modern rental profitability analytics platforms automate data collection from public and internal sources, helping analysts generate more precise comp sets and daily unit-level rents to optimize revenue. Moreover, you can slide and dice property data to optimize cap rates, cash flow, or long-term appreciation, ensuring that your portfolio stays aligned with your investment strategy. 

AirDNA specializes in data analytics for the short-term rental market. The platform aggregates rental rates, occupancy rates, and seasonal trends, to offer detailed analysis and forecasts for properties listed on Airbnb and VRBO. Using AirDNA’s platform, Venture REI managed to improve its occupancy rates from 48% to 92% before a big in-town event by adjusting the pricing according to the market signals. In addition, they improved conversion rates on inbound owner leads to 75%, doubling their short-term rental portfolio size in just 8 months.  

Canadian commercial real estate developer CBRE Group Inc. also believes that the key to better rental profitability lies in data. Or more precisely–location data. The company developed a proprietary location intelligence technology platform. Dimensions combines aerial imagery, competitor locations, and census data to visualize market share and trade areas. With this data, CBRE Group Inc. can run a more comprehensive analysis of their clients’ portfolios to optimize profitability and find yet untapped market opportunities. 

Tenant Management 

Data analytics also extra efficiencies into tenant management processes, allowing property owners to find, approve, and sign on new occupants faster. Instead of manual forms and paper-based document submissions, your teams can automatically pull background data via APIs to pre-screen new applicants.  

For instance, Doorloop property management software comes with a data-driven analytics engine for performing background, credit, and bankruptcy checks on prospective tenants in a matter of several clicks. The tool also automates other tedious tasks like payment collection and lease management. 

Predictive data analytics tools can also alert property managers about tenant turnover. Silver Homes says its tools can forecast about 75% of tenant turnover using historical and behavioral data. Its technology also provides insights into reasons for moving, helping property managers ensure high occupancy rates. 

Customer Engagement Tools  

Compared to other sectors, real estate has been lagging in digital customer experience (CX). McKinsey found that residential rental companies offering a superior CX enjoy a 15% premium in net operating income (NOI), compared to players who skip on this, for buildings with similar characteristics. 

The key to better CX is data as it enables more personalized, delightful experiences at unparalleled scale.  Real estate companies that use technology to improve customer interactions enjoy a 2% to 4% increase in NOI and capture extra profits from the sale of ancillary services (including event space rentals, cleaning services, and grocery delivery), McKinsey says. 

Data analytics can also become a launch pad for new customer-facing products. Redfin has transformed its massive data analytics program into public tools, now aiding it in customer acquisition and retention. The Hot Homes feature suggests to buyers which properties will sell off fast based on over 500 analyzed attributes, encouraging them to book a visit sooner. 

Global real estate player Jones Lang LaSalle (JLL) uses data analytics to help its institutional clients make smarter portfolio management decisions. Over the years, the company recommended one of its clients more than 300 specific actions, resulting in $120 million in annual savings and a reduction of more than 2 million square feet of underperforming assets. 

Optimized Marketing Strategies 

Data-driven marketing has become the norm across industries, and real estate companies are catching up. With the help of algorithms, leaders are successfully optimizing their strategies for property marketing, lead generation, and lead qualification. 

Over the years, Keller Williams has transformed from a residential real estate leader to a tech-led company, investing heavily in cloud, data analytics, and most recently AI deployments. In 2023, KW launched a new Paid Ads platform for its global workforce, offering agents a better experience for lead generation and qualification. The Automated Market Snapshots feature, for example, provides agents with instant hyper-local market analytics for launching ad campaigns.  Opportunities APIs, in turn, allows KW agents to easily track and manage deals across the entire lifecycle. 

Leadflow, in turn, developed intelligent lead generation software for real estate companies. It provides users with access to over 150 million property records to find different categories of leads and then qualify them using the platform’s AI algorithm. Each lead receives a score, based on their propensity to sell in the next 90 days. According to the company, marketing to such leads yields 271% higher response rates, compared to a general list of leads.  

Time to Act on Data Analytics in Real Estate 

Real estate isn’t short for data per se.On the contrary—it’s available in multiple formats, from multiple public and corporate systems. What many real estate leaders lack is the proper data management infrastructure to enable seamless data aggregation, cleaning, and transformation—an area where Edvantis can help. 

As part of our data science services, we help companies create the optimal infrastructure for launching advanced data analytics solutions and scaling business intelligence adoption across the entire company. From database optimization to data pipeline optimization and custom ML model development, we help real estate businesses gain the most out of their dormant data. Contact us to learn more about our services. 

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The UiPath Partner Network recognizes Edvantis’ proficiency and expertise in implementing automation solutions. As an Authorized Partner, Edvantis will collaborate with UiPath to bring innovative RPA solutions to our customers, enabling them to resolve complex operational challenges, streamline workflows, and optimize productivity for businesses. 

This partnership strengthens Edvantis’ commitment to delivering advanced robotic process automation (RPA) solutions to clients, enhancing operational efficiency, and enabling digital transformation across industries. Charles, Technical Director at Edvantis, says:

About Edvantis 

Edvantis is a global software engineering and consulting company, specializing in providing technology solutions that empower businesses to innovate and grow. With a proven track record in delivering innovative technology solutions, and a team of skilled professionals, Edvantis has become a trusted partner to clients across various industries, supporting their digital transformation and process optimization needs. 

About UiPath 

UiPath is a leading enterprise automation software company, focused on accelerating human achievement by streamlining repetitive processes through RPA. Known for its end-to-end automation platform, UiPath enables businesses to transform operations by combining AI with advanced automation capabilities. Serving companies of all sizes and across industries, UiPath empowers organizations to scale efficiently and remain competitive in an increasingly digital world. 

About the UiPath Partner Network 

The UiPath Partner Network is a diverse community of trusted professionals committed to transforming work into a more fulfilling, valuable, and strategic experience. UiPath partners help businesses create fully automated enterprises by leveraging the industry’s leading, most reliable, and widely adopted Robotic Process Automation (RPA) platform. 

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Greater connectivity, data availability, and are powering major digital transformations in the mobility sector, creating yet untapped opportunities for revenue diversification.  Edvantis regularly brings technology and business leaders to discuss what’s happening in the transportation sector. Here are our main findings. 

4 Sectors of Innovation in The Mobility Sector 

Three forces are shaping the transformation of the mobility sector: consumer demand, regulations, and technology.

Car owners have a growing interest in electric vehicles (EVs) and software-defined vehicles (SDVs). Although many also want agile, on‑demand, and affordable alternative transportation (car sharing, e-bike rentals, and more). Regulators, in turn, are pushing for greater adoption of public transportation and reduced car ownership, which can cut sharply into OEMs’ revenues unless they consider new digital-enabled revenue streams beyond car sales. 

All of these major changes are fueled and enabled by emerging technologies that create new opportunities for innovation across the following four axes. 

1. Software-Defined Vehicles 

The automotive sector continues its push towards software-defined vehicles — car models with add-on features, primarily or entirely enabled through software, rather than hardware characteristics alone. By 2030, more than 95% of the passenger cars sold are likely to have embedded connectivity. This shift presents major opportunities for new value creation, both for OEMs and other players in the mobility ecosystem. 

And the possibilities for monetization are endless: 

• Advanced Driver Assistance Systems (ADAS) and Level 2, 3, and 4 autonomy features 
• Predictive maintenance offerings, powered by real-time vehicle telematics data 
• Improved navigation and real-time information services 
• Telematics insurance products via partnership agreements 
• Battery charging and management optimization (for electric vehicles) 
• Personalized infotainment experiences, powered by a growing app ecosystem 

In-car services, in particular, are a rapidly growing market. Five in six automotive executives believe that digital services will be the key differentiating factors for competitive advantage in the automotive industry by 2040. 

Mercedes-Benz is working on an MB.CONNECT package — a bundle of in-car digital services, covering comfort, improved driving, and on-the-go entertainment. The company expects that from 2025 onwards, it could bring 80% customer retention and extra profits. 

General Motors also invested in building an end-to-end vehicle software platform (already coming with the latest model releases) to support over-the-air (OTA) updates, vehicle-to-everything communication (V2X), and its newest batch of in-car services. The OEM hopes to generate over $25 billion in revenue from in-car subscriptions by 2030. 

Consumers appear enticed. According to McKinsey, 49% of German and 58% of US consumers in the premium segment are very likely to buy a car with connectivity features. Almost 40% of US and German buyers are also open to switching automotive brands for better connectivity.  

Yet, consumer enthusiasm often fizzles out when it comes to purchases. The majority aren’t ready to pay extra for individual digital services or bundles at the current prices.

