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Posts tagged ‘AI agents’

Aug 5

Gartner’s $5.95 trillion AI forecast puts the chatbot era on a 2027 deadline

Spending on the chatbots and assistants embedded in enterprise software peaks at $272.6 billion in 2027 and then shrinks every year through 2030. Gartner buried that projection inside the 2Q26 update of its worldwide AI spending forecast, published July 24, and it matters more than the headline total. By 2030, embedded chatbot spending falls back to $205.8 billion, a hair above its 2025 starting point.

Embedded agenticAI takes the money instead, growing from $88.2 billion in 2025 to $1 trillion by 2030, an 11.4x expansion inside a single software category.

None of that slows the topline. Worldwide AI spending reaches $2.67 trillion in 2026, up 49.5% from 2025, on its way to $5.95 trillion by 2030. The figure Gartner published in May was $2.59 trillion for this year. Ninety days later, the client-facing number runs $74.8 billion higher, and the firm added $496.8 billion to its comparable 2025 through 2030 outlook in a single quarter.

Four tables below show where the money lands, which segments stall, and what Gartner changed its mind about between April and July.

Where $5.95 trillion lands

Infrastructure stays the biggest line through 2030 at $2.79 trillion, even as its share of total spending slides from 55% in 2025 to 46.9% at the end of the window. AI-optimized servers alone reach $981.7 billion by 2030, a 3.4x jump from 2025, and AI processing semiconductors add another $656.4 billion. O

One caution before quoting the total anywhere. Gartner’s note flags the forecast as a view across the whole AI value chain, so the chip and the server it ships inside both get counted. Read $5.95 trillion as the size of the AI economy, not as net end-user budgets.

Devices carry more of the infrastructure number than most readers expect. Business and consumer AI devices combine for $904.4 billion in 2030, and $647.4 billion of that is consumer hardware, the AI PCs and phones landing in shopping carts rather than data centers.

Growth flattens fast after next year. Total spending rises 49% in 2026 and 36% in 2027, then steps down to 21%, 18% and 15% through 2030, while infrastructure decelerates from 51% growth this year to 9% at the end of the forecast.

Of the $883.8 billion in net-new AI spending arriving in 2026, infrastructure absorbs $502.5 billion, or 57 cents of every new dollar. The shape of the curve says the buildout peaks now and software inherits the growth.

Farther down the board, AI cybersecurity at $220.9 billion and AI agents and assistants at $219.9 billion finish 2030 within $1 billion of each other. Gartner created the agents category only this quarter.

Agentic AI crosses $1 trillion inside enterprise software

Gartner rebuilt its segmentation this quarter, splitting cross-functional and consumer agents out of AI software and adding consumer agents to the forecast for the first time. The new structure exposes a replacement cycle the old rollup hid. Inside enterprise software, agentic AI overtakes chatbots in 2027, the same year chatbot spending tops out, and from that peak to 2030 the embedded chatbot line surrenders $66.8 billion.

Cross-functional agents, the ones that work across software from multiple vendors, start from a base of zero. Gartner books $347 million for cross-functional agentic AI in 2026 and $78.1 billion in 2030, a number it raised this quarter on the thesis that these agents begin cannibalizing traditional SaaS by decade’s end. The ceiling matters as much as the curve. Against $1.21 trillion in total 2030 AI software spending, $78.1 billion says the incumbents hold the decade, because data access, integration complexity and execution reliability hold the category back from serious SaaS competition until 2030, in Gartner’s read.

The buy-versus-build verdict is just as lopsided. Agent builder platforms, the tooling for constructing your own agents, reach only $12.6 billion by 2030, so embedded agentic AI outspends them nearly 80 to 1. The first production agent most companies run will ship inside software they already own. Gartner describes exactly that race, with vendors across software categories embedding agentic AI to defend their installed bases against cross-functional challengers.

