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Executive Summary: Tech Forecasting for 2026-2030

Macroeconomic Forces:

Technology Growth Engines:

  1. AI Everywhere: McKinsey finds 78% of firms using AI in at least one function. PwC estimates a USD 15.7 trillion GDP impact by 2030.
  2. Cloud, Edge, and Connectivity: IDC sees edge spending rising toward USD 380 billion by 2028. Gartner also states that 50% of critical apps will be outside centralized clouds by 2027.
  3. Quantum and Specialized Chips: Semiconductors will exceed USD 1 trillion in revenue by 2030. The quantum-computing market could grow nearly 35% annually from 2024 to 2032.
  4. Climate Tech and Circularity: IEA states that global energy investment is set to reach USD 3.3 trillion in 2025. Also, the global annual investment in grids will need to nearly double to more than USD 600 billion per year by 2030.
  5. Immersive and Human-Centric (HITL) Systems: VR and AR have the potential to deliver GBP 1.4 trillion to the global economy by 2030.
  6. AI-driven Cyber Resilience: PwC’s 2025 survey states that 67% of Swiss companies and 77% globally intend to increase their cyber budgets this year, with data protection/trust and cloud security among their top priorities.
  7. Hyperreal Digital Twins: McKinsey states that 44% of manufacturers are already using twins, with 15% planning to, and benefits include up to 25% first-time-right gains.
  8. Autonomous Robotics: The global robotics market is headed to USD 160-260 billion by 2030.
  9. Industry-Bio-Med-Tech Convergence: The global healthcare bioconvergence market is projected to reach USD 220.56 billion by 2030, expanding at a 7.6% compound annual growth rate (CAGR).
  10. Decentralized Machine Economies: STL Partners states that the Economy of Things (EoT) could comprise over 10% of all IoT-enabled devices by 2030.

Markets, Funding & M&A Signals:

Sector-by-Sector ROI Signals:

 

 

How We Researched and Where This Data is From

  • Analyzed our 3100+ industry reports on global business and technology shifts to gather technology forecasts from 2026-2030.
  • Cross-checked this information with external sources, including McKinsey, PwC, IDC, and the IEA, to ensure accuracy and relevance.
  • Leveraged the StartUs Insights Discovery Platform, an AI- and Big Data-powered innovation intelligence platform covering 9M+ emerging companies and 20K+ technology trends worldwide, to confirm our findings.

Frequently Asked Questions (FAQs)

1. How should business leaders use this technology forecast to inform investment decisions?

Leaders should treat the forecast as a strategic roadmap, linking technology growth engines to timelines and quantified ROI scenarios. For companies in the Morgan Stanley study of S&P 500 firms, AI adoption alone could deliver up to USD 920 billion in net economic benefit annually by 2026.

2. Which technologies will reach mass-market adoption first between 2026 and 2030?

According to Gartner’s 2025 Hype Cycle for Emerging Technologies, the first wave of mass-market adoption is to be driven by technologies enabling the autonomous business era. This includes machine customers, AI agents, decision intelligence, and programmable money.

Macroeconomic Forces Shaping Tech’s Future [2026-2030]

Between 2026 and 2030, the global technology landscape will move from experimentation to execution. International Data Corporation (IDC) projects that worldwide spending on digital transformation will reach almost USD 4 trillion by 2027. Meanwhile, research by McKinsey & Company indicates that generative AI alone could add between USD 2.6 and 4.4 trillion annually to the global economy.

In the next five years, macroeconomic dynamics are shaping the backdrop for technology investment and accelerating tech forecast-driven transformation. Below are three key forces that business leaders must track as they navigate the 2026-2030 landscape.

Moderating Growth and Ongoing Uncertainty

According to OECD projections, global potential output growth is set to decline gradually, from around 2.9% currently to 2.7% in the early 2030s, and even lower thereafter.

Meanwhile, the International Monetary Fund’s October 2025 update forecasts global real-GDP growth at 3.2% in 2025, then slipping to 3.1% in 2026.

Investment Climate and Interest Rate

According to the OECD, global growth in G20 economies will reach 3.2% in 2025. It will then ease to 2.9% in 2026 as tighter financing conditions and investment headwinds weigh on activity.

