Discover 10 Emerging Technologies in Software to Track in 2026

Accelerate Productivity in 2025

Reignite Growth Despite the Global Slowdown

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Executive Summary: Emerging Technologies in Software [2026]

1. Artificial Intelligence: 40% of enterprise applications will house task-specific AI agents, up from less than 5% in 2025. This demonstrates AI’s movement from experimentation to operational core.
2. Cloud and Edge Computing: 33% of companies utilizing edge computing reserve at least 10% of their IT budgets for edge projects, while 21% set aside less than 5%.
3. Internet of Things (IoT): A total of 21.1 billion connected IoT devices are set to be installed in 2025.
4. Blockchain and Distributed Ledger Technology: 86% of executives think positively about employing blockchain technology.
5. Immersive Technologies (AR/VR/XR): The Ford Motor Company cut training time by 70% and increased knowledge retention by 90% using VR assembly line training.
6. Hyperautomation: The global hyperautomation market size is projected to reach approximately USD 270.63 billion by 2034, growing at a CAGR of 17.04%.
7. Quantum Computing: Quantum computing is set to have a total economic impact of USD 250 billion by the end of the decade.
8. Software-Defined Infrastructure (SDI): Organizations report a 30% to 60% reduction in infrastructure spending when transitioning from variable-cost public cloud to fixed-cost SDI models.
9. Digital Twins: By 2029, over 95% of IoT platforms will offer digital twin capabilities, making them a standard industry feature by 2028.
10. Composable Architecture: 70% of organizations plan to adopt composable architecture to stay competitive.

How We Researched and Where This Data is From

• Analyzed our 3100+ industry reports on innovations to gather relevant insights and create a master matrix. Cross-checked this information with external sources for accuracy.
• Leveraged the StartUs Insights Discovery Platform, an AI- and Big Data-powered innovation intelligence platform covering 9M+ emerging companies and over 20K+ technology trends worldwide, to confirm our findings using the trend analysis tool.

Macro Trends Accelerating New Software Technologies

In 2026, the software landscape is set to change because of many big influences that speed up the development of new technologies. These changes affect both the demand and supply of new ideas. This makes it possible for companies to adopt and grow their software skills in response to strategic and operational needs.

Rising Pressure for Operational Efficiency

When compared to laggards, leaders who use AI and sophisticated software now increase EBITDA by 10% to 25%, according to global technology services suppliers. Businesses need to extract the maximum to encourage the use of software that automates, optimizes, and tracks operations from start to finish.

Data Explosion & Real-Time Decision Demand

The estimated global data volume reached approximately 182 zettabytes by 2025, and it is anticipated to double by 2028. Interest in edge computing, streaming analytics, and AI-powered operational platforms grows as a result of organizations’ need for software systems that absorb, analyze, and act on data instantly.

Shift Toward Modular, Flexible IT Architectures

Most people today use modular and API-first architectures instead of monolithic systems. Reports say that the infrastructure that was originally intended for web apps, including container orchestration, now needs to be able to handle AI workloads, hybrid clouds, and multi-cloud deployments. This change lets businesses add new software components more quickly, scale more easily, and adapt to changing business needs.

AI Commoditization & Worker Augmentation

AI is evolving from standalone solutions to a core layer integrated into enterprise software, according to several studies. According to the Bain & Company Technology Report, for instance, agentic AI and GenAI are already increasing productivity and will spread throughout workflows.

This reframes software strategy toward systems that enhance human decision-making, shorten execution cycles, and standardize knowledge work. As a result, enterprises direct more investment toward platforms and applications that incorporate AI natively.

10 Top Emerging Technologies in Software to Watch in 2026

1. Artificial Intelligence: 75% of Knowledge Workers use AI tools

By 2026, Gartner predicts 40% of enterprise applications will house task-specific AI agents, up from less than 5% in 2025. This demonstrates AI’s movement from experimentation to operational core.

Moreover, a study conducted by McKinsey showcases that 62% of organizations are at least experimenting with AI agents.

Adoption and Investment Trend

As businesses start using AI in their everyday work, investment in and use of AI continue to grow. The global AI market is set to reach USD 2.76 trillion in 2032, growing at a CAGR of 32.5%.

More and more employees are using AI tools at work, with nearly 75% of knowledge workers saying they do. Even while adoption is speeding up, most firms still have trouble scaling pilots into full deployments throughout the whole company.

Businesses increasingly prioritize investments in use cases that have measurable operational impact, such as automation, workflow optimization, and decision support systems.

Key Use Cases and ROI Potential

AI adds value to many business operations by automating jobs that require a lot of manual work, helping with knowledge work, and making decisions better. AI in customer service assists with answers to common questions and cuts down on the amount of labor that needs to be done by hand.

AI-powered automation in customer service reduces labor costs by up to 90% through answering routine inquiries and order tracking.

Predictive analytics gives real-time information that improves decision-making in finance, logistics, and operations. Software engineering teams use AI to help them write code, test it, and make documentation, which boosts their productivity. Embedded agents assist in keeping an eye on hybrid systems all the time and make sure that workflows run as smoothly as possible.