Consumer agents barely register yet in dollar terms. Gartner carries $26.9 million for consumer agentic AI in 2026, then $17.7 billion in 2027 as paid consumer agents arrive at scale, building to $51.8 billion by 2030. Adding consumer agents and assistants lifted Gartner’s 2030 total by $133 billion. For how these agent numbers stack against other analyst estimates, see my roundup of agentic AI forecasts and market estimates, 2026.

AI security expands 8.5x and splits in two

AI cybersecurity grows from $25.9 billion in 2025 to $220.9 billion in 2030, an 8.5x expansion at a 53.5% compound rate. Spending in the category grew 140% in 2025, and Gartner models another 98% jump this year. AI cybersecurity and AI data were also the only two markets left completely untouched between the 1Q26 and 2Q26 forecasts, which makes security the steadiest conviction in the entire model. For the standalone security spending outlook, see my breakdown of Gartner’s 2Q26 information security forecast.

Two markets move at different speeds inside the category. AI-amplified security, meaning AI capability inside security tooling, carries the volume and reaches $204.5 billion by 2030. Securing AI, the discipline of protecting AI systems themselves, runs smaller and faster, from $1.5 billion in 2025 to $16.4 billion in 2030 at a 60.4% compound rate.

Set the security numbers against the agent forecast and an exposure gap opens. The 2030 outlook has enterprises running $1.08 trillion of embedded and cross-functional agentic software while spending $16.4 billion to secure AI systems, roughly $66 of agentic software for every $1 of securing-AI budget. AI observability and governance tooling adds just $3.9 billion more. A software wave that large riding on a security ratio that thin is the budget argument CISOs should be starting now.

The fastest growth goes to whatever cuts the bill

Rank every segment by compound growth and one pattern jumps out, because the fastest-growing lines are the ones that make AI cheaper. Synthetic data generation leads the entire forecast at a 142.5% compound rate, expanding 84x from $146 million in 2025 to $12.2 billion in 2030. AI-ready datasets, the licensed real-data alternative, peak at $583 million in 2029 and then decline, leaving synthetic data outselling licensed data 22 to 1 by 2030. Gartner is forecasting the substitution of purchased data itself.

Domain-specific language models tell the same cost story at larger scale. DSLMs and specialized models grow 210% in 2026 and compound at 84.5% through 2030, rising from 12.2% of all model spend to 24.3%. Cost pressure also explains the strangest revision in the update. Gartner cut $57.8 billion in cumulative dollars from its generative AI model forecast while raising the segment’s 2026 growth rate from 110% to 117%, which nets out to more deployments running on cheaper models and smaller checks. Arunasree Cheparthi, a senior principal research analyst at Gartner and one of the forecast’s authors, said in the firm’s July 20 platforms and models announcement that spending “is shifting toward providers who can demonstrate clear value.”

What Gartner changed in 90 days

Between the April forecast and this one, Gartner added $500.8 billion to AI infrastructure across the 2025 through 2030 window, the largest revision in the update, and did it while flagging memory-related price increases. The note calls infrastructure demand inelastic to that pricing pressure, because hyperscalers keep buying AI-optimized servers on the conviction that model capabilities improve through 2030.

Software took the other side of the trade. AI software gained $191.9 billion and application development platforms picked up $10.7 billion. Gartner lifted the 2026 app-dev growth rate from 28% to 39% as enterprises build custom AI applications and demand usage tracking to prove the spend. The cuts land on everything that resembles consulting or plumbing. AI services lost $80.7 billion across the window, with every single year revised down, and platforms for data science and machine learning lost $68.2 billion, including an 8% cut to 2027 alone.

The services cut hides a structural shift rather than a retreat. Gartner still sizes AI services at $1.25 trillion in 2030, but the growth belongs to indirect services, which compound at 34.5% and pass direct, consulting-led engagements in 2028. Direct AI services compound at 15.7%, less than half the indirect rate. Buyers are routing transformation budgets through software and cloud purchases instead of billable hours.