Business investment in the US is forecast to rise only 3.6% in 2025 and drop to 3% (approx.) in 2026, influenced by elevated interest rates and capital cost pressures.

McKinsey projects that global investment in next-generation compute and data-center infrastructure will reach USD 6.7 trillion by 2030. This projection highlights the scale of capital flows required to sustain digital growth even amid higher-rate environments.

Government Policy and Stimulus

In June 2025, the UK Government committed GBD 86 billion over four years to support science and technology R&D, including AI, batteries, and biotech.

The Allianz report states that the global economy must invest about 3.5% of GDP each year, around USD 4.2 trillion annually, over the next decade. These investments will fund future-proof social, transport, energy, and digital infrastructure.

Additionally, the International Monetary Fund’s World Economic Outlook notes that tax and spending-driven stimulus is supporting output growth in advanced economies. It adds that investments in digital public infrastructure are a key component of near-term policy stimulus.

10 Technology Growth Engines: The Next Wave [2026-2030]

1. Artificial Intelligence Everywhere

 

Credit: McKinsey

 

In a latest survey by McKinsey, 78% of respondents said that they are already using AI in at least one business function. According to PwC, AI could contribute up to USD 15.7 trillion to the global economy by 2030, accounting for nearly 14% of global GDP in 2030.

 

Credit: McKinsey

 

In the coming five years, agentic AI and autonomous decision systems are expected to move from pilots to enterprise infrastructure.

 

Source: McKinsey

 

The technology’s reach is already visible across 2024-2025 enterprise operations. Microsoft reported a 27% jump in cloud revenue in 2025, largely driven by AI workloads on Azure OpenAI Service.

Similarly, Google DeepMind’s Gemini 2.5 Pro model is setting new benchmarks for multi-modal reasoning and agentic task execution

Also, Goldman Sachs forecasts that AI could raise global productivity growth by 1.5 percentage points annually through 2030.

Banks using internal AI assistants answer over 40% of employee IT inquiries in some cases. Also, Siemens uses AI copilots for industrial automation design.

Likewise, Amazon’s project Bedrock supports AI-driven logistics optimization across its fulfillment network.

AI infrastructure spending is accelerating rapidly across global markets. For example, Citi projects that AI-related capital expenditure will surpass USD 2.8 trillion by 2029. It will be driven by investments in hyperscaler data centers, model-training clusters, and specialized semiconductors.

In Q4 FY2025, NVIDIA’s data center segment generated USD 35.6 billion in revenue, a 93% year-over-year increase that shows how its hardware business is driving the AI-infrastructure boom.

Over the next years, agentic AI systems, capable of autonomously performing multistep tasks, will move from limited pilots to enterprise-wide infrastructure. Gartner predicts that 33% of enterprise software applications are expected to include agentic AI by 2028.

2. Cloud, Edge & Connectivity Convergence

Global cloud, edge, and connectivity ecosystems are converging into a unified digital backbone that enables real-time intelligence. According to IDC, global spending on the whole cloud economy is projected to surpass USD 1.3 trillion by 2025. This growth reflects enterprises’ increasing demand for low-latency computing and distributed data processing.

 

Credit: IDC

 

Likewise, IDC estimates that the global spending on edge computing to grow at 13.8%, reaching nearly USD 380 billion by 2028. Gartner projects that by 2027, enterprises will host 50% of their critical applications outside centralized public cloud locations.

Mordor Intelligence forecasts that the edge computing market alone will grow from USD 227.80 billion in 2025 to USD 424.15 billion by 2030 at a compound annual growth rate (CAGR) of 13.24%. This growth is driven by advancements in 5G and the emergence of 6G network deployments. Gartner also predicts that 75% of enterprises will prioritize backup of SaaS applications as a critical requirement by 2028.

Moreover, connectivity is central to this evolution. Ericsson forecasts that 5G subscriptions will be close to 5.6 billion globally by the end of 2029. This expansion will enable instant and intelligent decision-making across smart factories, logistics, and energy networks.

In June 2025, Thames Freeport and Nokia announced a strategic partnership to deploy Verizon Private 5G Networks across multiple key logistics, manufacturing, and innovation sites along the River Thames Estuary in the UK.