For every USD 1 invested in AI, businesses have seen an average return of USD 3.5, with 5% of companies reporting returns of USD 8.

Challenges and Considerations

• Data quality & governance: High-quality data is necessary for AI results. Inadequate input erodes confidence. 64% of organizations cite data quality as their top data integrity challenge.
• Ethics & compliance: Organizations deal with bias, transparency, and regulatory problems, including data privacy. PwC’s survey shows 58% of executives cite ethical and regulatory risk as primary AI adoption barriers.
• Workforce upskilling: Many users are adopting AI tools without formal training. 70% of leaders said their workforce isn’t ready to successfully leverage AI tools.
• Scaling from pilots: A majority of firms report difficulty in turning experimental AI projects into enterprise-scale value. 42% of companies abandoned most AI initiatives in 2025, up sharply from 17% in 2024.

Spotlighting an Innovator: Intelswift

Estonian startup Intelswift offers an integrated AI customer service automation platform that enhances customer engagement for sales, marketing, and support teams. It deploys AI-powered chatbots, an intelligent copilot, live chat, and advanced analytics. Its technology functions by automatically handling routine customer queries and interactions.

The startup transfers complex issues to human agents while constantly gathering operational data for actionable insights. In addition, the platform supports cross-channel communication and enables continuous learning through its analytics suite. This ensures that team workflows remain efficient and interactions stay personalized.

Intelswift enhances workforce productivity by automating repetitive tasks and empowers businesses to improve service quality and make informed decisions grounded in real-time analytics. This advances both operational efficiency and customer satisfaction.

2. Cloud and Edge Computing: 94% of Organizations Operate Using Cloud

More than 94% of organizations with over 1000 employees have a major portion of their workloads in the cloud, according to a survey of 800 organizations. These cloud platforms offer scalability, shared services, and global reach.

According to a report from Gartner, 75% of data will be generated and processed at the edge. Edge computing diverts computing and data processing closer to devices, sensors, and end users.

Adoption and Investment Trend

According to survey data, 33% of companies that use edge computing set aside at least 10% of their IT budgets for edge projects, while 21% set aside less than 5%.

The global cloud computing market is at USD 912.77 billion in 2025 and is expected to reach USD 5.150 trillion by 2034, growing at a CAGR of 21.2%.

The global edge computing industry is expected to grow at a CAGR of 8.1%, reaching USD 248.96 billion in 2030.

Major investments are announced by tech giants to accelerate innovation and localize cloud resources. For example, Microsoft’s USD 3 billion cloud and AI infrastructure in India and AWS’s USD 5 billion investment in cloud infrastructure in Thailand.

Key Use Cases and ROI Potential

The cloud-edge approach makes it possible to employ high-value software. Edge nodes cut down on latency for streaming services and make the user experience better. Edge computing near sensors lets predictive maintenance and local decision-making happen in real-time analytics and the Internet of Things (IoT) without delays in backhaul.

Hybrid designs allow global SaaS systems to work while keeping sensitive data close to regulated areas. It also cuts cloud usage costs by filtering 70% to 80% of raw data at the edge. Businesses see an ROI in decreased latency, better user experience, lower bandwidth costs, and faster deployment of distributed applications.

For instance, companies that put analytics on the edge save money and time by not having to move vast amounts of raw data to central cloud data centers.

Challenges and Considerations

• Managing dispersed compute resources makes things more complicated to run in both cloud and edge contexts. Examples include wind turbines, retail sensors, or mining site devices, where manual IT servicing is not feasible, and platform-level automation is essential.
• Data governance gets difficult when processing happens in more than one place, such as across countries, regulatory zones, and local nodes.
• Interoperability becomes a problem when users try to connect cloud services, edge nodes, on-prem systems, and old apps. Heterogeneity increases the risk of data silos, limits cross-vendor compatibility, and often slows the adoption of truly unified solutions.
• Vendor lock-in issues increase when businesses use cloud services with specialized edge hardware or proprietary platforms.
• Network security becomes a concern when software solutions need to be able to handle real-time, decentralized processing across thousands of devices and endpoints.

Spotlighting an Innovator: WaterWorksX

WaterWorksX is a New Zealand-based startup that provides an industrial edge platform that offers an edge-to-enterprise ecosystem to advance water and wastewater network management.

It connects control systems and telemetry with both edge devices and cloud applications. This allows utilities to continuously acquire real-time data and apply advanced analytics for optimization and predictive maintenance.

The startup’s platform provides interoperability across operational technology, IT infrastructure, and asset management layers. It enhances deployment and scaling for utilities managing critical infrastructure.

The enhanced network performance and operational efficiency enable utility operators to maintain resilient and reliable systems while minimizing downtime and optimizing resource allocation. This ensures dependable water infrastructure operations.

3. Internet of Things (IoT): Global IoT Spending hits USD 4.8 trillion

IoT propels a change in software technology from discrete applications to ubiquitous systems that incorporate real-world occurrences, real-time data, and enterprise software stacks.

In 2025, there will be 21.1 billion connected IoT devices, projected to grow to nearly 40 billion by 2030 at a CAGR of 13.2%.