Three dates to plan against

2027 is the year chatbot spending tops out and agentic AI takes over inside enterprise software, which gives any vendor still selling assistant-branded features through the end of next year to ride what growth remains. By 2028, indirect services pass consulting-led engagements and infrastructure growth drops to 15%, so the buildout stops flattering everyone’s numbers. And 2030 arrives with $1.08 trillion of agentic software guarded by $16.4 billion of securing-AI spend. The first two dates decide where the money goes, and the third decides what happens when it arrives unprotected.

This post is my personal reflection on Gartner’s AI spending research from an industry analyst perspective. It does not represent my employer.

Source: Gartner, Forecast: AI Spending, Worldwide, 2025-2030, 2Q26, Kay Arnott, Jon Erensen, Amarendra, Adrian O’Connell, Arunasree Cheparthi, Naresh Singh, Peter Middleton, Hardeep Singh, Shailendra Upadhyay, Rishi Padhi, 24 July 2026, G00855896.

Feb 10

Top 6 cybersecurity trends from Gartner’s 2026 Security Forecast

Over 57% of employees are using personal GenAI accounts for work. A third of them admit to uploading sensitive data into tools their security teams haven’t approved. Meanwhile, agentic AI is proliferating through no-code platforms and vibe coding, creating attack surfaces most CISOs can’t see, let alone govern. And quantum computing? No longer a 10-year planning horizon. It’s a 2030 action deadline.

Gartner’s Top Trends in Cybersecurity for 2026 report, released February 5, 2026, identifies six forces reshaping how CISOs must operate. These cut across governance, AI adoption, identity, workforce, and cryptographic strategy simultaneously. None of them is incremental.

The trends report lands alongside Gartner’s updated Forecast: Information Security, Worldwide, 2023–2029, 4Q25 (G00843183, December 18, 2025) and the Forecast Analysis: Information Security, Worldwide, 2026 (G00838442, February 5, 2026), which together project global information security spending reaching $244.2 billion in 2026, up 13.3% in current U.S. dollars. I’ve tracked this forecast through multiple quarterly updates. The trajectory keeps steepening. The six trends below explain where that money is going and why.

“Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director Analyst at Gartner. “This demands new approaches to cyber risk management, resilience, and resource allocation.”

The spending backdrop: $244 billion and accelerating

Before getting into the six trends, context matters. Gartner’s 4Q25 forecast shows the three major security segments all growing at double-digit constant currency rates in 2026:

Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183). Constant currency rates.

Cloud security remains the fastest-growing subsegment at 28.8% growth in 2026. Nothing else comes close. The combined cloud security market (cloud security posture management, cloud access security brokers, and cloud workload protection platforms) is projected to reach $32.4 billion by 2029, with a 25% CAGR in constant currency. I’ve been watching this subsegment accelerate for three quarters straight. CSPM alone is growing at a 31.30% CAGR.

 

Cloud security spending reaches $32.4 billion by 2029. CSPM leads at 31.30% CAGR. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

Trend 1: Agentic AI demands cybersecurity oversight

This is the trend that touches everything else on this list. Employees and developers are deploying AI agents through no-code/low-code platforms and “vibe coding” at a pace that outstrips security governance. Unmanaged AI agent proliferation. Unsecured code. Compliance violations that most security teams don’t even have visibility into yet. That’s the picture Gartner is painting.

Gartner’s recommendation is blunt: cybersecurity leaders must identify both sanctioned and unsanctioned AI agents operating within their environments, enforce access controls and data guardrails, and develop incident response playbooks specific to agent-driven threats.

“While AI agents and automation tools are becoming increasingly accessible and practical for organizations to adopt, strategic cybersecurity planning for these technologies is essential,” said Michaels. Cybersecurity leaders must work cross-functionally to manage agentic AI adoption, identifying sanctioned and unsanctioned AI agents, enforcing data access controls, and developing incident response playbooks.