Also, in March 2025, Telefonica announced a collaboration with Nokia and AWS. They validated the first 5G Standalone (SA) call using Nokia Cloud RAN workloads hosted both on-premises and in the AWS Spain region at Aragon.

Similarly, Microsoft announced an approximately USD 80 billion capital investment in cloud and AI infrastructure. The company also plans to expand edge zones to support 5G-enabled inference, augmented reality (AR)/virtual reality (VR) workloads, and real-time applications in logistics, healthcare, and IoT.

Further, by 2030, global data volumes are expected to surge to nearly 500 zettabytes in total and over 350 exabytes per month. This rapid growth will drive the convergence of cloud, edge, and ultra-connectivity, anchoring the next phase of intelligent automation.

3. Quantum & Specialized Chips Reshape Computational Boundaries

The global computing architecture is entering an innovative phase as demand for AI workloads and advanced simulations pushes the limits of classical processing.

 

Credit: McKinsey

 

According to McKinsey, the global semiconductor industry is on track to cross USD 1 trillion in annual revenue by 2030. The firm highlights generative AI, advanced compute/data-center chips, and growth in automotive and wireless sectors as key drivers of this expansion.

In parallel, IDC forecasts that global spending on AI infrastructure will exceed USD 200 billion by 2028, with accelerated servers driving most of the growth. Servers with embedded accelerators already represent 70% of total AI server spending and will surpass 75% by 2028. Enterprises are adopting domain-optimized processors such as GPUs, DPUs, and ASICs instead of traditional CPUs.

Quantum computing is progressing from theoretical research to real-world applications. IBM’s Quantum Roadmap includes plans to break the 1000-qubit barrier, with the Condor processor already announced, and further scaling toward fault-tolerant systems beyond 2029.

Moreover, Deloitte mentions that overall investment in quantum computing is growing and that the quantum-computing market could grow nearly 35% annually from 2024 to 2032.

Also, the Japanese government’s extra budget for the fiscal year ending in March allocates JPY 1.05 trillion to develop and research next-generation chips and quantum computers. It also earmarks JPY 471.4 billion to strengthen domestic advanced chip production.

NVIDIA’s Blackwell B200 accelerator offers up to 20 petaflops of FP4 sparse AI compute performance. In internal comparisons, NVIDIA estimates this to be around four times the AI throughput of the prior Hopper H100 generation.

According to Reuters, TSMC will invest USD 65 billion in its US fab 3 facility, which will produce 2-nanometer chips by 2030.

Energy intensity remains a central challenge. A Bloomberg article states that US data center electricity demand is projected to reach 8.6% (approx.) of US electricity demand by 2035.

Further, the US CHIPS and Science Act of 2022 appropriates about USD 52.7 billion for semiconductor manufacturing, R&D, workforce training, and allied coordination for FY2022-FY2027. Among its goals are to encourage semiconductor supply chain resilience and support emerging technologies such as quantum computing.

4. Climate Tech Drives Decarbonized Growth & Circularity

According to the IEA, global energy investment is set to reach USD 3.3 trillion in 2025, as countries and companies seek to protect themselves from geopolitical and economic risks.

The IEA indicates that global annual investment in grids will need to nearly double to more than USD 600 billion per year by 2030 under today’s policy settings. It also expects that the global renewable power capacity to grow significantly through 2030, driven largely by solar photovoltaics (PV).

Meanwhile, the IEA’s Net Zero Emissions Scenario calls for rapidly scaling up solar and wind this decade, targeting annual additions of 630 gigawatts (GW) of solar PVs and 390 GW of wind by 2030.

 

Credit: McKinsey

 

According to McKinsey, demand for net-zero goods and services across 11 value pools could generate more than USD 12 trillion in annual sales by 2030. The key segments identified include low-emission transport, power generation, hydrogen, carbon capture, and circular-economy technologies.

Corporate sustainability commitments and clean-energy procurement are showing strong growth. For example, the amount of renewable electricity sold to companies under long-term power purchase agreements rose 35% in 2024.

McKinsey & Company also states that members of the global lighthouse network in manufacturing achieved up to a 30% reduction in energy consumption through AI-enabled use cases in operations.