Source: McKinsey 

Likewise, many are concerned about how new features will affect their privacy — and automotives so far haven’t excelled in this area. A report from Mozilla gave 25 major OEMs failing marks for consumer privacy. Among the evaluated connected car brands, 84% shared vehicle owner data with third-party service providers and data brokers, oftentimes for a profit and without customers’ explicit agreement. 

Clearly, OEMs need to further refine the value prop for in-car digital services both to set more competitive prices and to address rightful concerns about privacy and security. 

Opportunities For Innovation

• Ethical ecosystem partnerships. Collaboration with third parties (insurance companies, mobility services providers) should be transparent for end-consumers. To achieve that, OEMs will need to implement a more transparent data governance process to prevent sensitive data leaking and a greater degree of customer data anonymization. Masked consumer data can still produce substantial benefits with the right data science approach. 

• Cybersecurity solutions for connected vehicles.  A standard connected vehicle can generate 25 GB of data per hour and collect information from more than 100 different data points (including sensitive ones like geolocation or biometrics). A data breach poses major risks. The development of new digital services thus requires strong security practices (in addition to secure hardware and middleware components) — extensive application and API security testing at every stage, which can be achieved with modern QA automation frameworks. 

2. EV charging infrastructure 

By 2030, EV stock can expand to almost 350 million vehicles. But future sector growth will hinge on public-private efforts to expand EV charging infrastructure availability. The US Department of Energy estimates that 1.2 million public charging ports will be needed by 2030. The EU is short on 8.8 million EV charging points.  

Players from multiple industries are already working in that direction. Brooklyn-based Itselectric aims to bring more Level 2 charging points to urban blocks by using existing energy infrastructure. The team partners with property owners and city officials to install curbside charges and share profits with nearby property owners. 

IONITY, a joint initiative by BMW Group, Ford Motor Company, Mercedes-Benz AG, and Volkswagen Group, has already built over 450 high-performance charging stations across Europe, delivering up to 350 kW of power per charge. In the US, BMW, GM, Honda, Hyundai, Kia, Mercedes, and Stellantis, launched a similar project this year. Ionna promises to construct over 30,000 charging stations across the U.S. by 2030. 

As part of their digital transformation efforts, energy companies also seek EV infrastructure deals. EDF and global infrastructure investor Morrison signed a partnership deal to deploy 8,000 ultra-fast charging points across France by 2030 to support EV market growth. Mer, a European EV charging company owned by Statkraft, recently signed a deal with IKEA in Germany to install 1,000 charging points across 54 locations nationwide. 

EV charging infrastructure construction, however, is just one aspect of the problem. To support the growing load on grids, the mobility sector will also need to implement new software solutions for EV charging infrastructure management — a market ripe for innovative software solutions. 

Opportunities For Innovation 

• Smart charging & load management.  To ensure EV charging station profitability and avoid excessive loads on the grids, operators will require analytics-driven solutions for fleet charging coordination. For example, for larger commercial owners, you can offer features to create prioritized scheduling queues, which correspond to the planned vehicle usage time slots (e.g., to cover a morning delivery route of 300 km). Or, on the contrary, delay charging for off-duty vehicles to benefit from off-peak energy tariffs.

• Secure user authentication.  Consumers will expect a simple, seamless, and secure chatting experience. In connected EVs, the process can be effectively streamlined with the help of digital car IDs, NFC, and/or license plate recognition technologies. Depending on the planned route, the management module can also suggest the optimal charging schedule once the vehicle is docked. Seamless roaming between different EV charging networks, which can be enabled through partnership deals, is another advantageous feature. 

• Remote station diagnostics. EV charging issues can be critical for consumers, especially as “range anxiety” remains a major buying deterrent. To deliver a delightful driving experience, you should have real-time status data (on, off, maintenance) on all stations in your network and share that information with the onboard EV trip planner. Likewise, having remote diagnostic access to the vehicle battery, as well as the charging station, to obtain log messages is critical for issues troubleshooting. 

3. Mobility as a Service (MaaS)

Urban planners continue to make a big push for reducing private car ownership to achieve ambitious sustainability goals.  Finland’s capital, Helsinki, aims to make it unnecessary for any city resident to own a private car by 2025. Singapore started a 15-year plan in 2017 to reduce residents’ reliance on cars. 

Alongside investments in public transportation and bike lanes, many cities are also increasing their support for MaaS players — car-sharing, micro-mobility, and autonomous shuttle companies — to create seamless multimodal transportation experiences. 

MaaS platforms integrate multiple transportation options into one user-centric mobility offering, allowing customers to seamlessly switch between state and privately-operated modalities. Berlin’s Jelbi offers access to 12 different local options, including all public transit routes and ‘shared’ offerings like bike, car, and e-scooter rentals. Dutch umob does the same across the Netherlands and has an ambitious plan to expand to new markets in Europe. 

Overall, usage of MaaS applications has been growing steadily year-on-year and is set to reach $1 trillion globally by 2030.

Source: McKinsey 

New transportation modalities like autonomous shuttles can further accelerate the sector’s growth. By 2030, some 10,000 autonomous shuttles may be cruising through Hamburg, Germany. The city recently launched a consortium of six project partners: public transport operator HOCHBAHN, on-demand service provider MOIA, vehicle manufacturers HOLON and Volkswagen Commercial Vehicles (the latter backs MOIA service); as well as the Karlsruhe Institute of Technology (KIT) and the Hamburg Authority for Transport and Mobility Change (BVM) to work on this vision. 

Mobility-as-a-Service (MaaS) provider, Moovit, in turn, has partnered with May Mobility to accelerate the integration of autonomous vehicles (AVs) in public transport networks. May Mobility’s AV fleets will be integrated into Moovit’s urban mobility app and soon available for bookings in select locations. 

However, to continue expanding their user base, partnership ecosystem, and market presence, MaaS companies will have to also address some of the present-day inefficiencies in their platforms, primarily around user experience. 

Opportunities For Innovation 

• API Integrations and management. Interoperability remains a major stumbling block for companies, looking to enter the MaaS ecosystem. The lack of shared data standards between intelligent transportation systems (ITS), especially legacy ones, complicates integrations with public transit options. At the same time, integrations with different ecosystem partners — mobility services providers, mapping services, ticketing solutions, etc —  also require substantial integration expertise. A mature API strategy is required to ensure scalable and secure collaboration between different ecosystem participants and deliver more delightful user experiences such as integrated ticketing, real-time schedule updates, end-to-end journey planning, and more. 

• New monetization strategies, focused on end-user value. Most MaaS products have monetized their products through a combination of commission-based models from demand generation for mobility service providers, premium subscription features, and anonymized customer data sharing with partners. However, these revenue streams aren’t often enough to fund further growth. Many mature players are expanding into asset ownership and building our corporate B2B and B2C rental fleets. Others are entering adjacent industries, ranging from logistics to finance. Think about how else can you leverage your assets (physical or digital) to improve customer experience and access new revenue streams. 

4. Streamlined Payments 

Until recently different players in the transportation sector mostly relied on third-party payment processors to handle all transactions: fuel, toll, parking, and ticking payments. But, by doing so, many have missed the opportunity to capture extra transactional revenues and innovate the customer experience. 

Directly embedding payment solutions into your solutions provides multiple advantages: 

• Reduced transaction costs 
• Faster settlement times 
• Seamless customer experience 
• New revenue streams 

And, in case of the public transportation, greater passenger volumes. Visa survey found that 41% of passengers would use public transport more often if contactless payments were available. Another 47% also want more fare-capped rides. Contactless open-loop ticketing systems allow the implementation of these solutions. Card taps are faster and more convenient than cash fares or pre-paid card top-ups for residents and visitors alike.  Edvantis has helped develop a ticketing device and supporting payment software for Stockholm’s Lokaltrafik AB (SL) with such features, now used by over 800K passengers each month. 

Such systems can be also programmed to support subsidized fairs — for kids or the elderly. The Los Angeles Department of Transportation and LA County Metropolitan Transportation Authority designed a mobility wallet as part of its Low-Income Fare is Easy (LIFE) program. The wallet app provides subsidized access to a wide range of local mobility options. Moreover, mobility wallet solutions can also include special fares for passengers who are switching between private vehicles and public transport to incentivize trips by alternative means. 

In-car payments is another coming-of-age market with strong commercial potential. With increased vehicle connectivity, drivers and passengers can now complete more tasks on the go — order drive-thru, book a hotel stay, or purchase entertainment content. Not to mention the standard chores like paying for parking or toll roads. Thanks to in-built wallets and NFC technology, vehicles are becoming a new promising payment mechanism. 