The spending data backs this up. Gartner’s 4Q25 forecast projects the AI-amplified security market reaching $160 billion by 2029, up from $49 billion in 2025. Gartner is clear that this isn’t additive spending. It represents the portion of existing security products that now embed AI capabilities. But the expectation tells the story: over 75% of enterprises will use AI-amplified cybersecurity products by 2028, up from less than 25% in 2025. Vendors that don’t embed AI will lose shelf space. (For more on AI security platforms, see Gartner’s Top Strategic Technology Trends for 2026, which predicts that over 50% of enterprises will use AI security platforms to protect their AI investments by 2028.)

Trend 2: Global regulatory volatility drives cyber resilience efforts

Regulators are getting personal. Boards and executives now face direct liability for compliance failures. Not just organizational fines, but individual accountability. The penalties for inaction have moved from theoretical to career-ending. Across multiple jurisdictions simultaneously.

Gartner advises cybersecurity leaders to formalize collaboration across legal, business, and procurement teams to establish clear accountability for cyber risk. Align control frameworks to recognized standards. Address data sovereignty concerns before they become enforcement actions. The organizations doing this well are treating regulatory preparedness as a core security function, not an annual compliance checkbox.

This is where the spending data gets interesting. Gartner’s forecast shows security consulting services growing from $24.2 billion (2024) to $36.6 billion (2029), adding $12.4 billion in five years. Security professional services follow a similar trajectory: $27.3 billion to $40.8 billion, adding $13.5 billion. Organizations are buying outside expertise because they can’t build regulatory competence fast enough in-house. I’ve been covering these numbers for three quarters, and the services growth is the part of the forecast that keeps surprising me.

Infrastructure protection adds $26.4 billion between 2024 and 2029, the largest absolute growth of any subsegment. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

Trend 3: Post-quantum computing moves into action plans

Gartner predicts advances in quantum computing will render the asymmetric cryptography that organizations rely on unsafe by 2030. Four years. That’s the window to adopt post-quantum cryptography alternatives before “harvest now, decrypt later” attacks start cashing in on data that adversaries are collecting today.

Organizations need to identify their cryptographic deployments, assess data sensitivity and lifespan, and prioritize cryptographic agility. That last phrase keeps coming up in my conversations with CISOs. The ability to swap encryption methods without re-architecting entire systems. Swapping an algorithm is one thing. Doing it across a production environment without downtime is an entirely different problem.

“Post-quantum cryptography is reshaping cybersecurity strategies by prompting organizations to identify, manage, and replace traditional encryption methods, while prioritizing cryptographic agility,” said Michaels. “By investing in these capabilities and prioritizing migration now, assets will be secured when quantum threats become a reality.“

The encryption market in Gartner’s 4Q25 forecast grows from $1.04 billion in 2023 to $2.04 billion by 2029 at an 11.95% CAGR. A 2.0x increase. For what has historically been one of the slower-growing security subsegments, that’s a significant acceleration. Quantum urgency is changing the math.

Trend 4: Identity and access management adapts to AI agents

AI agents are breaking traditional IAM models. Plain and simple. Identity registration and governance, credential automation, and policy-driven authorization weren’t designed for autonomous machine actors that can initiate actions, access data, and interact with systems without human intervention. The scale problem compounds fast: when every employee can deploy dozens of AI agents, the identity surface area explodes.

Gartner recommends a targeted, risk-based approach. Invest where gaps and risks are greatest. Leverage automation where possible. The practical starting point is understanding which AI agents carry the most privilege and the least oversight. Those are your highest-risk identities right now, and most organizations haven’t inventoried them.

The identity market is already significant. Gartner’s 4Q25 forecast shows identity access management growing from $18.7 billion (2024) to $29.0 billion (2029), adding $10.3 billion in five years. That’s before the full scale of agentic AI identity requirements hits the market. IAM vendors that solve machine-actor identity at scale will capture a disproportionate share of that $10.3 billion growth.