IoT integration allows businesses to manage processes and keep an eye on assets. It also creates smart environments that include real-time decision-making in production, supply chains, field operations, and customer interactions.

Adoption and Investment Trend

The whole IoT market, including hardware, connectivity, software, and services, is predicted to reach USD 865.2 billion in 2030, growing at a CAGR of 9.6%.

The total global IT spending is estimated to grow to USD 4.8 trillion, with the IoT market estimated to account for 7% of it.

It is predicted that the IoT platforms market alone, which includes analytics, device management, and integration software, will increase at a CAGR of around 13.2% to reach USD 49.17 billion by 2034.

A faster growth path is also provided by the convergence of IoT and 5G connections. For instance, the 5G-IoT market is expected to reach USD 89.4 billion by 2030 at a CAGR of 50.3%.

Key Use Cases and ROI Potential

IoT makes several high-impact use cases possible in many fields. Predictive maintenance uses IoT sensors and software analytics to cut down on downtime and maintenance expenses in production.

It reduces unplanned downtime by up to 50%, which leads to higher asset availability and production output. It also cuts maintenance costs by 10% to 40% through targeting the right maintenance activities on time.

Asset tracking and condition monitoring software use real-time data from devices to improve the flow of inventory and deliveries in supply chains and logistics.

Smart buildings and utilities employ linked equipment and software dashboards to make the best use of energy, keep track of assets, and improve services. This enables cost reductions, with one study showing 4.6% daily energy savings leading to a 22% monthly cost reduction in commercial buildings.

Challenges and Considerations

• Expanding Attack Surfaces: With the rapid increase of IoT devices, organizations face highly expanded attack surfaces. This makes it difficult to monitor device activities and protect against unauthorized access or cyberattacks. There was a 46% increase in ransomware attacks targeting industrial environments, causing operational disruption.
• Lack of standardization: IoT devices from different manufacturers often use incompatible protocols and data formats. This results in fragmentation and interoperability problems across platforms and systems.
• Scalability: IoT initiatives frequently start out as pilots but find it difficult to expand to thousands of devices and several locations. Manual onboarding and network configuration become impractical for thousands of devices across multiple locations, affecting deployment timelines and reliability.
• Infrastructure and connectivity limitations: Unreliable networks or electricity disrupt IoT networks in distant or industrial areas. This impacts data quality and device uptime. Wireless networks in environments with physical barriers like metal machinery and industrial settings are susceptible to interference, impacting consistent communication with IoT endpoints.

Spotlighting an Innovator: Uniot

Ukrainian startup Uniot offers an edge-first IoT platform that orchestrates connected devices through automated scripts. It embeds logic at the device level and secures data flows backed by blockchain-based integrity checks.

The platform processes data as close to the devices as possible, where users define scripts in familiar scripting languages that run directly on gateways and endpoints. It monitors sensor inputs, triggers actions, and synchronizes only essential information with the cloud for oversight and storage.

Uniot also incorporates blockchain mechanisms to register device events and configuration changes, which strengthens trust across distributed environments and supports tamper-evident audit trails for multi-stakeholder deployments.

In addition, the startup offers a unified environment where DIY developers and enterprises design automation scenarios, manage heterogeneous devices, and adapt rules in real time. The platform achieves this without rewriting underlying firmware, which reduces integration complexity and improves operational responsiveness.

4. Blockchain and Distributed Ledger Technology: Blockchain Market grows at CAGR 64.2%

Blockchain and distributed ledger technology (DLT) allow several people to exchange records of transactions or states that are immutable. This cuts down on the need for middlemen and makes data transactions more transparent.

The architecture supports interoperability by providing standardized frameworks and protocols for secure data exchange between disparate systems, platforms, and organizations.

The blockchain interoperability market is estimated at USD 332.8 million in 2025 and is expected to be USD 1.832 billion in 2035, growing at 18.6%.

The technology enables trusted multi-party workflows in business software, such as supply chain provenance, cross-border payments, and digital identification. It also opens up new architectural models for connecting operations between companies.

Adoption and Investment Trend

Businesses are more interested in blockchain and DLT than ever before. According to one set of estimates, the blockchain market will grow from USD 32.99 billion in 2025 to USD 393.45 billion by 2030, growing at a CAGR of almost 64.2%.

Additionally, enterprise polls show that over 86% of executives think there are good reasons to employ blockchain. But only about 31% of organizations have moved past the trial stage as of 2024.

Going forward, interoperability, regulatory frameworks, and the tokenization of real-world assets (RWA) gain importance for expanding DLT implementations. Regulatory experiments done last year have led the EU and major financial actors to focus further on this aspect. It creates new partnerships and regulatory pilots underway to support compliant and efficient asset tokenization.

Key Use Cases and ROI Potential

Blockchain reduces cross-border payment processing time from 3 to 5 days to seconds and cuts remittance costs by up to 80% by automating reconciliation and recordkeeping.

In the supply chain, DLT enables end-to-end real-time tracking of goods from production to delivery, reducing delays, fraud, and recall costs. A global study across 150+ implementations reported a 20% to 30% reduction in supply chain costs and a 75% improvement in traceability.