For OEMs and other mobility ecosystem participants, in-car payments offer a new opportunity to both improve CX and capitalize on transactional revenues. BMW recently launched an in-car fuel payment system, which allows owners to settle the payment at select participating stations without exiting the car. Sheeva.AI, in turn, is helping Citroen roll out an in-car payment product to settle transactions at 2 million fuel pumps, parking spaces, electric vehicle (EV) chargers, and other service points. 

Pairpoint estimated that 40% of the total value opportunities, created by in-vehicle payments will come from EV charging or car fuelling. Road tolling, parking, and maintenance-related payments represent the rest of the market. 

Opportunities For Innovation 

• Smart parking. Mobility companies can drastically improve the parking experience by providing drivers with real-time information on nearby spot availability and rates. This information can be embedded straight into the vehicle navigation dashboard for convenience. This would require API-based integrations to gain real-time data from partnering organizations. Additionally, you can transform the payment experience by processing parking reservations and payments straight from the vehicle dashboard. 

• Intelligent tolling systems. Sensor-based tolling systems with contactless payments can help infrastructure providers minimize revenue leaks and OEMs — further elevate the payment experience. For example, ClearRoad tolling API provides full access to toll charge information across the US, which allows users to register multiple vehicles with different billing agencies from one interface. 

• POS financing.  Lastly, OEMs can also attract more buyers and retain a greater fraction of after-sales profits by offering more attractive financing solutions. Millennial drivers are increasingly interested in buy now, pay later (BNPL) offers for car repair, servicing, and MoT expenses. Citroen UK recently launched a BNPL offer to finance accessory purchases and repairs.  Moreover, three-quarters of car buyers are open to purchasing a new vehicle fully online and seek out online auto-financing options with convenient, app-enabled account management. Offering such products is another major digital transformation opportunity for the mobility sector. 

What’s Next? 

The mobility sector has already gone digital, bringing in new delightful consumer experiences in driving, payments, fueling, charging, and multi-modal journeys. But several important blockers remain. The ecosystem will need to grapple with complex challenges around data privacy and security. Establishing robust data governance frameworks and building robust security perimeters will be critical to win over consumer trust. 

Improvements in user experience are also necessary to accelerate the adoption of in-car subscription and payment services, as well as a greater focus on integrations with more mobility partners. Edvantis has been successfully helping transportation companies re-imagine their value proposition with new technologies — cloud computing, data analytics, and ML/AI. Contact us to learn more about our services.

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Healthcare leaders have also faced the ‘perfect storm’ of challenges — the global pandemic, mass staff shortages, patient and practitioner demographics shifts, and ongoing cost squeezes. In such an uncertain climate, it may be hard to determine which areas deserve further attention (and financing) as the ROI of novel technologies isn’t always evident.

This post highlights the technology areas where digital transformation in healthcare will have the most impact in the next two to five years.

Four Technology Themes Shaping the Next Decade of Healthcare

As healthcare systems face staffing shortages, rising costs, and growing patient demands, leaders are increasingly turning to innovative technologies to improve service accessibility, diagnostic accuracy, and overall patient engagement. 

Based on our analysis, four main technology themes will shape the future of healthcare: greater adoption of clinical decision support systems, remote patient solutions, increased focus on senior care technologies, and greater integration of AI into workflows.

1. Clinical Decision Support Systems 

Clinical staff is at a tipping point: 53% of physicians report burnout. Emergency medicine (65%) and internal medicine (60%) specialists are even more stressed.

Prolonged stress and burnout drive the intention to quit, which has hardly abated after the pandemic. The World Health Organization (WHO) warned that European and Central Asian healthcare systems face a “ticking time bomb” — a combination of low pay, long hours, inadequate support, and serious staff shortages that can paralyze local healthcare systems unless diffused.

In the US, similar issues persist. Mercer estimates a shortage of over 100,000 healthcare workers by 2028. In Canada, the healthcare sector already had 96,200 vacant medical positions as of Q4 2022.

While healthcare staff shortages are a complex problem requiring multi-level intervention, one aspect can be swiftly addressed with technologies: physicians’ day-to-day well-being and productivity. In Europe, the ‘physician tools’ startup segment is worth over $25 billion, according to Dealroom. In 2022, these companies raised an extra $1.2 billion, despite the general dip in investments.

Source: Dealroom

Clinical decision support systems (CDSS) are a particularly promising market, expected to be worth $3.76 billion by 2031.

A CDSS is a collection of digital tools for streamlined information lookup, summarization, and analysis for diagnostic support. Advanced systems, powered by big data analytics, may include features like automatic cross-check of drug-disease interactions, individualized dosing support, or symptoms cross-validation across available order sets.

Epic Systems allows clinicians to use real-world data about similar patients for smarter care decisions. The Best Care tool compares the patient’s symptoms against the internal research database to identify similar cases. It offers contextual recommendations on effective treatments and connects the physician with a peer for a quick consult.

French MyPL, in turn, provides clinically validated patient information to oncology professionals. The web app offers access to the latest patient insights through convenient profiles, securely aggregated from EHR systems, PDF reports, and clinical notes. Each profile is compiled and validated by the company’s proprietary machine learning algorithms. Decision support is just one of the many viable use cases of big data analytics in healthcare. 

Clinical decision support systems can also eliminate redundant steps and inefficiencies in healthcare delivery processes. Epic software helped several infusion centers optimize patient scheduling based on historical data and predictive insights, recommending the least busy days. Participating institutions managed to improve patient volume by 20%, increase gross income by 15%, and reduce wait times by 41%, according to a case study. 

According to the American Medical Association, an average physician uses 3.8 digital tools. Adoption is increasing as three-quarters believe digital technologies can reduce stress and burnout.

2. Expansion of Remote Healthcare Ecosystem 

Short-staffed healthcare practices have long waiting times for new patients, especially in countries with state-subsidized care. Other locations face increasing emergency admissions, prolonged stays due to complications, and a growing burden of chronic disease patients. Respectively, the adoption of remote patient management (RPM) solutions and telehealthcare systems ranks high on the leaders’ agenda.

Remote solutions for chronic disease management are in focus. Patients with chronic conditions struggle to receive timely care due to long waiting times and inertia to change. Digital technologies can help physicians better engage with chronic patients through timely remote interventions and digital therapeutics (DX) tools.

Fitterfly has created a diabetes-targeting digital program. The app compiles data from lab tests and the patient’s continuous glucose monitor (CGM) to provide tailored advice on diet, exercise, sleep, and stress management. Users can receive coaching or share their information with healthcare providers. Results from clinical trials show that after 90 days, 46.9% of participants reduced HbA1c levels by over 1%, and 38.5% achieved a weight loss of over 4%.

Other institutions are investing in digital front-door solutions — tools that ease access to healthcare services like online booking platforms, virtual queues, and on-demand telemedicine. These solutions can ensure a more even distribution of patients and workloads among professionals, leading to lower wait times and better overall access to healthcare.

Australia launched a national eHealth NSW platform, offering a unified online entry point to various digital health services. These include a Patient Reported Measures (PRMs) tool for sharing health outcomes and care experiences; a HOPE platform, and a Child Health Reminders (CHR) service for timely vaccinations.

Southern Cross Healthcare in New Zealand used a digital front door platform to improve the patient pre-admission process. Adopted in the Auckland Surgical Centre unit, the system allows 90% of patients to complete all necessary pre-admission and admission forms before the procedure. Clinical staff benefits from an 83% reduction in booking processing time per patient. 

The trend we’re seeing is a progressive expansion of virtual hospital operations. Healthcare institutions further into their digital transformation journey are building fully digital units, offering on-demand online care and faster coordination to obtain physical admissions. Ksyos operates a national digital hospital in the Netherlands. Staffed with 7,500 general practitioners and 4,500 medical specialists and paramedics, the institution offers high-quality medical care without waiting times, digitally where possible, and physically where necessary. 

Seha Virtual Hospital in Saudi Arabia offers 15 specialized health services, including psychiatry, kidney disease, diabetes, medical rehab, and genetic and geriatric diseases. It also provides virtual rays, e-pharmacy, and at-home care services. Digital at its core, Seha Hospital leverages smart automation to triage patients and prioritize urgent cases. Teams also use AI algorithms for image analysis and augmented reality for knowledge transfer. During surgeries, doctors can request instant peer advice via an AR platform. IoT-powered remote monitoring systems aid in follow-up care.

Greater adoption of remote patient care solutions reduced long waiting times and accelerated treatment start, leading to higher patient engagement and satisfaction. For healthcare practitioners, these systems reduce administrative toil and cost waste.

3. Data-Driven Senior Care Solutions

Rapid population aging is straining national healthcare systems.

OECD countries spend 1.5% of their gross domestic product on long-term care services like medical, nursing, personal, and assistance care. In Scandinavia and the Netherlands, spending is as high as 3.5% of GDP.