Trend 5: AI-driven SOC solutions destabilize operational norms

AI-enabled security operations centers are enhancing alert triage and investigation workflows. The technology works. But deploying AI into a SOC doesn’t automatically reduce headcount needs. It changes the skill mix. Analysts who excelled at manual triage need different capabilities to oversee AI-driven workflows. Organizations are discovering this the hard way. That’s an organizational transformation challenge, and throwing more technology at it doesn’t help.

“To realize the full potential of AI in security operations, cybersecurity leaders must prioritize people as much as technology,” said Michaels. “Strengthening workforce capabilities, implementing human-in-the-loop frameworks into AI-supported processes and aligning adoption with clear strategic objectives will be critical to maintaining resilience as SOCs evolve.”

The talent dimension makes this harder than it already sounds. ISC2’s 2024 Cybersecurity Workforce Study, published in October 2024, documented a global workforce gap of 4.8 million professionals, a 19% year-over-year increase. The active workforce flatlined at 5.5 million (up just 0.1%). The numbers are brutal: 25% of organizations reported cybersecurity layoffs in 2024. 37% faced budget cuts. 90% report skills shortages. 58% believe the shortage puts their organization at significant risk. On the spending side, managed security services are growing at 11.1% in 2026, the fastest rate in the services segment. Organizations can’t hire fast enough, so they’re buying managed SOC capacity instead.

Trend 6: GenAI breaks traditional cybersecurity awareness tactics

Existing security awareness programs are failing. Full stop. A Gartner survey of 175 employees conducted between May and November 2025 found that 57% use personal GenAI accounts for work purposes, while 33% admit to uploading sensitive information to tools their organizations haven’t sanctioned. Those numbers should alarm every CISO reading this. A third of your workforce is actively feeding proprietary data into tools you can’t audit.

Gartner recommends shifting from general awareness training to adaptive behavioral programs that include AI-specific tasks. Generic compliance videos won’t cut it here. The organizations getting this right are making approved GenAI tools easy to access and unsanctioned tools hard to justify. Trying to ban GenAI outright just drives usage underground and costs you talent.

Strengthening governance, embedding secure practices, and establishing clear policies for authorized GenAI use will reduce exposure to privacy breaches and intellectual property loss. The governance gap on GenAI usage is, in my view, the most underestimated risk on this entire list. Every other trend has a spending line item attached to it. This one requires behavioral change, which is harder to buy.

Total market trajectory: $173.5 billion to $323.5 billion

Gartner’s year-by-year spending trajectory shows the acceleration curve these six trends are riding:

Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183, December 18, 2025). Current U.S. dollars.

 

CSPM and CASB lead all security categories with 31% and 26% CAGR through 2029. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

What this means for CISOs

Three of the six trends (agentic AI oversight, IAM for machine actors, and GenAI awareness) are fundamentally about the same problem: autonomous AI systems operating inside enterprise environments without adequate governance. The other three (regulatory volatility, post-quantum readiness, and AI-driven SOCs) are the structural forces those governance failures will collide with. That convergence is the signal about where 2026 budgets need to go.

The organizations that will navigate this environment successfully are doing three things simultaneously:

Mapping their AI agent footprint now. If you don’t know how many AI agents are operating across your environment, sanctioned and unsanctioned, you can’t govern what you can’t see. Gartner’s 75% AI-amplified product adoption projection by 2028 means this window for establishing control is narrow.

Building cryptographic agility into their architecture. The 2030 quantum deadline means migration planning starts in 2026, not 2028. The encryption market’s 2.0x growth reflects early movers. Late movers face rip-and-replace costs that compound every quarter they wait.

Investing in people alongside AI tooling. AI-enabled SOCs work when human operators have the skills to oversee them. The ISC2 data is unambiguous: a 4.8 million professional gap growing at 19% year-over-year. Managed security services growth at 11.1% tells you where CISOs are finding capacity.

Gartner’s numbers aren’t projections anymore. They’re procurement trends already hitting finance systems. The $244.2 billion flowing into information security this year will fund agentic AI governance, quantum migration, and SOC transformation, whether your organization participates or not.