For digital identity and credentialing, blockchain-based digital identity management reduces fraud incidents by 30 to 40%, cuts onboarding time by 50 to 60%, and lowers compliance costs by 20 to 30%.

Asset tokenization increases the liquidity of previously illiquid assets. It reduces high transaction costs by eliminating intermediaries and enabling fractional ownership.

Further, smart contracts automate compliance checks, dividend payments, and settlement processes. This reduces human error and operational expenses.

Challenges and Considerations

• Integration complexity: The integration of DLT into current enterprise software stacks, legacy systems, and workflows requires considerable architectural redesign and connector work.
• Scalability and performance: Blockchain platforms like Bitcoin and Ethereum have limits in transactions per second (TPS) (Bitcoin: 6-8 TPS, Ethereum: 12-15 TPS) compared to thousands of TPS in centralized systems. This leads to high fees, long confirmation times, network congestion, and difficulties scaling to enterprise workloads.
• Legal and regulatory uncertainty: Data privacy, multi-jurisdictional ledgers, and tokenized asset governance pose compliance problems and impede adoption. The patchwork of state and international regulations and the lack of clear guidance impede blockchain adoption by enterprises.
• Vendor fragmentation and interoperability: The ecosystem’s numerous ledger systems, smart contract languages, and network models make compatibility and selection challenging.
• Trust and change management: Adoption necessitates a compelling value proposition that goes beyond decentralization for its own sake, cultural changes, and stakeholder alignment across partners.

Spotlighting an Innovator: zkFold

Swiss blockchain startup zkFold develops scaling and interoperability technology for the Cardano blockchain. It provides products like zkFold Symbolic smart contracts that utilize advanced zero-knowledge proof algorithms to optimize smart contract performance.

The startup’s system translates high-level Haskell code into arithmetic circuits. It directly powers cryptographically secure, privacy-preserving operations on Cardano and enables developers to create robust decentralized applications with reduced transaction costs.

The startup implements mechanisms such as zk-rollups and a modular architecture; it achieves high transaction throughput and streamlined data compression. It supports frictionless cross-chain operations, effectively addressing compute, storage, and network limitations.

zkFold facilitates enhanced smart contract execution and interoperability across both public and private blockchains. This offers developers and enterprises an efficient solution to scale, collaborate, and drive blockchain adoption.

5. Immersive Technologies (AR/VR/XR): 75% of Fortune 500 companies employ VR for Training

Immersive technologies, including augmented reality (AR), virtual reality (VR), and extended reality (XR), mix digital information with actual or totally simulated settings. These tools change the way companies train their workers, make products, work together, and provide customers with great experiences.

Around 75% of Fortune 500 companies have adopted VR for training and education, and enterprise users are projected to drive 60% of total VR revenue by 2030.

Immersive technologies allow businesses to look for better ways to visualize complicated data and cut down on the expense of physical prototypes. This allows distributed teams with spatial computing environments that improve engagement and accuracy.

Adoption and Investment Trend

As platforms develop, enterprise use cases get more credible, and hardware gets lighter, investment in AR, VR, and XR keeps growing. Businesses in the manufacturing, healthcare, automotive, and retail sectors utilize immersive tools for training, maintenance, remote support, and design workflows.

The global AR and VR market is predicted to reach USD 96.32 billion by 2029, growing at a CAGR of 34.2%.

Additionally, the XR market is expected to reach USD 519.5 billion by 2032, growing at a CAGR of 30.8%.

As AI-driven content creation shortens development times and collaborative 3D workspaces interact with cloud platforms, adoption speeds up.

The AR IoT in manufacturing adoption is estimated to reach USD 40 to 50 billion in economic value by 2025.

Key Use Cases and ROI Potential

Immersive technologies allow businesses to substitute real-world tasks with digital simulations that save money and make things more accurate. Virtual training environments reduce the need for real equipment and downtime. It also allows for uniform learning on a large scale.

The Ford Motor Company cut training time by 70% and increased knowledge retention by 90% using VR assembly line training.

Product and design teams utilize immersive visualization to test ideas earlier and find problems more quickly. This shortens the time it takes to make prototypes.

AR overlays allow remote collaboration with field technicians, which makes it easier for them to solve problems the first time and cuts down on travel expenditures. AR collaboration reduces downtime, saving potential losses of up to USD 50 billion per year globally.

Virtual try-ons and interactive product demos that get people more involved and help them make decisions faster are good for retail and customer-facing businesses. These retail experiences create a 40% increase in engagement with AR applications.

Challenges and Considerations

• Hardware Investment & Lifecycle Management: Major investments are often required in hardware, especially when upgrading digital systems or supporting new technologies like AI and spatial computing. Further, lifecycle management complicates IT budgeting, with best practices required for procurement, maintenance, and retirement to avoid obsolescence and sunk costs.
• Content Availability & Custom Development Effort: Immersive and custom software applications face limited content and template availability. This necessitates substantial development efforts for bespoke experiences. Custom solutions demand rigorous requirement analysis, iterative communication, and dedicated milestones, all of which extend timelines and raise costs.
• User Adoption Barriers: Usability and comfort are major drivers of user resistance. Technologies that disrupt established workflows, require steep learning curves, or are physically uncomfortable face slower and less confident adoption.
• Effective Training: Effective training and ongoing support are critical, with 83% of companies deploying LMS platforms and over 40% of Fortune 500 firms relying on continuous e-learning to address adoption barriers and support users post-launch.