Likewise, BNEF forecasts that the global passenger EV sales are projected to rise from 17.6 million (approx.) in 2024 to a higher figure by 2030. It also forecasts that EVs could make up 42% (approx.) of new car sales globally by 2030.

Digital technologies are also supporting the climate tech ecosystem. The IEA reports that AI-based fault detection rapidly identifies and precisely pinpoints grid faults. This reduces outage durations by 30-50%.

Further, the WEF states that the global economy is currently only 7.2% circular. The WEF also estimates that a shift toward the circular economy could offer more than USD 4.5 trillion in additional economic output by 2030.

5. Immersive & Human-Centric Technologies become Mainstream

According to Grand View Research, the extended reality (XR) market is projected to reach USD 1069.27 billion by 2030, growing at a CAGR of 32.9% from 2024 to 2030. This market growth is driven by rising adoption across training, manufacturing, and design collaboration. As hardware costs decline and network bandwidth expands, immersive systems are becoming critical productivity enablers rather than experimental add-ons.

Also, PwC states that VR and AR have the potential to deliver GBP 1.4 trillion to the global economy by 2030.

Another PwC report finds that VR and AR could add USD 1.5 trillion to the global economy and create over 23 million jobs by 2030. These technologies already contribute more than USD 46 billion to global GDP.

By mid-decade, immersive environments will merge with human-in-the-loop (HITL) automation systems where humans guide, correct, and collaborate with AI to ensure contextual decision-making. Industry commentators note that by 2030, human-in-the-loop will be a foundational design feature in AI-driven automation, particularly in regulated or high-stakes sectors.

Additionally, Philips reports that the AR surgical navigation technology combines a flat detector with cameras to capture external and internal anatomy (via X-ray) and generate a 3D AR view guiding implants. Similarly, Johnson & Johnson’s VR training modules accelerate surgeon readiness.

For manufacturers like Bosch and BMW, AR/VR support and predictive-maintenance systems are reducing disruptions on the shop floor and speeding up maintenance workflows.

Consulting firms predict that immersive technologies, coupled with HITL automation, will become core to workforce strategy by 2030. Deloitte’s 2025 Global Human Capital Trends report notes that embedding learning into daily workflows and leveraging immersive technologies is vital for faster capability building and aligning skills with business outcomes.

Likewise, Accenture’s 2025 Technology Vision reveals that 69% of executives believe that AI will bring an urgent imperative for reinvention in how their organizations design, build, and operate technology systems.

 

 

6. Cybersecurity Reinvented with AI

In 2025, the cybersecurity landscape reached an inflection point as threat complexity and attack velocity outpaced human response capabilities. According to McKinsey, AI-enabled security systems are allowing organizations to reduce their mean time to detect and respond to cyberattacks.

 

Credit: PwC

 

According to PwC’s 2025 survey, 67% of Swiss companies and 77% globally intend to increase their cyber budgets this year, with data protection/trust and cloud security among their top priorities.

 

Credit: PwC

 

Also, the WEF reports that AI-enabled cybersecurity tools are enabling a shift from reactive defense to predictive resilience through the use of automated threat detection, cross-sector signal analysis, and real-time response coordination.

 

Credit: Gartner

 

Gartner reports that worldwide end-user spending on information security will reach USD 213 billion in 2025, rising from USD 193 billion in 2024. The firm projects a 12.5% increase in 2026, bringing total spending to USD 240 billion.

AI’s role in cybersecurity is expanding from anomaly detection to autonomous threat containment. IBM’s 2025 report shows that global data breach costs have declined for the first time in five years. The report finds that average costs fell 9% to USD 4.44 million, down from USD 4.88 million the previous year.

Similarly, CrowdStrike highlights behavioral analytics and ML-based detections as the core of modern cyber defense. The report notes that adversaries are using malware-free, identity-driven, and cloud-based intrusion methods.

Additionally, Microsoft security copilot uses generative AI to summarize incident data, guide responses, and automate security workflows. Early adopters reported up to 40% time savings on core tasks and 60%+ efficiency gains on report preparation.

Google Cloud’s Chronicle security operations platform leverages AI-based analytics and correlation engines to ingest and analyze very large volumes of security telemetry.