Bottom line: CISOs planning for 2027 are watching their competitors buy the tools they’ll be scrambling for in 18 months. The data says move now.

Sep 15

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

 

Eighty percent of enterprises have increased their investment in GenAI over the past year, with nearly one-quarter (24%) now integrating the technology into their operations, up from just 6% in the previous year.

Capgemini Research Institute’s recent report, Harnessing the value of generative AI: 2nd edition: Top use cases across sectors, highlights enterprises’ accelerating pace of GenAI adoption and growing importance across their operations and industries.

“Generative AI is not just a technological innovation; it’s a catalyst for transformative change across multiple sectors, driving productivity gains, operational efficiency, and strategic shifts in business models,” write the report’s authors. Enterprise leaders’ sentiment underscores the increasing recognition of GenAI as a critical technology for staying competitive in an increasingly turbulent economic environment.

According to the report, GenAI’s rapid adoption across IT and marketing indicates that companies are actively integrating it into their core operations to drive tangible, measurable benefits. Capgemini’s findings highlight the need for a strong data governance framework, strategic talent development, and vigilant cybersecurity to maximize GenAI’s potential as companies scale their initiatives.

How enterprises are maximizing GenAI’s value

Capgemini found ten key ways enterprises are positioning themselves to maximize GenAI’s potential. These strategies demonstrate how all companies can potentially invest in and integrate GenAI to boost growth, efficiency, and innovation across departments and industries.

Investment surge reflects growing confidence in GenAI. 89% of large businesses with annual revenues over $20 billion are leading this investment surge, highlighting GenAI’s significance for future growth. Additionally, 73% of companies with revenues between $1 billion and $5 billion have significantly increased their GenAI budgets, showing that this trend is not limited to the largest companies. This investment trend indicates that many companies believe GenAI can drive enterprise evolution and deliver substantial returns, with many expecting double-digit productivity and customer engagement growth.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI maturity grows steadily across industries. GenAI implementations have continued to mature across industry sectors over the past year. Up from 6% in 2023, 18% of organizations will fully integrate GenAI into most or all functions in 2024. With 64% and 53% of companies enabling GenAI, high-tech and financial services lead. Retail grew 17%–40% and industrial manufacturing 14%–35%. With 53% and 47% of telecom and energy/utilities adopting GenAI, respectively, progress has been made.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI’s integration across organizational functions is growing. In one year, GenAI IT adoption rose from 4% to 27% across organizational functions. GenAI is improving enterprise productivity and innovation through this broad integration across sales, marketing, operations, and R&D. Capgemini also found that GenAI is transforming operations and creating value across all business areas.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Productivity and customer engagement gains. Over the last year, organizations that have implemented GenAI have reported a 7.8% increase in productivity and a 6.7% increase in customer engagement. These tangible benefits demonstrate GenAI’s ability to provide real, measurable value to enterprises. Early adopters report significant improvements in key performance metrics, highlighting the strategic importance of incorporating GenAI into business operations.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Small Language Models (SLM) are gaining momentum. 24% of organizations have implemented SLMs, and 56% plan to do so within three years. These models are cheaper and less computationally intensive than larger AI models, so many companies are piloting and eventually moving them into production. SLMs excel in industry-specific applications, allowing businesses to harness AI’s potential without the infrastructure and resource demands of larger models. SLMs are becoming a good option for companies trying to compete in an AI-driven market as they seek efficient and scalable AI solutions.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI is enabling enterprises to advance from chatbots to autonomous multi-agent systems. GenAI is helping 62% of organizations upgrade from chatbots to AI agents that autonomously manage complex goals. 48% of users use multi-agent systems, where AI agents operate independently in changing environments. As businesses automate and optimize complex processes with these systems, decision-making and operational efficiency improve across industries. AI has evolved from simple user interactions to complex, agentic use cases, as shown in the image.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI agents are accelerating the shift to autonomous operations. GenAI agents are increasingly used in enterprise automation, with 82% of companies planning to implement them in 1–3 years. These agents are evolving from supportive tools to autonomous entities that can perform complex tasks without interaction. This shift is significant, with 71% of organizations expecting AI agents to automate workflows and 64% expecting customer service and productivity improvements. AI agents are not just efficient; they are a radical shift toward fully autonomous, AI-driven operations that will transform enterprise productivity and strategic decision-making.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI is forcing major business strategy shifts. 54% of companies expect GenAI to improve their strategies, up from 39% in 2023. 40% of companies are revising their business models to stay competitive as GenAI becomes more important. As GenAI becomes more important, 74% of businesses believe they must use it to grow revenue and stay ahead of the competition.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Strengthening data foundations is crucial for GenAI’s success. More than 60% of companies realize GenAI’s potential depends on solid data foundations. Only 51% have documented data integration processes and 46% have AI data management policies. Even enterprises that have adopted GenAI still struggle to make the most of all their external data sources. Capgemini makes it clear that for GenAI initiatives to succeed, companies need scalable, secure data infrastructure.