Spotlighting an Innovator: Expolab

Belgian startup Expolab offers XR tools that deliver immersive interaction with brands and products through augmented reality, virtual reality, and mixed reality solutions designed for expos, sales, marketing, and storytelling applications.

The startup’s platform allows users to integrate digital assets such as 3D models, photos, or videos into brochures and physical environments. It launches engaging experiences instantly via QR code scans, smartphone cameras, or VR headsets.

Expolab’s system supports transitions between AR, VR, and MR, letting users place digital objects in real space, enter fully virtual worlds, or blend both realms for real-time, interactive engagement.

Its features include user-friendly deployment of dynamic branded content, rapid setup for event exhibitors, and compatibility with multiple device types, which reduces friction for both clients and audiences. Expolab enables organizations to enhance customer interaction, streamline product showcases, and tell brand stories more effectively.

6. Hyperautomation: 90% of organizations see Hyperautomation as a Priority

Hyperautomation puts together several automation technologies, like robotic process automation (RPA), machine learning (ML), intelligent document processing (IDP), and analytics, into end-to-end workflows that cover both business and IT activities.

A recent study by Gartner indicates hyperautomation reduces operational costs by around 30% more than incremental tooling.

It moves businesses from automating single processes to creating digital operations that keep getting better. Hyperautomation enables software to get to value faster by letting computers find, run, and improve processes on their own, instead of just using human or semi-automated approaches.

Adoption and Investment Trend

The global hyperautomation market size is projected to reach approximately USD 270.63 billion by 2034, growing at a CAGR of 17.04%.

34% of organizations adopt hyperautomation specifically to improve employee productivity.

According to Gartner, 90% of large enterprises identify hyperautomation as a strategic priority to streamline processes using AI. The pursuit of operational excellence across processes and functions to support resilience enhances the demand for hyperautomation.

These trends reflect rising investment in automation platforms, low-code and no-code tools, and the convergence of AI with process automation in enterprise software strategies.

Key Use Cases and ROI Potential

Hyperautomation generates value by redesigning workflows across functions. In finance, it automates invoice processing, reconciliation, and compliance tasks. Bank of America’s AI-powered virtual assistant, Erica, has handled over 1.5 billion interactions, assisting customers with transactions, spending insights, and financial planning.

For HR applications, it accelerates onboarding and document management. For example, Grant Thornton used FlowForma to automate key processes like job appraisal, client acceptance, and data access requests. This AI-backed automation reduced inefficiencies and improved process speed by 60% while ensuring compliance and better transparency.

Organizations that adopt hyperautomation report faster process throughput, fewer manual errors, and reduced operating costs. For instance, companies using AI-driven end-to-end automation of expense processing achieved over 80% reduction in processing time.

The ROI is reflected in greater workforce productivity, improved scalability of operations, and quicker innovation cycles.

Challenges and Considerations

• Architectural and Operational Complexity: Automation across multiple new technologies (such as AI, IoT, edge computing, and RPA) disrupts existing enterprise architecture and workflows. This increases architectural and operational complexity. Integrating these technologies requires coordination across traditionally siloed IT, operations, and business teams, demanding new governance and architectural models.
• Scaling Beyond Pilot Initiatives: Fewer than 20% of organizations worldwide have mastered the measurement and scaling of hyperautomation initiatives. Organizations struggle to define and track business impact metrics, hindering executive buy-in and project expansion beyond initial pilots.
• Integration of Legacy Systems: Integrating automation technology with legacy systems such as warehouse management systems (WMS) and enterprise resource planning (ERP) is cited as a top challenge. This is due to outdated APIs, inconsistent data formats, and communication protocol mismatches.
• Workforce Readiness, Governance, and Skills Gaps: There is a growing gap between automation technology adoption and available workforce skills. For example, 63% of Indian organizations report difficulty in hiring qualified AI talent, and 43% highlight lack of internal expertise as a top impediment to automation adoption.

Spotlighting an Innovator: Autom Mate

US-based startup Autom Mate offers a no-code and low-code technology orchestration platform that enhances operations by connecting disparate business systems and automating workflows.

It integrates multiple platforms, applications, and data sources through intuitive drag-and-drop interfaces. This enables organizations to unify processes without extensive technical expertise.

The startup’s solution allows businesses to automate tasks and data transfer in real time, maintain control, and ensure consistent performance across their digital ecosystem.

It centralizes integration and automation. Autom Mate accelerates efficiency and promotes organizational agility while reducing IT bottlenecks for enterprise clients.

7. Quantum Computing: Funding for Quantum Startups hit over USD 2 billion

Quantum computing uses quantum bits (qubits), superposition, and entanglement to do calculations far faster than classical computers. It optimizes, simulates, and encrypts data that classical systems have a hard time with in software-centric businesses.