 

Credit: Accenture

 

Further, Accenture reports that organizations that achieve the highest security maturity are 69% less likely to suffer an advanced attack and achieve a 1.5 times higher success rate at blocking threats compared with lower-maturity peers.

7. Hyperreal Digital Twins Orchestrate Physical Systems

According to McKinsey, digital twins, which are virtual replicas of physical assets and processes, are evolving from simulation tools to full-scale orchestration platforms. In one survey, 44% of manufacturers reported having already implemented a digital twin, and another 15% planned to deploy one. These systems offer benefits such as up to 25% improvement in first-time-right design and up to 20% freed engineering capacity.

 

 

MarketsandMarkets states that the digital twin market is expected to grow from USD 21.14 billion in 2025 to around USD 149.81 billion by 2030, at a CAGR of 47.9%.

Over the next few years, digital twins will transition from passive monitoring to autonomous decision-making. Gartner predicts that by 2027, over 40% of large organizations worldwide will use digital twins alongside Web3 and spatial-computing technologies in strategic projects. AI, simulation, and edge computing systems will integrate to create hyperreal environments. These digital layers will predict operational disruptions and coordinate physical systems in real time.

Moreover, Siemens’ collaboration with NVIDIA’s Omniverse platform enables industrial digital twins that combine photorealistic 3D visualization with AI-driven analytics. Similarly, Shell uses digital-twin systems to model and monitor its heavy-asset operations. These systems enable scenario simulation, data-driven maintenance, and reduced unplanned downtime.

Further, Accenture reports that scaling virtual twin technologies across industries could provide USD 1.29 trillion in additional economic value and reduce 7.52 gigatons of CO₂ emissions by 2030.

8. Autonomous Robotics Support the Next Industrial Innovation

McKinsey & Company observes that robotics and automation are entering a new phase driven by intelligent autonomy. AI-enabled robots, digital twins, and self-programming devices are becoming vital to industrial operations and manufacturing competitiveness.

 

Credit: BCG

 

The global robotics market is projected to grow to USD 160-260 billion by 2030. Also, according to Mordor Intelligence, the global robotics market reached USD 73.64 billion in 2025 and is projected to expand to USD 185.37 billion in 2030 at a CAGR of 20.28%. This is due to industries deploying fleets of autonomous robots for manufacturing, logistics, construction, and healthcare.

Moreover, Amazon’s reported fleet of hundreds of thousands of autonomous mobile robots in fulfillment centers highlights the transition from pilot to at-scale deployment. Analysts predict that Amazon will save USD 10 billion annually by 2030 through robotics integration.

Similarly, ABB reported a record-high order intake of USD 9.8 billion in Q2, up 16% year-on-year. This growth highlights rising global demand for industrial automation solutions.

Additionally, Deloitte states that companies deploying smart manufacturing technologies (including automation, data/analytics, and robotics) reported labor-productivity improvements in the 7-20% range and production-output gains of 10-20%.

By 2027, 50% of companies with warehouse operations will leverage AI-enabled vision systems to replace traditional scanning-based cycle-counting processes.

9. Industry-Bio-Med-Tech Convergence

The fusion of biotechnology, medical technology, and industrial systems is creating a new wave of cross-sector innovation that will redefine productivity and healthcare outcomes by 2030. According to Mordor Intelligence, the global healthcare bioconvergence market, valued at USD 153.21 billion in 2025, is projected to reach USD 220.56 billion by 2030, expanding at a 7.6% CAGR.

Similarly, Grand View Research estimates the sector will reach USD 215 billion by 2030, driven by advancements in bioelectronics, nanotech-enabled sensors, and AI-integrated diagnostics. In parallel, Bonafide Research projects the global medtech market to rise from USD 724 billion in 2024 to more than USD 1 trillion by 2030. This market growth signals deep interdependence between industrial engineering, biotechnology, and digital healthcare ecosystems.

As this convergence accelerates, Deloitte notes that the convergence of AI, genomics, synthetic biology, and digital-cyber interfaces is expected to accelerate pharmaceutical R&D and improve cost-efficiency by 2030. Similarly, McKinsey’s report estimates that the global bio-revolution could deliver USD 2-4 trillion annually by around 2030-2040.