In the US, people over 55 account for 56% of total health spending, despite being only 30% of the population. Digital transformation in this area holds great promise for extending life, improving quality of life, and reducing the economic burdens of supporting senior citizens. 

CarePredict launched a specialized wearable device for elderly care facilities. Wrist-worn, Tempo uses kinematic algorithms to process data and detect activities like eating, sleeping, and moving. It helps remotely ensure the elder’s healthy lifestyle without 24/7 camera monitoring. The platform also provides caretakers with biomarkers predictive of health declines like falls, UTIs, depression, or nutrition deficits.

CareZone, in turn, built an intuitive medication management app for senior citizens and their caregivers. The app allows users to create a comprehensive list of medications, set reminders, track history, and order refills in one click. Users can also scan the bottles to share prescriptions with doctors for faster refills.

Digital therapeutics programs for senior mental support have gained traction, showing promising outcomes in reducing cognitive decline and mental illness symptoms. Mynd developed a VR platform for engaging seniors in therapies that foster physical, cognitive, and mental wellness. Adopted at 15 skilled nursing facilities in Indiana, Mynd program had an 88% success in reducing feelings of isolation and increased socialization by 88% among participants.

Digital technologies enable caregivers to focus on the most important aspects of care and communication. With real-time data collection, centralized access, and robust analytics, they can continuously monitor patients’ well-being, ensure timely interventions, and optimize workloads.

4. Integration of AI into Healthcare Workflows 

Machine learning (ML) and deep learning (DL), known as artificial intelligence (AI), are hot topics in healthcare. Skeptics worry about limited human interaction in the doctor-patient relationship, patient data privacy, and potential biases in automated systems.

Indeed, ML and DL systems cannot (and should not) fully replace human judgment. However, they are providing a massive aid for diagnostic support. Specialized medical image analysis models with AI have proven their capability for early disease detection, more accurate diagnoses, and streamlined surgical planning.

DeepMind AI for Radiography, which automatically detects and segments anatomical structures in chest X-rays, delivered 25% fewer false positives in a large mammography dataset, compared with commonly used clinical workflows – without missing any true positives. 

AIR Recon DL — an MR image reconstruction solution from the GE Healthcare Edison platform — has benefited over two million patients globally. It increases MR image sharpness for a more confident diagnosis and reduces exam times by up to 50%.

Likewise, AI systems have excelled in health risk prediction and prevention, using patient data for providers to deliver appropriate interventions.

Mass General Hospital in the US has deployed AI models to predict surgical risk and determine the best interventions for high-risk patients. For example, TOP is an AI-powered app for predicting outcomes for trauma and emergency surgery patients. It’s instantly accessible, saving healthcare personnel time in decision-making.

Klarity, a HealthTech startup, built advanced models for predicting premature morbidity and chronic disease. Trained on a dataset of 634 million, the models use AI and expert-consensus-driven algorithms to deliver maximum outcome certainty. It can adapt to predict any medical risk by processing self-reported and wearable device data.

Beyond treatment support, AI systems can significantly aid with admin work, which takes up over 15% of physicians’ time on average. Generative AI, in particular, can streamline note-keeping, transcription, and recommendation-generation tasks.

A recent study found that 45% of medical summaries generated by LLMs were equivalent and 36% were superior to those from healthcare professionals. Additionally, the large language model summaries scored higher on conciseness, correctness, and completeness than the expert summaries.

In the US, companies like Abridge, Suki, and Microsoft’s Nuance are already successfully deploying gen AI for medical record management. Abridge automatically transforms patient conversations into digital notes with high accuracy. After adoption, Reid Health, achieved an 87% decrease in patient call turnaround time and a 60% decrease in after-hours documentation. 

Suki offers more than note generation. It also does dictation, recommends medical codes, and incorporates live data, like patient vitals, from the EHR. According to the company, its assistant helps clinicians complete notes 72% faster and decreases amended encounter rates by 48%.

Overall, 83% of healthcare professionals believe that AI can eventually reduce many of the problems facing healthcare, especially in the areas of workflow efficiency, administration, and document management. 

However, to profit from AI capabilities (and other emerging technologies), healthcare organizations will first need to resolve some of the remaining technical blockers. 

How Leaders Approach Digital Transformations in Healthcare 

Although digital transformation ambitions run high, progress often remains slow. Healthcare leaders cite “legacy systems”, “budget limitations”, “poor data quality”, and “inability to recruit top tech talent” as the main barriers. 

The optimal approach to solving these challenges is partnerships with technology companies to develop or license new technologies. 

Over 34 collaborations between Big Tech companies (Alphabet, Amazon, Microsoft, IBM) and healthcare institutions have been established as of 2023. In many cases, these include joint projects in adopting the company’s cloud services. Although some also have a synergistic relationship. For example, the Moorfields Eye Hospital in the UK, for example, provided DeepMind Health (part of Alphabet) a dataset of retina scans for training an AI model. In exchange, Moorfields can use DeepMind’s trained AI model to identify potentially blinding eye diseases 

At the same time, many companies are also hiring software development partners to assist in digital transformation projects. According to Black Book Market Research, 93% of hospital leaders are employing more third-party vendors to implement digital technologies. manage IT infrastructure, maintain help desk functions, and handle big data management.

Strategic partnerships with software development companies can reallocate more resources towards patient-centric priorities, rather than IT function management. In addition, partnerships allow to circumnavigate ongoing technical staffing challenges and accelerate time-to-market for new initiatives. 

For the last decade, Edvanits has been supporting healthcare businesses in their digital transformation efforts. We’ve helped launch a new telemedicine platform, now serving over a million patients, several remote prescription tools, and a range of advanced big data analytics solutions for healthcare. Contact us to learn more about our software engineering services.

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Edvantis joined 10 other Ukrainian tech innovators with a stand at the Ukrainian Pavilion, which was organized in collaboration with Unite Ukraine, the Office for Entrepreneurship and Export Development, Diia.Business, and the NAZOVNI international platform, with the support of the Ukrainian embassy in Berlin. The Ukrainian pavilion attracted significant attention, receiving visits from notable representatives, including a delegation from the European Commission, Ambassador of Ukraine to Germany Oleksii Makeiev, and executives from Deutsche Bahn AG. 

The event took place in Berlin from September 24th to the 27th, bringing together over 137,400 attendees from 137 countries.  

At the event, Edvantis showcased its cutting-edge payment and ticketing services designed to transform public transportation systems, with a focus on optimizing operations and enhancing passenger experience. Edvantis presented its achievements in software development, data science, and ticketing applications, emphasizing how these technologies contribute to more sustainable, efficient, and smarter urban mobility. 

During the event, George, General Manager at Edvantis GmbH, participated in several productive meetings with potential partners and clients, exploring collaboration opportunities in the public transportation sector and discussing Edvantis’ expertise in transit ticketing and payment services. 

“We were thrilled to connect with so many industry professionals at the InnoTrans 2024,” said George. “Our services are designed to help transportation companies make data-driven decisions, thereby improving their efficiency, generating more revenue and creating smarter public transportation systems for everyone.” 

Throughout the InnoTrans 2024, Edvantis engaged in meetings with importers, distributors, transportation operators, and potential customers, discussing how its technologies can streamline operations and improve service reliability. The company presented its successful projects in payment solutions for public transport, which sparked strong interest among numerous attendees. 

About Edvantis

Edvantis is a global IT service provider, delivering tailor-made digital transformation solutions to clients across various industries, including public transportation, healthcare, real estate, software & hi-tech, fintech, transportation & logistics, and public sector. With offices in Germany, Poland, and the United States, and development centers across Eastern Europe, Edvantis is committed to driving business success through innovation for customers worldwide. 

About InnoTrans 2024 

InnoTrans is the world’s leading international trade fair for transport technology, held every two years in Berlin. It serves as a global platform for transportation professionals to showcase innovations, exchange knowledge, and explore the future of public transport and urban mobility solutions. 

The post Edvantis Represents Ukraine at InnoTrans 2024, Largest Transport Technology Fair  appeared first on Edvantis.

To accelerate the transition from traditional energy production to digital and sustainable energy generation, business leaders need to invest in new technologies: cloud computing, predictive analytics, digital twins, and IoT among other sectors. Learn how digital transformation in the energy sector takes shape from this post. 

6 Digital Transformation Opportunities in the Energy Sector 

Between now and 2030, electricity consumption in the EU is expected to increase by 60%. Given the current progress, the U.S. Energy Information Administration (EIA) is concerned that the global energy consumption increases may outpace efficiency gains and drive continued emissions growth by 2050. 