While a classical processor handling 10 bits performs 10 calculations, a quantum processor with 10 qubits manages up to 2¹⁰ or 1024 calculations simultaneously. This demonstrates exponential scaling and parallelism.

Recent analytics indicate that quantum computing is capable of driving USD 250 billion in total economic impact by the end of the decade.

Adoption and Investment Trend

The quantum computing ecosystem is quickly moving from research to its first commercial use. McKinsey & Company says that developments in recent years mean that quantum sensing and specialized quantum devices are ready for mass production.

The global quantum computing market is at USD 3.52 billion in 2025 and is expected to reach USD 20.20 billion in 2030, growing at a CAGR of 41.8%.

Venture capital funding in quantum startups hit over USD 2 billion last year, up 50% from 2023. Investment in the first three quarters of 2025 alone was USD 1.25 billion, more than doubling the previous year’s figures.

Key Use Cases and ROI Potential

Quantum computing allows enterprise software ecosystems in areas where traditional methods fall short.

For instance, quantum simulation shortens R&D cycles in drug discovery and materials science by modeling molecular interactions with more accuracy than conventional systems. Polaris Quantum Biotech’s QuADD platform, built using quantum computing, optimizes molecular libraries in days, faster than traditional drug discovery timelines.

In the financial services industry, quantum algorithms can help portfolios do better, figure out market risk, and come up with better trade-execution techniques. This cuts down on modeling mistakes and speeds up decision-making. Bank of Montreal worked with Xanadu to apply quantum Monte Carlo algorithms for faster and more accurate trading product valuations, directly increasing trading speed and accuracy.

Additionally, quantum optimization enables complicated scheduling, routing, and inventory problems in supply chains and logistics by cutting costs, time, and resource use. BMW used Honeywell quantum computers to optimize component procurement. This enabled cost and time savings in the automotive supply chain.

Challenges and Considerations

• Lack of fault-tolerant quantum hardware: Today’s quantum computers are noisy, error-prone, and operate with limited qubits, far from the scale and reliability needed for general, fault-tolerant operation.
• Identifying quantum advantage: Few tasks currently realize an advantage over classical methods. Most business and technical problems remain more efficiently solved by existing classical technology. Verified instances of quantum speedup are rare and mostly confined to highly specific or artificial benchmarks.
• Evolving software stacks: Significant skills shortages and fragmented tools complicate development, with quantum software requiring mastery of novel concepts, languages, and integration with classical IT systems. The talent gap means many jobs remain unfilled and slows practical progress in building hybrid applications.
• Security and cryptography: Quantum computers threaten today’s encryption, prompting urgent migration planning for post-quantum cryptography, yet most organizations have not started this transition. Malicious actors are already gathering encrypted data to decrypt after stronger quantum capabilities arrive.

Spotlighting an Innovator: Qoro

UK-based startup Qoro develops a network software platform that powers distributed quantum computing by integrating quantum and classical computing systems.

The platform builds a comprehensive network stack that connects a diverse set of devices. This includes GPU clusters, high-performance computers, and quantum computers that enable enhanced interoperability across infrastructure.

Qoro automates the entire lifecycle from application submission to execution, dynamically optimizing the workflow based on both the specific algorithm and the hardware available.

The solution distinguishes itself by abstracting hardware details from users. It streamlines the development of quantum programs by providing hardware vendors with a scalable, efficient stack for resource sharing and device interconnection.

The startup delivers an integrated system that lowers barriers to quantum software development and enhances resource efficiency for networked quantum and classical computing environments.

8. Software-Defined Infrastructure (SDI): Reduces Infrastructure Spending by at least 30%

Software-defined infrastructure manages computation, storage, networking, and occasionally security resources using software instead of fixed hardware setups.

With this method, businesses dynamically supply capacity, use policy-based governance, and see infrastructure components as moving parts in a software stack.

A recent report indicates 70% of enterprises are planning to adopt some form of SDI by 2025.

SDI enables organizations to modernize their IT infrastructure by allowing them to launch apps faster. This scales up their resources more cost-effectively, uses hybrid clouds, and improves flexibility.

Adoption and Investment Trend

The SDI market is growing strongly. One market report estimates the global SDI market is projected to reach USD 110.2 billion by 2033, growing at a CAGR of 12.8%.

Digital transformation propels SDI investments. The adoption of hybrid/multi-cloud models and the rising need for dynamic scalability to handle IoT, AI, and big data workloads is also accelerating SDI initiatives.

As enterprises pursue hyper-scale cloud models, edge infrastructures, and multi-cloud management, they increasingly allocate budget toward SDI solutions that support automation, orchestration, and infrastructure-as-code frameworks. Regionally, North America leads adoption, while Asia-Pacific is rapidly expanding.

Key Use Cases and ROI Potential

SDI enables organizations to deploy new applications and services faster by standardizing resource pools and automating infrastructure provisioning.

For example, companies using software-defined storage and networking integrate dev/test, production, and disaster recovery environments on the same hardware. This reduces capital and operational costs. Organizations report a 30% to 60% reduction in infrastructure spending when transitioning from variable-cost public cloud to fixed-cost SDI models.