Tighten AI controls or risk trust and compliance. Ethics in AI deployment is a priority for 57% of organizations, which recognize the need for control mechanisms that can flex and adapt as their business goals change. While 46% have clear AI governance frameworks, 73% agree that human oversight is necessary to validate AI-driven decisions. Without strong governance, bias, and accountability issues could counteract GenAI’s benefits, so organizations must act now.

Conclusion

With 80% of organizations increasing their investment and almost a quarter already including it in their operations, GenAI is fast changing how businesses run. This general acceptance emphasizes GenAI’s importance as a main engine of efficiency and creativity, providing real advantages in customer interaction and output.

Organizations are not only embracing GenAI as AI agents and Small Language Models (SLMs) acquire traction; they are also including GenAI in their basic strategies. Those who match GenAI with their business models, make investments in solid data foundations, and develop the knowledge required to maximize its possibilities will inherit the future. They will lead in the era of artificial intelligence by doing this, establishing new benchmarks for operational excellence and creativity.

Jul 7

GenAI and IoT security are core to Forrester's top 10 emerging technologies in 2024
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GenAI and IoT security are core to Forrester’s top 10 emerging technologies in 2024

Predicting that generative AI (genAI) for visual content, genAI for language, TuringBots, and IoT security will be the four technologies that deliver the most immediate ROI in two years, Forrester’s Top 10 Emerging Technologies In 2024 reflects the urgency more businesses have for making AI pay while securing their most at-risk endpoints.

Rounding out Forrester’s ten emerging technologies are AI agents, autonomous mobility, edge intelligence, quantum security, extended reality (XR), and Zero Trust Edge (ZTE).

Forrester’s stack ranking of technologies by ROI potential

Advising clients to include ten emerging technologies on their radar and roadmap, Forrester has segmented them into short-term, medium-, and long-term groups based on their potential to deliver ROI. Three of the ten emerging technologies are cybersecurity related.

Technologies predicted to deliver the most significant ROI over the next two years

GenAI for visual content and language. Given how quickly genAI’s adoption is accelerating across enterprises via a myriad of cloud-based apps and tools, especially in marketing, digital design, and communications, it’s clear why Forrester predicted that genAI for visual content, genAI for language have the potential to deliver ROI in two years. Forrester notes that “genAI for language is already delivering value in customer support and content creation but continues to advance at a blinding pace. It is accelerating many other technologies as it goes.”

TuringBots are predicted to accelerate app development. The report states that these AI-powered software robots “help developers build applications that deliver more than just code generation” thanks to advancements in genAI for language. TuringBots are defined as “AI-powered software that augments application development teams’ automation and semiautonomous capabilities to plan, analyze, design, code, test, deliver, and deploy while providing assistive intelligence on code, development processes, and applications.”

IoT Security to secure the proliferating number and variety of endpoint devices. Forrester defines IoT security technology as including components that are “familiar to endpoint management and security: asset management, identity and access management (IAM), data security management, Zero Trust networking, and attack surface risk management.” Forrester predicts that deploying IoT security solutions will deliver expected business value within a year as vendors increasingly offer capabilities as part of other cybersecurity platforms.