These factors will likely translate to extra regulatory pressure on the energy companies to increase the production of renewable energy and emission offsetting. To cope with the task, leaders are making investments in the following digital technologies. 

1. Data-Driven Distributed Grid Management

Since 2015 grid-related investment in digital technologies has grown by over 50%, according to IEA. In 2023, it reached 19% of the total grid investment, with the distribution segment commanding 75% of the total digital spending. By modernizing the distribution segment through the integration of smart technologies, leaders can achieve better demand management and grid stability.

Distributed energy systems (DES) offer several advantages over centralized energy systems. They are more versatile and can employ a wide range of energy resources and technologies to be grid-connected or off-grid. In fact, most of the off-grid DESs are renewables-based, proving more economically competitive in remote communities than conventional energy systems. Grid-connected solutions, in turn, can supply the extra capacities during peak demand flexibly and sustainably.

Source: NREL 

Although DES can dramatically help with supply and distribution, it also poses new challenges for energy companies. Because DER can generate, store, or flexibly draw or discharge power to the grid, the demand is becoming increasingly variable, impeding grid balancing. Likewise, renewable energy generation levels can be unpredictable too due to weather, which complicates capacity planning. 

Investment in digital technologies like cloud, IoT, and big data analytics, can help businesses improve DES management through the following capabilities: 

• Forecast management. A data analytics solution can be placed between the network management systems (which provide real-time controls), and planning suites (which help prepare for long-term changes to the network). Forecast analytics systems will allow us to better estimate short-term peaks in demand and model the necessary grid transformation to account for future demand growth. 

• Demand-side management. Thanks to the wider adoption of IoT (smart meters, remote monitoring systems, condition monitoring solutions, smart inverters, etc), power companies gain greater visibility into demand and supply. This allows more granular controls. For example, water heaters can be switched on when photovoltaic energy production increases the voltage of the LV networks in residential areas.

• Battery charging infrastructure management.  As more electric vehicles (EVs) hit the road, new solutions are required for managing various battery-charging methods (fast, slow, or scheduled). Leaders should also plan for managing battery-based storage, which could play an important role in the networks as capacities increase. 

EDF International Networks, for example, is working together with Capgemini on accelerating global deployments of smart grids —  grids that use digital technologies for real-time monitoring and optimized distribution. On the home turf in France, EDF helped RTE and Enedis, France’s TSO and DSO, integrate a number of smart grid solutions into their day-to-day network management process. According to the global Smart Grid Index, Enedis was ranked first for the second year in a row in 2023.

Abroad EDF has been involved in launching a microgrid demonstrator in Singapore and smart grid deployment in Uruguay. The current state of technologies already makes advanced network automation possible, so leaders must act on the available opportunities.

2. AI-Powered Energy Forecasting 

Smart grids generate massive quantities of data, which contain insights into better management. The global wind turbines produce over 400 billion data points per year. To transform the incoming firehouse of data into operational insights, energy sector leaders are turning to machine learning (ML) and artificial intelligence (AI). 

According to Gartner, 92% of power and utility companies plan to incorporate AI and ML into their operations by 2026. 

Source: Gartner

One area where AI is already making tangible impacts is supply and demand forecasting. Hydro-Québec (HQ) in Canada operates some 60 hydroelectric generating stations, supplying the region with green energy. Short-term load demand forecasting (a few hours to a few days horizon) is critical to ensure proper generation management, reliability, and maintenance of the power grid. 

Since 2018, HQ has developed a range of AI-based load demand forecasting models. The suite of AI tools allows the company to produce load forecasting models from both a macroscopic (also referred to as “top-down”) and microscopic (known as “bottom-up”) point of view. Macroscopic data uses aggregated consumption data from power meters in the province. Microscopic data comes from millions of smart meters. 

Using the system, HQ can evaluate the impacts of load, weather, market changes, and other factors to build accurate forecasts, perform congestion analysis, schedule maintenance, and ensure adequate power generation and transmission at all times. 

Google DeepMind, in turn, created an AI model to improve the accuracy of forecasts for its 700 MW renewable fleet of wind turbines. Using historical data, the team trained a neural network to predict future energy outputs 36 hours in advance. This way, Google can sell its spare power ahead of time, rather than in real time. By leveraging AI predictions, it has increased the financial value of its wind power by 20%. Enticed by the results, Engie partnered with Google to optimize its wind portfolio using AI.

AI-provided insights also allow energy companies to implement better tariffs, benefiting the consumers. Swiss ABB launched an AI-enabled energy demand forecasting application for commercial building managers that allows them to optimize usage in real time to avoid peak charges and benefit from time-of-use tariffs.

Generative AI is also making inroads in the industry. Researchers from MIT Lincoln Laboratory are testing a gen AI application for smart grid modeling. Using a HILLTOP+ microgrid simulation platform, the team plans to test new smart grid technologies (hardware and software) in a virtual environment first to better understand the system scalability and interoperability before physical deployment.

3. Predictive Maintenance 

With consolidated, digital data from a combination of grid assets, energy companies can enable predictive maintenance scenarios. 

Using data analysis and machine learning, predictive maintenance systems identify potential equipment failures before they occur using real-time conditioning data. With greater visibility into the asset condition, operators can schedule just-in-time interventions, ensuring that maintenance is carried out when necessary. Such an approach helps maximize the equipment’s lifespan and performance, as well as minimize downtime and maintenance costs. 

Italian Enel S.p.A. relies on several predictive maintenance systems across its portfolio of hydroelectric, thermoelectric, nuclear, geothermal, wind-power, and photovoltaic power stations. At a thermoelectric plant in Brindisi, the company rolled out predictive maintenance systems on some 450 critical machines. This has reduced incidents by up to 90% in some applications, preventing a downtime of $366,000 in one case. 

Siemens went a step further and recently added gen AI capabilities to its Senseye Predictive Maintenance platform. The platform already incorporates AI and ML algorithms to auto-detect equipment performance issues and alert users. With new Gen AI features, technicians can also request further information from the system using text commands to get extra clarifications and cross-correlate data. The interactive dialogue will speed up and streamline the decision-making process.

4. Digital Twins

Digital twins are data-based, digital replicas of real-world assets. They can represent a solar farm or a hydrogen manufacturing facility, visualizing all the processes and their outputs in the facility. 

Effectively, a digital twin aggregates and transforms the available structured (database entries, reports, real-time machinery data streams) and unstructured data (print documents, images, audio, etc) into a real-time model of the asset or facility. Real-time visibility can help operators diagnose potential issues earlier, optimize operational efficiencies, and evaluate new operating scenarios. 

E.ON Group, for example, developed a digital twin platform to aggregate and visualize data from its portfolio of power transformers. Since equipment originated from different manufacturers and distribution grids were managed by different regional operators, asset condition data from the transformers was not centrally available for analysis.

The new platform helps teams visualize the location of all transformers, and review individual set characteristics and time-series data from a web dashboard. Furthermore, users can easily evaluate failure modes per asset type using assessment functions to determine the remaining life of the equipment and prioritize capital investments. 

German Next Kraftwerke went a step further and virtualized its end-to-end operation. The company has combined data from over 10,000 assets into one of Europe’s largest virtual power plants (VPP) installations. Using the Next Kraftwerke platform, users can obtain accurate supply and demand forecasts to better balance renewable power sources and participate in trades. 

Tokyo Electric Power Company (TEPCO), in turn, launched a virtual power plant project in 2019. Its VPP aggregates data from solar, PV, and battery-based sources and validates it against the demand response for optimized energy supply and demand. The project has helped TEPCO improve grid stability and integrate DES sources into the grid.

5. Electric Vehicle Charging Infrastructure

Some 40 million EVs are already on the roads and by 2030, the global EV stock will increase to 350 million vehicles, according to IEA. Electric fleet growth calls for better EV charging infrastructure, which is currently in short supply. The US alone will need over one million extra public EV chargers by 2030 to support federal EV adoption targets. 

Energy companies can capitalize on the demand for charging infrastructure by moving from being just energy distributors to service suppliers. For example, they can provide public, overnight charging. TotalEnergies already does this in the Netherlands, supplying 100% renewable, locally-generated power to consumers.  

EDF and Nissan launched a new commercial vehicle-to-grid (V2G) service for corporate EV fleets in the UK. The service supports two-way energy flows, recharging the EV battery when the tariffs are the lowest and discharging excess energy to sell back into the grid. Oncor Electric Delivery and Toyota Motor North America are also collaborating on a V2G pilot in the region. 

Effectively, energy players can access new revenue streams by collaborating with the automotive sector on EV charging solutions to further incentivize adoption. After all, EVs can become a new element of the distributed energy systems, participating both in demand and supply.  Enel partnered with Nissan in Denmark to promote energy trading among EV owners. When using the ‘smart charging’ feature on its infrastructure, fleet owners will receive rebates for assisting the grid.