Hybrid cloud deployments manage workloads across public and private clouds with unified policy and control. This improves utilization and reduces latency for business-critical applications.

Containerized platform stacks running on SDI foundations accelerate time-to-market for new software functionality. This allows IT teams to shift focus from infrastructure maintenance to innovation.

The ROI stems from lower total cost of ownership (TCO), improved resource utilization, faster provisioning cycles, and enhanced flexibility in responding to change.

Challenges and Considerations

• Change Management and Skill Upgrades: The majority of SDI failures are due to a lack of adequate change management and workforce skills. This emphasizes the need for organization-wide training and a culture shift.
• Toolchain Convergence and Vendor Integration: Enterprises implementing SDI struggle with toolchain sprawl, where disparate management and monitoring tools hinder unified visibility across compute, storage, and networking layers. This forces either consolidation or a substantial integration effort. Multi-vendor environments cause delays in SDI projects due to incompatible toolchains and cloud APIs across vendors and providers.
• Security and Governance Complexity: The move to software-driven control planes increases the surface area for cyber threats. 81% of organizations express concern about securing distributed and hybrid infrastructure, as legacy hardware-based controls cannot be relied upon.
• Integration with Cloud and DevOps: SDI projects not aligned with cloud services and DevOps pipelines end up as isolated islands, with IT leaders reporting disconnected SDI deployments that fail to deliver automation and agile benefits across the larger organization.

Spotlighting an Innovator: Arcfra

Singaporean startup Arcfra offers a full-stack software-defined platform that enhances the management of on-premises enterprise cloud infrastructure.

The startup’s platform integrates computing, storage, networking, security, backup, disaster recovery, and Kubernetes services into a cohesive solution. It utilizes direct deployment on bare metal systems.

The startup features unified support for virtual machines and containers, which allows enterprises to modernize their cloud infrastructure while maintaining flexibility for both legacy and cloud-native workloads. This includes automated disaster recovery and resiliency functions, which reduce operational complexity and ensure high availability.

9. Digital Twins: Digital Twin Market to hit USD 149.81 billion by 2030

Digital twins are virtual copies of physical assets, processes, or systems that use live data to show how they behave, perform, and work in real time. It uses sensors, analytics, simulation models, and enterprise software to establish a feedback loop between the physical and digital worlds that never stops.

By 2029, over 95% of IoT platforms will offer digital twin capabilities, making them a standard industry feature by 2028.

As companies use data-driven decision-making in manufacturing, energy, logistics, buildings, and urban systems, their strategic importance expands.

Digital twins help businesses learn how their assets work, predict what will happen in the future, and improve performance without affecting real-world operations. Recent reports estimate that organizations adopting digital twins see up to 30% enhancements in production cycle times.

Adoption and Investment Trend

Adoption keeps going up as businesses expand their IoT deployments and invest more in real-time analytics and link operational technology (OT) with IT systems.

The global digital twin market is at USD 21.14 billion and is expected to reach USD 149.81 billion in 2030, growing at a 47.9% CAGR.

Digital-twin platforms allow businesses to cut down on downtime, manage the life cycles of their assets, speed up production, and make better plans for their infrastructure. Companies effectively implementing digital twins in their digital transformation strategy report 25% faster time-to-market and 20% improvement in product quality.

As more people become interested in model-based engineering, supply chain visibility, and data-driven operations, investments grow. The market for model-based manufacturing technologies is set to reach USD 54.4 billion in 2025 and is projected to grow to USD 116.6 billion by 2035.

Key Use Cases and ROI Potential

Digital twins add real value by making operations more reliable, lowering lifecycle costs, and allowing for proactive decision-making.

In manufacturing, they use simulations of production lines to find the best way to get things done, find bottlenecks, and predict when maintenance will be needed. Companies like Unilever utilize digital twins to optimize production lines, achieving better overall equipment effectiveness (OEE) and reducing waste and energy consumption.

For the energy sector, they use models of turbines, grids, and renewable assets to find problems before they cause failures and keep performance levels high. Case studies showcase that digital twin deployments cut costs by up to five times and unlock over USD 100 million in value through precision asset management.

Additionally, building operations staff employ digital models to make the best use of energy, plan upgrades, and keep an eye on the environment. Predictive maintenance powered by digital twins reduces building maintenance costs by up to 30% and lowers breakdowns by up to 70%.

Challenges and Considerations

• Need for Strong Data Engineering Foundation: Data engineering is essential for integrating data from sensors, operational systems, and business software. This is critical for creating a unified, high-quality view for analytics and operational decision-making.
• Maintaining High-Fidelity Modeling: Accurate, high-fidelity digital twins require continuous calibration using real-world data, including operational and sensor data. Research has shown that calibration frameworks (like Bayesian models) must adapt models based on ongoing field observations and track both parameter uncertainty and discrepancies between simulated and real-world behavior to maintain reliability.
• Interoperability and Vendor Ecosystem Challenges: Digital twin implementations often encounter interoperability issues due to fragmented vendor ecosystems, proprietary formats, and non-standard modeling practices. This hinders smooth data exchange across platforms.
• Data Governance, Security, and Privacy Risks: As digital twins collect detailed operational and environmental data, governance, security, and privacy concerns escalate due to the sensitive nature and regulatory requirements of collected information. Data governance and compliance workflows, combined with robust security protocols, are necessary to mitigate risk and build trust in digital twin deployments.