GenAI and IoT security are core to Forrester's top 10 emerging technologies in 2024

Source: Forrester’s Top 10 Emerging Technologies In 2024

Emerging technologies predicted to deliver ROI in two to five years

AI agents. Forrester is seeing AI agent technology stacks include advanced deep learning techniques, including generative, predictive, and reinforcement learning, that enable greater context, analysis, strategy, and planning. Forrester believes their full realization is two to five years away, predicting that “organizations with large amounts of information and sizable human workforces will likely see the biggest and most immediate benefits.”

Autonomous mobility. Manufacturing and logistics are two industries shifting workloads from initial pilots into production, according to Forrester. Both industries are facing continued labor shortages, regulatory pressures, and rising costs and see the potential to improve traffic and supply chain management results. Key benefits include greater operational efficiencies across shop floors, improved regulatory compliance, enhanced worker productivity and safety, and more accurate data to track environmental sustainability efforts.

Edge intelligence. Edge intelligence, according to Forrester, is “the ability to collect data, make assumptions based on that data, and link that data to relevant, distributed, orchestrated, and contextually driven responses in a network of application, device, and communication ecosystems.” The report further defines the tech stack for edge intelligence as including streaming analytics, edge ML, federated ML, and real-time data management on intelligent devices and edge servers.

Quantum security. Reducing the risk of “harvest now, decrypt later” quantum attacks, providing increased cryptographic agility for the future, and improving digital signatures are a few of the many benefits quantum security delivers. Asymmetric and symmetric key generation, symmetric key distribution via QKD, digital signatures and certificate management, and keeping an accurate list of cryptographic algorithms are some of the most common uses. These benefits and use cases form the basis of Forrestter’s assigning quantum security into the mid-segment of their stack ranking.

GenAI and IoT security are core to Forrester's top 10 emerging technologies in 2024

Source: Forrester’s Top 10 Emerging Technologies In 2024

Emerging technologies predicted to deliver ROI in over five years

Extended reality (XR). Forrester defines XR as “a technology that overlays computer imagery on a user’s field of vision, with augmented reality (AR), mixed reality, and virtual reality (VR) technologies that are supported by the same developer tools, sensors, cameras, and simulation engines.” Their report notes that only 8% of US online adults own a virtual-reality headset, and just 16% have used an augmented-reality device or app. While XR is advancing in training and onboarding, companies are resisting investing in tools like these until they see broad adoption.

Zero Trust Edge (ZTE). ZTE technology has the potential to protect remote workers, retail outlets, and branch offices with embedded local security. Highly distributed enterprises with little variation between sites are predicted to see the greatest benefit first.

Conclusion

Forrester sees security as core to any organization seeking to maximize the value and ROI of emerging technologies.

Three cybersecurity technologies, IoT security, quantum security, and zero trust edge (ZTE)—form the foundation of the ten emerging technologies. “The inclusion of these security technologies underscores a crucial point: the future belongs to those with the foresight and will to invest in security now. As AI capabilities expand, so do the potential vulnerabilities that malicious actors can exploit,” writes Brian Hopkins, vice president, emerging tech portfolio at Forrester.

Defending endpoints need to start with a zero-trust framework that enforces least privileged access and monitors everything happening on the network while also enabling microsegmentation to reduce the blast radius of a potential cyberattack. Relying on legacy account and identity and access management (IAM) systems that assume trust across systems and within identity management data structures is a breach waiting to happen.

Forrester’s top ten emerging technologies show a progression from already having significant use cases and adoption to newer technologies that are nascent in the market. All share a common characteristic with security, however. As technologies get more complex and remain unproven, security technologies need to step up the use of new technologies to counter threats. Quantum security and zero trust edge correspond with the direction of the ten emerging technologies. They reflect the need to keep improving security to protect the best ROI possible with new technologies on the horizon.