6. Peer to Peer Trading 

As more grinds become decentralized, we’re seeing a new wave of prosumers – individuals who both consume and produce energy. These may include EV owners, as well as households with solar panel installations or businesses, operating wind farmers. All of them can generate, store, and trade produce energy in a decentralized fashion using a peer-to-peer (P2P) marketplace. P2P electricity trading schemes are already underway in  Australia, Colombia, Germany, Japan, Malaysia, the Netherlands, the UK, and the US. 

P2P energy trading platforms like PowerLedger, Exnaton, and ENTRNCE take the traditional role of an energy retailer by managing the price and volume of buy-sell transactions. Supported trade scenarios include over-the-grid trading, when the prosumer is connected to the central grid, but manages price and volume risk independently through direct peer trades. The Vandebron platform allows consumers to purchase power directly from prosumers who set the price. It balances the wholesale markets by connecting consumers, prosumers, and generators, plus provides suppliers with generation forecasts for their assets. 

In a partly independent microgrid scenario, the prosumer creates a microgrid that manages some of the aggregate energy needs but remains connected to the central grid for the remainder. One example of this setup is the Schoonschip Amsterdam microgrid. In this community-led project, the surplus electricity is traded among its members, but the community remains grid-connected for redundancy. 

The Port of Rotterdam also ran a similar experiment. During the pilot, commercial consumers could trade renewable energy via the decentralized platform with the help of an AI-enabled energy trading agent, which suggested procurement strategies, based on their needs. The results were rather impressive: consumers reduced costs by 11%, while producers gained a 14% increase in revenue. Additionally, during the trial 92% of solar-generated energy was consumed, overcoming historic losses. 

The third possible trading scenario is a fully independent microgrid — a privately operated, self-sufficient network, not connected to any central electricity network, which supports intra-participant P2P trades. The Brooklyn Microgrid project is working towards this vision. Led by LO3 Energy with support from Siemens, the team is working on an independent community energy market. The microgrid combines network control systems, switchgear, innovative battery solutions, and smart electric meters, which help with load balancing and demand forecasting. Participants can buy and sell energy using smart contracts based on blockchain. 

For energy companies, P2P trading presents yet another avenue to incentivize decentralized production, generate a profit on trading transactions, and improve supply to underserved communities.

Conclusion

The energy market is on the brink of major transformations. Decentralized energy production and P2P trading will grow in volumes, incentivized by government support. EV fleet owners, in turn, will further expand the DES market as V2G technology becomes more widely deployed. By choosing the path of collaboration, energy companies can accelerate their transition to more sustainable energy production and share some of the capital expenditures on infrastructure.  

Investments in digital technologies, in turn, will help effectively integrate DES distribution into existing network flows to better forecast demand, optimize production volumes, and optimize prices. Edvantis team would be delighted to further advise you on how to approach digital transformations. Contact us for a consultation. 

The post Digital Transformation in the Energy Sector: Emerging Scenarios appeared first on Edvantis.

Two years forward, the market is full to the brink with options. Microsoft launched Microsoft Copilot. Amazon — Q Developer, and Google came slightly late to the party with Gemini AI Coding Assistant. Beyond Big Tech, startups are also releasing gen AI coding tools, based on fine-tuned versions of open-source large language models (LLMs).  

The generative AI coding market is booming and set to grow from $18.4 Million to $95.5 million by 2030, despite the dual user sentiment. Some Software Developers became disillusioned with the depth and accuracy of Gen AI coding tools. Other teams continue to integrate the technology into different stages of their software development lifecycle, citing improvements in code quality and development productivity.  

So how useful are generative AI tools for software engineering? Do the hyped benefits pan out in complex projects? We discuss all the potentials and limitations of AI-assisted software engineering in our blog post.

AI Coding Assistants: 6 Feasible Use Cases 

Three-quarters of Software Developers plan to or already use AI coding assistants, according to a StackOverflow community survey. By large slide ChatGPT and GitHub Copilot are in the lead. 

Source: Stack Overflow

Enterprises too are on board the Gen AI trend. Gartner says two-thirds are either in the pilot or deployment stages with AI coding tools as of April 2024. The company also predicts that 75% of Software Engineers will use AI coding assistants by 2028 — a big boost from less than 10% of enterprises in 2023. 

Why the rush? Studies by Microsoft, GitHub, and MIT all point towards substantial productivity gains in software engineering with AI assistants, mostly by automating tedious, low-value work. And they can streamline repetitive actions across the entire software development lifecycle (SDLC) — from requirements analysis to coding, testing, deployment, and document generation. In addition, some Gen AI tools also offer help in other areas like IT operations, cybersecurity, and data management. 

But not everything about AI coding assistants is hunky-dory. There are many advantages, but also some clear limits to how well they perform for popular use cases. 

Code Generation and Reviews 

Gen AI assistants shine when it comes to repetitive, standardized coding. They’re great at catching formatting inconsistencies, wonky coding lines, and minor bugs. Think of it as typing a document with and without a spellchecker. 

Jonathan Burket, a Senior Engineering Manager at Duolingo, one of the early GitHub Copilot adopters, shared that the tool is “very, very effective for boilerplate code” generation. It tab-completes basic classes and functions across programming languages. 

According to Burket, Software Developers working with a new repository or framework got a 25% speed boost, and those familiar with the codebase did tasks about 10% faster.  GitHub Copilot also helped Duolingo reduce mean code review times by 67% thanks to contextual suggestions. 

But there are also some limitations to keep in mind. As Birgitta Böckeler observed, AI coding tools sometimes provide useful in-line suggestions, but in other cases — the tips can be misleading. 

The suggestion accuracy varies a lot across tech stack and programming languages. Most LLMs were trained on a large public corpus of knowledge and hence more exposed sample data for popular languages like Java or Python, than something more niche like Lysp or Haskel. 

Novice Software Developers may also take all suggestions at face value without cross-checking the data, especially for larger code suggestions, increasing the risks of bugs or introducing unnecessary code redundancies.

Code Refactoring 

Code refactoring is another painstaking software engineering task where gen AI assistants can add value. They can provide suggestions, based on the company’s coding standards and available documentation, to promote uniformity. Automatic documentation and comments generation also help achieve greater code maintainability.  All of this minimizes technical debt accumulation due to idiosyncrasies and structural issues in different sections of the codebase. 

McKinsey’s study found that Gen AI coding tools help complete refactoring tasks 20-30% faster, which is something many teams dealing with legacy system modernization would want to strive for. 

AI coding assistant Metabob goes a notch further and helps identify more complex problems in system architectures, including concurrency issues, memory inefficient processes, and some 150+ other categories of problems.  The graph-attention networks and generative AI, trained on millions of bug fixes, supplied by experienced Software Engineers. 

Source: Metabob

Another great use case of AI coding assistants is code conversion between languages, often required as part of re-platforming an application or when building integrations with newer systems. 

But there’s always a but. A Coding on Copilot whitepaper from GitClear found that AI assistants may be a co-opt for “bad behavior” like mindless suggestions copy-pasting and introduction of unnecessary redundancies. “Instead of refactoring and working to DRY (‘Don’t Repeat Yourself’) code, these Assistants offer a one-keystroke temptation to repeat existing code,” the paper concluded. 

CodeScene team also found that popular LLMs weren’t that great for refactoring. According to Adam Tornhill, founder and CTO, AI failed to improve code 30% of the time. Worse, its suggestions actually broke unit tests about two-thirds of the time. Effectively, AI assistants were changing the external behavior of the code in subtle, but critical ways, instead of refactoring it. Again, this creates issues for junior talent, who may not yet have the skills to catch those problems. 

Test Case Generation

Nearly half of the QA teams spend at least nine hours writing one test case for a complex scenario (think complex product logic, multiple integrations, etc). Gen AI can help bring extra automation into the QA processes to enable faster and more thorough software testing. 

For example, Katalon offers a GPT-powered co-authoring platform for test creation. The platform also helps analyze TestOPs data to improve coverage rates and test quality.  OpenText’s UFT One embedded machine learning advanced optical character recognition to streamline functional test creation time and maintenance. 

Fine-tuned open-source LLMs also performed well in several test generation scenarios, in a study done by Umea University. 

Source: Umea University

AI systems substantially reduced the time for test case generation. However, only 66.67% of the AI-generated test cases were practically useful (though they may still have contained minor noise). 

Generally, AI can create accurate unit tests for a well-maintained codebase. But it is hardly useful in test-driven development (TDD) i.e., when the team first creates unit tests and then produces code, capable of passing it. 