Moreover, IQVIA reports that companies are embedding AI and analytics into clinical-trial design and development. Analysts project that the global AI in clinical trials market will grow at a 12-15% CAGR through 2030.

Additionally, Roche and PathAI partnered in February 2024 to integrate AI-driven pathology into digital diagnostics. Roche’s Tissue Diagnostics unit will work exclusively with PathAI to develop AI-enabled pathology algorithms for companion diagnostics and deploy them on the navify digital pathology platform.

Likewise, AstraZeneca and Illumina partnered to integrate AI-enabled genomics into drug discovery and biomarker validation. They are applying Illumina’s AI tools, PrimateAI and SpliceAI, and AstraZeneca’s analytics framework to large-scale genomics and multi-omics data at the Centre for Genomics Research.

Looking ahead, the bioelectronics segment is expected to reach USD 20.36 billion by 2031, at a CAGR of 11.30% from 2024-2031.

10. Decentralized Machine Economies for Digital Ownership

 

Credit: STL Partners

 

According to STL Partners, the Economy of Things (EoT) could comprise over 10% of all IoT-enabled devices by 2030, with approximately 3.3 billion devices capable of autonomous economic transactions. Likewise, GSMA Intelligence states that IoT connections are forecast to exceed 38 billion by 2030, with the enterprise segment accounting for more than 60% of the total.

The number of machine-to-machine (M2M) connections continues to grow, forming the backbone of decentralized ownership models. Analysts from Zion Market Research estimate that the M2M connections market will reach USD 29.1 billion by 2030, growing at a moderate CAGR of around 4.5%.

Moreover, real-world platforms are enabling decentralized machine economies. For instance, smart-vehicle fleets and logistics robots are enrolled into tokenized networks where they sell services like charging, transportation, and mapping to each other and to organizations. These platforms integrate digital ownership, identity, and micro-payments.

Decentralized identity and blockchain wallets are also reshaping ownership. Decentralized identity and blockchain-wallet capabilities are reshaping ownership models in IoT marketplaces. Analysts and academic research suggest that by 2030 a significant share of these marketplaces will enable devices to hold tokens and negotiate payments directly.

Markets, Funding & M&A Signals

 

 

Venture Capital Reallocation

AI is absorbing a structurally larger share of venture dollars. Q3-2025 marked the fourth consecutive USD 90 billion+ funding quarter, with AI on track to capture 50%+ of annual VC for the first time, pointing to persistent crowding-in around AI infrastructure, agents, and tooling.

 

Investment and cumulative capacity in LNG liquefaction, 2015-2028

Credit: IEA

 

Clean energy and electrification remain the second major magnet for capital even as policy swings persist. 2024 set a USD 3 trillion+ global energy investment run rate, with USD 2 trillion into clean energy, creating downstream demand for grid software, storage, and power-aware data centers.

Corporate venture capital continues to shape AI deal flow, with CVC participation at 25% (approx.) of AI deals (2024). This is a signal that strategic buyers are pre-positioning for capability gaps they may later acquire.

Public Market and IPO Resurgence

Public markets are regaining momentum after two years of subdued listings, with technology IPOs set to re-emerge as a major funding channel between 2026 and 2030.

 

Credit: PwC

 

PwC reports that global IPO proceeds reached USD 58.2 billion in the first half of 2025, rising USD 8.6 billion from H1 2024 (USD 49.6 billion) and marking 17% year-on-year growth.

The pipeline for 2026 includes more than 400 private tech firms, predominantly AI infrastructure, semiconductor, and climate-tech companies that are preparing for listing across the US, Europe, and Asia.

 

Credit: EY

 

Also, the EY report states that in H1 2025, the global market saw USD 61.4 billion raised from 539 IPOs, a 17% increase in proceeds year-on-year. The report says that the technology sector saw a total capital raised increase of 19% compared with the same period last year.

Moreover, Bloomberg Intelligence notes that AI-exposed names have shown no signs of slowing, either across fundamentals or valuations. Another report says that despite recent declines, the fundamentals for AI-semiconductor names remain strong, and investor optimism remains.

