Bridging the reality gap

There is a widening disconnect between innovation potential and implementation capability. Organizations face critical decisions between partnerships, acquisitions, or organic development to address capability gaps in rapidly evolving technology landscapes – the build-vs-buy dilemma.

Automotive

“OEMs are redefining smart cockpit strategies by bridging the build vs buy divide.”

Elaine Chung, Senior Analyst, Smart Cockpit

The smart cockpit transformation is exposing a widening gap between innovation ambition and execution capability.

Emerging OEMs lead in AI and architecture innovation but now face the challenge of scaling through disciplined partnerships. Legacy OEMs, conversely, are racing to reclaim digital ownership after decades of supplier dependence, balancing collaboration with internal capability building. Across both groups, strategic clarity on what to build and what to buy has become the defining factor of competitiveness.

Ultimately, success will hinge on strategic focus across critical domains. This means owning cockpit OS, AI frameworks, and data ecosystems that define differentiation, while partnering on compute, connectivity, and cloud platforms to accelerate scale. Those who master this balance will shape the next era of intelligent mobility.

This research is taken from:

Advanced Automotive Intelligence Service

Manufacturing Tech

“Global manufacturing in 2026 is poised for growth, driven by technology investments, reshoring, and supply chain realignment—but a clear reality gap persists between ambition and execution.”

Joanne Goh, Senior Research Manager, Manufacturing Technology

Export-heavy economies face slower momentum, and supply chain diversification exposes gaps in coordination, agility, and operational continuity.

Software-defined automation, physical AI, and humanoid robotics promise efficiency and workforce augmentation, yet adoption is uneven, highlighting the gap between technological potential and real-world implementation.

Hub countries gain influence, and local vendors compete on service and ecosystem integration, but the industry must bridge organizational, digital, and logistical gaps to realize fully intelligent, resilient manufacturing networks.

SDA: The technology is ready, but the operational environment is not. Adoption remains slow and pilot-driven because infrastructure, organizational readiness, and capital expenditure models lag vendor ambitions.

Process industries have the mindset and architecture to move first, while discrete sectors face legacy constraints. Proprietary ecosystems dominate even as end users signal a shift toward open, interoperable architectures. The result is a widening divide between what factories could achieve with software-defined control and what today’s fragmented, aging manufacturing landscape can realistically support.

This research is taken from:

Digital Factory Intelligence Service

Physical Security

"Advanced AI-powered video analytics, cloud-based security platforms, and integrated command center technologies demonstrate significant capability but face adoption barriers including privacy concerns, integration complexity, and unclear ROI that slow mainstream deployment beyond early adopter segments."

Paul Bremner, Principal Analyst, Physical Security

The physical security industry showcases substantial innovation across AI-powered video analytics for proactive threat detection, cloud-based unified security platforms, integrated command center technologies, and advanced access control solutions with biometric authentication and mobile credentials.

This research is taken from:

Command and Control Rooms - 2025

Semiconductors

“A mixed bag of strong infrastructure investment growth and advanced technology headlines amid lagging consumer adoption."

Mark Watson, Senior Research Director, Semiconductors

However, translating these technological capabilities into widespread commercial adoption faces significant barriers that extend far beyond technical performance metrics. These include privacy and civil liberties concerns with AI surveillance, a lack of technical expertise to evaluate AI performance, and integration requirements with legacy security and building systems.

The reality gap proves particularly evident in retail, healthcare, and education sectors where budget constraints, privacy sensitivities, and operational complexity slow adoption of proven AI video analytics and cloud security platforms.

Bridging this gap requires strategic decisions about phased technology deployment balancing on-premises and cloud architectures, partnership models with security integrators possessing AI and IT networking expertise, and build-versus-buy approaches for video management platforms.

From huge investments to circular partnerships, AI continues to drive revolutionary growth in the semiconductor sector. The memory markets for example, including DRAM, NOR and NAND, are being reshaped by changing architecture and usage demands across different AI and edge applications. Server revenue growth is forecast nearly 15% CAGR through 2029 , primarily fueled by AI infrastructure demand, which in turn is driving strong high-bandwidth memory (HBM) demand.

Advanced packaging technologies like 2.5D integration and 3D stacking are also becoming critical for AI performance requirements. However, a significant adoption gap persists, with the industry questioning when AI investments will yield returns.

Smart Building

"Smart building innovation continues to outpace adoption, as organizational readiness, integration complexity, and ROI uncertainty limit the path from advanced capability to real world deployment.”

Ivy Sun, Research Manager, Smart Building

Innovation in smart building technology continues to advance faster than the industry’s ability to deploy it effectively. While the potential of AI analytics, digital twins, and service-based management platforms is widely acknowledged, most organizations remain constrained by legacy infrastructure and limited operational readiness.

By the end of 2025, over 90% of building owners plan to invest in intelligent automation, yet fewer than 5% have achieved full integration across HVAC, lighting, and security platforms.

The challenge is no longer access to advanced technology but the capability to implement, integrate, and sustain it. Bridging this reality gap requires not only a coordinated focus on interoperability, but also workforce upskilling and cross-vendor collaboration to convert digital ambition into measurable building performance.

AI demand is leveling off from the explosive 2023-2024 expansion, creating uncertainty about ROI timelines. Smartphone revenue is projected to grow modestly as compelling AI use cases haven't yet stimulated significant replacement demand, highlighting the technology-adoption disconnect.

This research is taken from:

DRAM Memory Intelligence Service

This research is taken from:

Smart Buildings Intelligence Service

Smart Grid

"Utilities are collecting billions of data points but struggling to convert that data into real-time operational value, making managed services the fastest path to bridging the analytics gap.”

Ozge Celik Russell, Senior Analyst, Smart Building and Energy

Advanced metering infrastructure 2.0 smart meter deployments and the flood of data from grid-edge devices like sensors, monitoring equipment, and distributed generation assets have created a “reality gap”. Utilities possess the data potential but lack the internal IT and AI capabilities to implement real-time, complex use cases (like predictive outage detection or transformer load optimization). To bridge this gap quickly, utilities are increasingly shifting from a “build” approach (developing in-house data science teams and custom platforms) to a “buy” model.

Managed services allow them to rapidly acquire advanced analytics, AI-ready data governance, and cybersecurity support as an operational expenditure, enabling accelerated value realization without the steep upfront investment and long-term talent burden.

This research is taken from:

Smart Utility Meter Intelligence Service