Data Management Tasks 

The flip side of big data analytics is the growing volume of data management tasks on technical teams’ platters. Data science teams can spend a good chunk of productive time on data cleansing and organization tasks like metadata management, data lineage tracking, dataset pre-cleansing, and image annotation among others. 

Gen AI slots nicely into those workflows and allows teams to automate:

• Metadata label generation, using the corporate taxonomy for naming conventions
• Lineage information annotation to maintain auditable data trails across the organization 
• Data cleansing tasks like automotive “noise” removal of corrupt, incomplete, or poor-quality assets
• Data quality management tasks like records deduplication or data format, type, and values standardization 

Informatica recently presented a new gen AI tool for streamlining data management tasks on its cloud data management platform. 

CLAIRE GPT helps business users discover the necessary data using natural language commands instead of SQL queries. For the infrastructure teams,  benefits include the automatic generation of the first draft of the data pipeline to transform data within the same source instances. 

However, to get the most value from Gen AI tools for data management, prospective adopters must already have strong data management processes and supporting infrastructure. This includes scalable data platforms, proper data governance, and existing pipelines for data pipelines for data cleansing, transformation, and uploads. Without these foundations in place, Gen AI data management tools will be of little value. 

Security Scans and Threat Investigation 

Beyond engineering, Gen AI assistants also help cybersecurity teams maintain better watch over the corporate perimeter. Integrated with popular security platforms, such as tools help alert prioritization, case investigation, and security policy management among other tasks. 

Nokia recently released an embedded GenAI assistant for its NetGuard Cybersecurity Dome for telecom security. 

Trained on insights from 5G Network architecture, 5G Security practices, and Nokia’s telco domain expertise, the tool can provide accurate information on recommended network architecture configurations, optimal topology, and protection mechanisms against common adversity attacks. The GenAI assistant is expected to reduce the time it takes to identify and resolve a threat by up to 50%. 

Cylance Assistant from BlackBerry, in turn, provides a conversational interface for the company’s predictive cybersecurity AI model, Cylance AI, enabling Security Analysts to query security intel using text-based commands. 

The wrinkle, however, is that adoption of Generative AI tools often creates new security concerns. A SNYK survey found that security professionals are 3X more likely than C-suite members and significantly more likely than Software Developers to state that AI-generated code was “bad”. This is likely because security teams are often forced to deal with the “aftermath” of poor Gen-AI code, which creates new vulnerabilities and system errors. 

Whether you plan to adopt gen AI for coding or other types of tasks, you’ll have to stay vigilant about their security (and the security of the outputs they produce). 

Getting the Value from AI Coding Assistants 

When it comes to gen AI coding assistance, leaders may need to pack the excitement in favor of long-term strategy. You need first to clearly articulate the problem you’re trying to solve and it should be more precise than just “increasing software development productivity”. 

Think about the issues you want to address. For example, “lack of capabilities for unit test case generation” or “delayed deployments due to long peer code review times”. Set specific KPIs to measure the adoption impacts. These can be proxy metrics like “percentage of automated unit test coverage” or “mean peer code review time”. Then evaluate if Gen AI is indeed the best solution — it may or may not be. 

Lastly, when evaluating the ROI of Gen AI coding tools adoption, you should also factor in the extra training your teams may need to understand how to best profit from the new system. Senior staffers may be better at figuring out the optimal use cases, whereas junior staff may feel initially empowered to complete more work, but might then get disillusioned with the feedback during peer reviews about the code quality. Out best advice — start with several scoped pilots, analyze the results, and then work out the optimal strategy for progressive technology roll-out.

The post Generative AI for Software Engineering: Use Cases and Limitations  appeared first on Edvantis.

The software development community has experienced similar transitions before. In the previous decade, object-oriented programming and the World Wide Web emerged. The early 2000s ushered in the Agile manifesto. Finally, closer to the 2010s, DevOps and cloud computing gradually slid into typical development best practices. 

With each of these innovations came talks about opportunities, warnings about risks, test and trial processes, and then eventually acceptance. Same with Gen AI for software engineering – we have yet to experiment with it before issuing an industry-wide adoption. Large enterprises do just that, with 27% of them carrying out pilot projects. 

Let’s immerse in their early findings to find out what AI-assisted software engineering is, how it can benefit your development workflows, and which barriers you need to account for. This way, you’ll have a better understanding of whether you’re ready to adopt AI for software development yourself.

What is AI-Assisted Software Development? 

AI-augmented software development refers to the use of AI technologies (most often large language models) to accelerate and automate the process of writing, modifying, testing, and deploying code. Essentially, this practice is meant to minimize repetitive tasks and let the whole development team tackle more value-added activities.

AI-augmented software development employs a variety of tools: 

• Ready-to-launch AI coding assistants: commercial tools based on pre-trained large language models (LLMs) that can generate, autocomplete, and explain your code. The top 5 most popular picks in this category are GitHub Copilot, Visual Studio IntelliCode, Codeium, Gemini, and Tabnine. Most of these tools are cloud-based and are available either for free or on a subscription basis. Codeium also offers an AI coding assistant that can run on-premises or in your virtual private cloud.
• Custom-trained models: private, fine-tuned LLMs that companies deploy on their servers or private clouds and train with their own datasets (proprietary code, internal documentation, etc.). The most popular large language models used for such customization are OpenAI’s GPT-4, BERT (specifically its extension CodeBERT), and Llama 3. 

How Does AI-Assisted Software Development Work?

First and foremost, Software Developers need to select a use case where AI can actually add value. Currently, AI coding assistants handle simple tasks better than challenging ones. In a recent Stack Overflow survey, almost 28% of Developers indicated that AI was incapable of handling more complex problems, and it was a significant barrier to adoption. 

Having selected a task, Software Engineers can use AI code assistants to perform the following actions:

• Type the code and wait for AI to autocomplete it based on the surrounding code.
• Explicitly ask AI to perform a certain task (explain/improve/write code). Follow prompt engineering best practices and specify context and instructions for better results. 
• Highlight the code snippet and ask an AI code assistant to fix or improve it.
• Receive code improvement recommendations and fix suggestions in real time without even writing prompts.
• Chat with the AI coding assistants to receive answers about your codebase or project documentation.
• Research the problem without leaving the integrated development environment (IDE).

Working with an AI coding assistant is an iterative process. At times, the suggested code simply does not work, so Software Developers need to either frame the prompt differently or give more specifications. Even after the AI-generated code is operational, Software Engineers need to assess its quality characteristics: readability, maintainability, efficiency, reliability, scalability, security, and consistency. Finally, it is a required practice to test if the code does not disrupt existing business logic or functionality.

To perform all these functions well, Software Developers need to have the following skills:

• Prompt engineering: For the AI coding assistant to produce desired outcomes, Software Developers need to be able to write clear and specific instructions, often iterating them. There are different prompting engineering approaches: zero-shot, few-shot, chain-of-though and others.
• Code reviewing: Code generated by artificial intelligence may contain syntax errors, problems with logic, or security holes. Software Developers need to evaluate their and their colleagues’ code before going forward with it.

Capabilities of modern AI coding assistants open up opportunities for many use cases. Software engineering teams have seen the benefits of using AI for code generation, test case automation, refactoring, security alerting, data management, and many other practices. For a detailed overview of the most popular use cases, read our recent blog post, “Generative AI for Software Engineering: Use Cases and Limitations”.

How Popular is AI-Assisted Software Engineering?

AI-assisted software development has long ago captured the attention of the software development community. According to Stack Overflow, 70% of Software Developers already use or plan to use AI tools in their work. Amongst US-based Developers the acceptance level is even higher: 92% of them have already integrated AI both in and outside their work.

However, not all groups of Developers are eager to adopt AI. Software Engineers specializing in backend, app, and embedded system development are less likely to use AI coding assistants. Working with complex systems and handling security-related issues, these Developers don’t see themselves using AI in the near future.

AI has yet to make a name for itself when it comes to adoption at the enterprise level. Although 62% of C-level representatives agree that AI in software engineering is necessary for a competitive edge, companies aren’t exactly making a rush. According to Capgemini, only 11% of organizations are actively using AI in their software development workflows. There is, however, a change on the horizon. Capgemini predicts that by 2026, 85% of all software workforces will use generation AI for training, experimenting, pilot projects, or implementing in their workflows.

What are the reasons for such optimistic predictions? Because AI in software engineering has the potential to yield unparalleled benefits, especially as AI coding assistants mature and become more accurate and secure. 

Benefits of AI-Assisted Software Development (With Real-Life Examples)

Companies that deployed pilot projects for AI-assisted software development almost unanimously pointed to two key benefits: faster task competition and time efficiency (and consequently cost savings). 