Meanwhile, Deloitte states that venture-backed technology firms are accelerating toward public markets as liquidity conditions improve. The firm notes that the average time from founding to IPO for venture-backed tech companies has shortened. This marks one of the fastest venture-to-public cycles in two decades.

Cross-Sector Consolidation and M&A

AI build-out has re-accelerated tech dealmaking. IT and tech M&A value reached about USD 740.7 billion in 2024, rising 46% year over year and making tech the largest sector by value.

Flagship transactions demonstrate where competitive moats are forming. Synopsys acquired Ansys for USD 35 billion in 2024. It combines electronic design automation (EDA) with multiphysics simulation to create an end-to-end silicon-to-systems toolchain that will change chip, automotive, aerospace, and industrial design.

UK and China regulators cleared the deal in 2025, and the companies completed the merger after final approvals.

Cisco completed its USD 28 billion acquisition of Splunk in 2024, uniting observability and security capabilities to power AI-era operations. These mega-deals signal how convergence between software, data, and hardware ecosystems is reshaping competitive dynamics. They also show that AI infrastructure and simulation software have become strategic assets in digital-industry transformation.

 

Credit: SEMI

 

Additionally, rising upstream capital expenditure signals consolidation ahead. Industry forecasts show 300 mm fab equipment spending reaching USD 107 billion in 2025, USD 116 billion in 2026, USD 120 billion in 2027, and USD 138 billion in 2028. This growth favors large-scale suppliers and integrated software-hardware platforms spanning EDA, simulation, and metrology.

Companies are expected to pursue more bolt-on acquisitions and mega-mergers focused on power-efficient computing, advanced packaging, and factory software.

Sector-by-Sector Outlook: Technology Impacts and ROI Scenarios

Finance: AI Integration to Boost Efficiency up to 15% by 2030

The financial sector is projected to undergo a deep structural shift between 2026 and 2030 as automation, AI-driven analytics, and embedded systems redefine profitability.

PwC projects that banks integrating AI into front- and back-office workflows could see up to a 15 percentage-point improvement in efficiency ratios by 2030. Also, institutions relying on legacy infrastructure risk lagging in both cost optimization and compliance agility.

Likewise, IDC highlights rapid growth, with a five-year CAGR of 20.5% driven by data-intensive use cases like AI-powered claims automation, real-time financial advice, and digital banking.

Healthcare & Biotech: AI in Healthcare to Yield USD 646B Savings by 2030

 

Credit: Strategy&

 

According to Strategy&, a part of PwC, AI in healthcare could yield USD 646 billion in cost savings and USD 222 billion in revenue gains by 2030.

Also, McKinsey reports that AI-enabled drug discovery and development are shortening R&D timelines, with trial lengths reduced by 15-30%. The firm projects that by the end of the decade, these technologies could generate tens of billions of dollars in annual productivity gains across the biopharma sector.

Similarly, PwC projects that by 2035, about USD 1 trillion in annual healthcare spending will shift away from fragmented, infrastructure-heavy systems.

In parallel, IQVIA reports that digital and decentralized clinical trial models are rapidly gaining traction.

Additionally, AstraZeneca states that it is using AI and ML tools to enhance biologics discovery and to design new biologic drugs. Likewise, GE HealthCare is deploying AI-powered imaging and advanced therapies to reshape oncology for earlier detection and more personalized treatment.

Manufacturing: ~75% of Companies will Adopt Automation by 2027

McKinsey reports that advanced companies reduce maintenance costs by 20-30% by applying technology-enabled efficiencies to managing distributed fixed assets.

The National Association of Manufacturers (NAM) reports that 40% of manufacturing leaders aim to achieve cost reductions through smart factory strategies. The report adds that these leaders also expect improved customer satisfaction from such initiatives.

According to Mordor Intelligence, the global smart factory market size is estimated at USD 389.14 billion in 2025 and is forecast to reach approximately USD 591.57 billion by 2030.

Meanwhile, Gartner predicts that by 2027, more than 75% of companies will have adopted some form of cyber-physical automation in their warehouse operations.

 

Credit: Deloitte

 

Moreover, Deloitte states that a smart factory approach, connecting machines, processes, and people, has the potential to improve asset efficiency by up to 20%. The firm adds that it also enables significant energy savings, cutting operational costs by as much as 30%.