Spotlighting an Innovator: IoTwin

IoTwin is a US-based startup that offers a real-time 3D digital twin platform that utilizes AI, IoT, and security systems. The startup’s platform finds application in advanced facility management, real estate, security, and insurance-related asset derisking applications.

The technology operates by generating dynamic and interactive simulations of actual physical spaces. It also uses real-time data inputs from connected devices and sensors to continuously reflect and analyze operational states, emergencies, and risks.

The startup features accurate 3D modeling, intelligent security integrations, and flexible compatibility with most hardware and software environments. This enables deployment for organizations of varying sizes.

The platform empowers decision-makers to optimize day-to-day operations and emergency response, delivering actionable insights that bridge operational challenges to clarity and safety. It provides measurable value in risk reduction, efficiency, and asset protection.

10. Composable Architecture: Shortens time to Market by 37%

Composable architecture is a modular, API-first way of building software in which services, data components, and business activities are put together and taken apart on the fly to fit the demands of the business as they change. Gartner predicts that by 2025, over 70% of organizations will adopt some form of composable systems to stay competitive.

Organizations use replaceable building parts instead of monolithic systems. These building blocks make it easy to make changes, try new things, and grow. With this architecture, tech teams can add new features faster, adapt to changes in the market more easily, and use new technologies without having to redo the whole system.

Organizations running composable architectures report an average of 37% shorter time-to-market compared to monolithic systems.

Adoption and Investment Trend

As companies work to modernize their e-commerce, financial services, logistics, and industrial software, the use of composable architecture is growing faster.

Additional investment is going into API-driven platforms, microservices, headless systems, and low-code orchestration technologies. This is happening as more companies move toward cloud-native engineering and platform teams. API-related investments in financial services alone are projected to exceed USD 25 billion in 2025, with a CAGR of 11.9% for the broader API monetization platform market.

Companies replace old, inflexible systems with modular parts that are upgradable on their own. This cuts down on downtime and makes operations more flexible. Companies adopting composable architectures are expected to outpace their competition by 80% in digital delivery speed by 2026.

The focus on faster release cycles, better developer productivity, and working with AI-driven services makes businesses even more committed to composable strategies.

Key Use Cases and ROI Potential

Composable architecture supports a number of important operations by letting businesses establish and grow digital services without having to completely change their systems.

The approach enables product teams to release software faster by putting together reusable parts. This cuts down on the time it takes to get customer-facing apps to market.

In e-commerce, modular checkout, search, payment, and personalization services let developers keep improving without affecting the main platforms. Retailers implement AI-driven personalization or integrate new payment methods with minimal disruption.

Companies employ composable data services to connect different systems and make analytics workflows more efficient, which leads to better decisions.

Platform engineering teams build internal developer platforms that make things easier, boost productivity, and standardize best practices. Enterprise users report up to a 40% increase in software development and QA productivity after adopting composable solutions.

Modular systems eliminate up to 75% of operational costs owing to easier updates, less downtime, and the ability to swap out modules without full-scale migrations.

Challenges and Considerations

• Complexity of Operations and Architecture: Managing hundreds of microservices significantly increases operational complexity compared to monolithic systems. Tasks like monitoring, deployment, inter-service communication, and troubleshooting become much harder as the number of services grows. 62% of organizations report that managing inter-service dependencies is a significant challenge in microservice environments.
• Versioning, Dependencies, and Lifecycle: Version control across microservices is necessary to manage changes and support backward compatibility. It also increases the complexity of tracking multiple APIs and dependencies across changing environments.
• Legacy System Migration: Migrating from legacy monolithic systems to microservices is a technically and organizationally complex process. There are risks of integration failures, disruptions, and data integrity issues, which require careful planning, modular migration strategies, and compatibility layers.
• Risks of Poor Standards and Fragmented Design: Without clear architectural standards, teams may create microservices in different ways, leading to a distributed monolith rather than true modularity. This reduces system flexibility and scalability, making change management and deployments more error-prone.

Spotlighting an Innovator: NuStudio

US-based startup NuStudio provides a composable, AI-first, API-powered data platform that manages enterprise workloads by integrating modular services. These services unify data pipelines, governance, and intelligence across complex systems.

It orchestrates data mesh architecture, federated queries, workflow automation, and end-to-end lineage, while embedding foundation model support and real-time inference. It uses agents to accelerate development and production deployment.

The startup incorporates enterprise-grade controls, such as role-based access, audit logging, observability, and seamless directory and catalog integrations.

It also features prebuilt vertical modules for rapid deployment in healthcare, defense, industrial IoT, and financial operations. NuStudio enables organizations to streamline operations, reinforce data security, and minimize fragmentation while driving actionable insights and scalability for their most demanding data and AI challenges.

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