Yet, the potential of AI for software development goes beyond the gift of time. In the long run, software leaders can get higher returns on investment by shifting their focus from time/cost reduction to value creation. These are the benefits AI for software engineering can unlock: 

Source: Gartner 

1. Acceleration of the Development Process

Modern AI coding assistants, although they are still maturing, boast advanced capabilities and can generate entire code blocks, suggest fixes, maintain documentation, or detect issues. When given relevant project context, code base, and clear instructions, AI tools can significantly accelerate the development process and enable Developers to produce more work in less time. 

CloudZero, a cloud cost intelligence platform, increased bug fixing speed by 300% with Copilot. As a result, they are able to shorten the time between idea and implementation.

PayPal did a pilot project and compared the time it took to develop a simple custom app using AI and without it. Using no AI assistant, their control group completed the work in eight hours. While their AI-augmented team completed the same amount of work in just 2 hours and 45 minutes. 

And Bolttech, an InsurTech provider, slashed the time spent updating code documentation by 75%. By doing so, they freed up valuable development time to focus on coding and multitasking.

2. Reduction of Repetitive, Tedious Tasks

Software Developers often spend their time troubleshooting and doing repetitive tasks rather than actually coding and coming up with creative solutions. According to Atlassian’s 2024 report, 69% of Software Developers lose 8 hours or more of their working week to inefficiencies. Amongst these time wasters are technical debt (59%), insufficient documentation (41%), build process (27%), and lack of time for deep work (27%). 

AI can solve this problem by taking over low-value tasks, such as debugging, refactoring, or boilerplate code writing.  

HOVER, a construction software provider, uses an AI code assistant to write most of its boilerplate code. “Copilot can get you about 80% of the way there, then you just have to make the final tweaks to solve your specific problem,” says the Director of Engineering at HOVER.

World Wide Technology, an enterprise tech services provider, saw a 30-50% increase in productivity for repetitive work after integrating Codeium. The company automated documentation automation, unit test generation, and code explaining, which allowed Software Developers to focus on business-critical initiatives. 

3. More Products/Software Updates Released

Accelerating the software development cycles, AI coding tools can potentially help companies make more software and update releases. This means that businesses who adopt AI for software engineering will have a better advantage when meeting the ever-increasing needs of their users. 

By eliminating repetitive work and letting Developers focus on high-quality coding, AI coding assistants enable faster and more efficient CI/CD pipelines. Emirates NBD, an online banking provider, reported a 2x rise in in-production monthly deployments as a result of GitHub Copilot implementation. Meanwhile, at Wayfair, with the help of Google’s Gemini, software engineering teams were able to set up environments 55% faster than they could before.

4. Faster Resolution of Technical Debt

As companies increase their tech footprint and expand their release schedule, their user base grows but so does technical debt. Technical debt is the practice of relying on temporary coding fixes in an attempt to deliver software faster. In time, if technical debt piles up, it might lead to significant code defects and scaling issues.  

AI coding assistants can help teams strike a balance between reducing technical debt and developing new code. First of all, AI can help you resolve technical debt by enabling you to refactor and debug the existing code. Secondly, AI code assistants can give Software Developers more time to produce higher-quality code in the future without the need to rely on patch solutions. This way, technical debt will accumulate more slowly. 

Accenture has rolled out GitHub Copilot for 12000 of their Developers. As a way to deal with technical debt, they ask Copilot’s chat to explain existing code and then feed that explanation (along with additional project context) back to the coding assistant. 

Additionally, AI has the potential to facilitate the migration of large code bases from legacy technologies. EY has conducted several programs demonstrating the value of LLMs in code migrations. The company tested and tried custom models for SAS to PySpark conversion, PostgreSQL to Google BigQuery migration, and L/SQL to Spark SQL transformation. 

5. Increased Satisfaction in Development Teams

AI relieves Software Developers of tasks they don’t enjoy. As a result, teams get more time for creative initiatives, teamwork, and upskilling. For instance, 4 out of 5 Engineers believe AI coding tools will improve collaboration among their teams. Additionally, 70% of them believe AI coding tools will give them an edge at work, primarily through upskilling and productivity increases.

At Accenture, for instance, coding has become more enjoyable for 95% of Developers since they adopted Copilot. At TomTom, the feeling of productivity has increased for 85% of Software Engineers with an AI coding assistant. 

Are There Limitations?

Yet, AI for software engineering does not come without its challenges. At least half of Developers have doubts about the accuracy of output from AI coding tools. Also, there are concerns about regulatory compliance, bias, additional validation work, and the AI tool learning curve. 

Overcoming these challenges is possible with a comprehensive strategy for adopting AI for software development. You need to clearly understand the goals of implementing AI coding assistants, select an AI tool that fits your criteria, assemble development teams for a pilot project, and perform trials. Finally, you will need to ensure that your internal AI practices are in line with international legislation (EU AI Act) and create no disruptions in your relationships with partners, customers, and external vendors. It’s important to use AI only when you have explicit, legally-documented permission from your clients, customers, and users.

The post AI for Software Development: General Overview and Benefits appeared first on Edvantis.

Service providers make it into the Leader quadrant only if they are highly rated by G2 users and have high proficiency scores. Steven, Chief Executive Officer at Edvantis, says:

About Software Developer Services Grid

In their Software Developer Services Grid, G2 spotlights companies that assist businesses with software development and develop customized products for their needs. They evaluate service providers who, after estimating the project’s time and cost, can design, develop, test, and deliver it from scratch. 

The Grid for Software Developer Services ranks service providers based on customer satisfaction, market presence, and proficiency. 

• Leaders: Service providers with high ratings from G2 users and high proficiency measurements.
• High performers: Companies with high satisfaction ratings but low proficiency scores. 
• Contenders: Firms with high proficiency scores but low satisfaction ratings.
• Niche providers: Companies with low proficiency and customer satisfaction ratings.

As a company that regularly earns positive feedback and offers high-quality software development services, Edvantis has been named G2’s 2024 Summer Leader.

About Edvantis 

Edvantis is a value-driven technology company offering software development, quality assurance, data science, AI, and IT operations services. Since our founding in 2005, we have delivered over 450 projects for companies worldwide, including Fortune 500 and Gartner Magic Quadrant leaders. Our technology teams have the industry’s top experts skilled at modernizing your IT infrastructure, developing competitive products, and incorporating innovation across all business functions. Edvantis’ excellent customer service and software services are consistently acknowledged by G2, Clutch, IAOP, Stevie Awards, and other notable organizations.

About G2 

G2 is the leading platform for comparing and rating software and service providers. With 2.4M verified reviews across 2000 categories, G2 helps businesses select reliable technology providers and vendors. It is G2’s highest priority to provide honest reviews that are 100% verified.

Every year, G2’s peer reviews help over 90 million users make better software decisions. G2 is the review marketplace of choice for Forbes Cloud 100.  

The post G2 Ranks Edvantis Among Leading Software Development Providers, Summer 2024 appeared first on Edvantis.

Award winners are selected based on their expertise and ability to deliver compared to other companies in their line of service. Result evaluations take into account feedback from thousands of reviews published on Clutch.

Edvantis has become a double award recipient, thus receiving a place among the Clutch A-list! This is our second dual recognition following our Clutch Champion and Global Award win last fall.

“These awards reflect the high quality of our work this year, as demonstrated by the Clutch reviews we received and our 96% CSAT,” said Steven, Edvantis CEO. “We’re proud to be recognized amongst the top-rated software development leaders worldwide and will continue to raise the bar.”

“We are delighted to present this award for the second year, celebrating both new and returning achievers for their outstanding performance,” said Sonny Ganguly, Clutch CEO. “These honorees represent the top companies on our platform, consistently exceeding client expectations and receiving an abundance of positive feedback from their clients. Their continued excellence sets a high standard, inspiring others in their respective service lines to strive for similar levels of distinction.

About Edvantis

Edvantis is a global software development company that helps organizations achieve their goals through technology and innovation. With 450+ successfully delivered projects, we are a trusted partner for leaders in the healthcare, real estate, hi-tech, transportation industries, and public sector. Clutch, G2, IAOP, Stevie Awards, and other leading review platforms and critical players in the field recognize Edvantis’ service quality year after year.

About Clutch

Clutch empowers better business decisions as the leading global marketplace of B2B service providers. More than 1 million business leaders start at Clutch each month to read in-depth client interviews and discover trusted agency partners to meet their business needs. Clutch has been honored for the past 6 consecutive years as an Inc.5000 fastest-growing company and by the Washington Business Journal as one of the 50 fastest-growing private companies in the DC metro area for 2023.

The post Edvantis Recognized as a Clutch Global Leader and a Clutch Champion for Spring 2024 appeared first on Edvantis.