Also, Tesla integrates computer-vision AI to automate and optimize its production lines. The system enables robots to use visual feedback for real-time defect detection, adaptive part positioning, and improved safety.

Additionally, Foxconn plans to deploy 5000 robots by 2026 for automated, 5G-enabled assembly lines that reduce labor intensity and increase responsiveness.

Energy & Utilities: Predictive Analytics Reduces Maintenance Costs by up to 30%

According to the IEA, global investment in clean energy technologies and infrastructure is expected to reach about USD 2.2 trillion in 2025. The IEA also states that annual investment in clean energy will need to rise to roughly USD 4-4.5 trillion by 2030 to align with a net-zero emissions scenario.

Moreover, BloombergNEF states that renewable generation is set to increase 84% by 2030 and that solar, wind, and other renewables will serve 67% of the world’s demand for electric power by 2050.

The International Energy Agency (IEA) notes that AI-based fault detection and grid sensors reduce outage durations by 30-50%

Deloitte also states that deploying predictive analytics and digital-twin technologies reduces maintenance costs by around 20-30%.

Additionally, National Grid (UK) is working with Open Climate Fix to apply AI-based nowcasting of solar generation for grid operators to manage renewable inputs more dynamically.

Siemens Energy also offers cloud-based platforms and distributed energy resource (DER) management tools for enabling decentralized control of microgrids and other grid-edge systems.

Retail & Consumer: AI Integration to Increase Revenue by up to 8%

McKinsey estimates that generative AI could create between USD 400 billion and USD 660 billion in annual value for the retail and consumer-packaged goods sector. It also states that AI-powered capability has the potential to enhance customer satisfaction by 15% to 20% and increase revenue by 5% to 8%.

Another source states that AI-powered personalization allows businesses to increase their customer lifetime value (CLV) by up to 20%.

Also, McKinsey’s analysis states that advanced tech-enabled checkout, talent management, merchandising and replenishment, and store environment maintenance allow the grocery industry to create distinctive in-store experiences for customers and reduce costs by as much as 15% to 30%.

Further, Microsoft reports that retailers using digital twins reduce inventory levels, improve store operations, and increase product availability. They achieve these outcomes through store layout simulations, what-if planning, and faster responses to changing conditions.

Public Sector & Infrastructure: AI to Prevent 15% of Projected Natural Disaster Losses

The OECD’s report shows that member countries average a Digital Government Index (DGI) score of 0.61 on a 0-1 scale. This score establishes the current level of digital-government maturity and serves as a baseline for measuring progress toward 2030 goals.

According to the OECD, to meet climate and development objectives globally, an annual investment of USD 6.9 trillion in sustainable infrastructure will be needed by 2030.

 

Growing risks and the utility of AI to enhance infrastructure resilience

Credit: Deloitte

 

AI applications such as predictive maintenance and digital twins could prevent 15% of projected natural disaster losses to power grids, water systems, and transportation infrastructure. These technologies may generate up to USD 70 billion in global savings by 2050.

Also, according to McKinsey, closing the productivity gap in construction could open about USD 1.6 trillion in global value annually.

It also estimates that addressing global infrastructure needs will require roughly USD 106 trillion in investment across seven major infrastructure verticals by 2040.

Additionally, technologies such as AI, robotics, and digital workflows may enable productivity improvements of 31% by 2030 in the construction sector.

Logistics & Supply Chain: AI to Reduce Distribution Costs by up to 20%

Gartner projects that by 2030, 70% of large organizations will adopt AI-based supply chain forecasting platforms. These systems will eliminate manual input and enable near-instant demand response.

Advanced analytics harnesses AI in distribution operations to reduce costs by 15-20%. The technology provides visibility into factors influencing employee attraction, attrition, and performance. It also recommends targeted logistics and supply chain actions to improve retention and talent development.

Moreover, Amazon announced that its Lab126 device unit has formed a new research group focused on agentic AI for warehouse robots. The group is developing robots that understand natural language commands and perform multi-task operations such as unloading trailers and retrieving parts.

Further, the digital transformation spending in logistics is expected to increase from USD 57.2 billion in 2023 to nearly USD 106.95 billion by 2030, at a CAGR of 9.35%.

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