Six tech trends shaping 2026

Consumer AI faces strategic importance amid economic headwinds

The consumer AI market presents a complex investment landscape where investors may focus on platforms with established distribution networks over pure-play AI assistants. Companies with existing advertising platforms and commerce infrastructure that can monetize AI demand regardless of the front-end interface stand to benefit.

Current market dynamics reveal challenging unit economics for consumer AI products. This economic reality reduces the likelihood that large language models will completely disintermediate existing search, social media, and marketplace businesses.

The anticipated launch of screenless devices in 2026 and accelerated agentic browsing capabilities pose potential risks to traditional traffic patterns. However, these developments also highlight the need for sustainable economic partnerships, as the cost of fulfilling open-ended queries at scale on proprietary hardware would likely worsen underlying margin challenges.

"Investment approaches in consumer AI may be less about pure-play assistants and more focused on platforms with durable distribution, ad platforms and commerce rails that can charge for AI demand regardless of front-end interface."

Brad Erickson, Internet Analyst, RBC Capital Markets

Enterprise AI adoption reaches critical inflection point

Enterprise AI is expected to transition from board-level discussions and pilot programs to demonstrable return on investment gains in 2026. This shift represents a market recalibration favoring practical implementations and quantifiable value over generalized capabilities.

The past year revealed important insights about enterprise AI maturity. Despite significant capital deployment, adoption has faced persistent challenges including integration complexity, data governance issues, and proof-of-concepts that struggle to scale in production environments. Organizations investing in AI code generation, retrieval-augmented generation systems, fine-tuning pipelines, and agentic AI architectures have begun seeing meaningful productivity improvements.

Looking ahead, enterprise AI spending is expected to accelerate, with growth shifting from infrastructure investment toward application-layer solutions. Production use cases are expected to center on customer service automation, financial process optimization, supply-chain forecasting, and documentation analysis.

"We believe enterprise AI adoption will reach an inflection point in 2026, transitioning from board-level conversations and pilot-stage experimentation to measurable enterprise ROI gains."

Matt Hedberg, Head of Global TIMT Research, RBC Capital Markets

AI transformation pressures software margins in near term

The impact of AI on software profitability involves a complex four-stage evolution. Initially, companies may see margin expansion through internal AI adoption, reducing research and development intensity and support costs. However, as organizations develop AI-native products, they face a dual challenge of increased inferencing costs and higher research and development spending.

"We could see gross margins that are meaningfully lower, but result in meaningfully higher gross margin dollars and, more importantly, profit dollars."

Rishi Jaluria, Software Analyst, RBC Capital Markets

The transition from traditional cloud solutions to AI-native platforms introduces pricing model complexity. Many software companies must evolve from seat-based subscription models to consumption-oriented structures, creating potential revenue recognition volatility. Despite near-term margin pressure, the long-term outlook suggests meaningfully higher profit dollars, similar to the historical transition from on-premise to software-as-a-service models.

In the steady-state scenario, with AI representing the vast majority of the business, AI-native software companies may achieve gross margins that are lower than current levels but generate substantially higher revenue per customer, potentially commanding pricing premiums of approximately 2-3 times current levels based on efficiency gains and new revenue opportunities.

Investment timeframes extend as AI reshapes return calculations

AI is fundamentally changing how companies and investors evaluate return on invested capital timeframes. The traditional payback period mindset is shifting toward longer-term platform durability assessments spanning multiple product cycles. This evolution particularly affects mega-cap technology companies using strong current growth to fund unprecedented infrastructure investments.

The largest technology platforms are tracking toward several hundred billion dollars in annual infrastructure spending, with cumulative datacenter investments projected in the low-to-mid single-digit trillions through the decade's end. While return on invested capital is technically compressing in the near term, these companies are positioning for multiple waves of AI monetization across advertising, cloud services, productivity tools, agents and vertical applications.

The central uncertainty revolves around the useful life of AI infrastructure, particularly graphics processing units. These components are showing signals of faster depreciation than legacy systems, though they can potentially be redeployed for lower-intensity workloads.

Cybersecurity remains resilient amid AI transformation

Despite widespread AI adoption across industries, cybersecurity is expected to maintain its critical importance and strong growth trajectory. 2026 is anticipated to mark a fundamental shift where AI becomes integral to both cyber-attacks and cyber-defense simultaneously. This creates a market bifurcation between vendors capable of operationalizing AI-native security stacks and those relying on legacy point solutions.

Threat actors are increasingly using AI to automate reconnaissance, accelerate lateral movement, and generate sophisticated social engineering attacks. Traditional signature-based and rule-driven security architectures are proving inadequate against modern AI-driven attack vectors. Organizations require modern cybersecurity platforms that can defend against AI-based threats while protecting sensitive data used in AI deployments.

Cybersecurity spending growth is expected to significantly outpace overall IT spending in 2026, driven by operational imperatives and increased regulatory pressure following high-profile breaches. Vendors that can embed AI throughout their product portfolio and consolidators able to close the skills gap via AI and automated workflows are positioned to benefit from platform consolidation cycles.

Proprietary data commands premium as AI reshapes information landscape

The AI revolution is creating a fundamental divide between companies with proprietary data assets and those relying on publicly available information. Organizations with unique datasets, established benchmarks, and consumption-based enterprise contracts are positioned to command premium valuations as AI enhances monetization opportunities.

The introduction of Model Context Protocol connectors has dramatically reduced data integration timeframes from months to minutes, potentially increasing competition by lowering barriers for smaller data providers. However, companies with domain expertise and specialized toolsets maintain competitive advantages through their ability to deliver clean, contextualized datasets.

"Companies with proprietary data and benchmarks, along with consumption-based enterprise contracts, will continue to command a premium as GenAI helps improve monetization opportunities through Agentic AI capabilities."

Ashish Sabadra, Business, Education and Professional Services Analyst, RBC Capital Markets

Data providers are increasingly structuring their offerings in AI-native formats to remain competitive, while monetization models shift from traditional fixed-cost contracts to flexible consumption-based pricing structures.

The relationship between AI developers and data providers remains symbiotic, as large language models still require specialized expertise to break down complex, domain-specific challenges into manageable components. This dynamic supports continued partnerships rather than complete disintermediation.