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Feb 26

Roundup of agentic AI forecasts and market estimates, 2026

Roundup of agentic AI forecasts and market estimates, 2026

Agentic AI spending is projected to reach $201.9 billion in 2026 (Gartner), overtaking chatbot spending by 2027.  Four independent firms size the standalone market at $7–8 billion with 40%+ CAGRs. But adoption lags the money: only 23% of organizations have scaled agent deployments (McKinsey), and 40% of projects face cancellation by 2027 (Gartner).

Fortune Business Insights projects $7.29 billion in 2025, reaching $139.19 billion by 2034 at 40.5% CAGR. Precedence Research sizes it at $7.55 billion in 2025, growing to $199.05 billion by 2034 at 43.84% CAGR. MarketsandMarkets puts the figure at $7.06 billion in 2025, reaching $93.20 billion by 2032 at 44.6% CAGR. Deloitte’s TMT Predictions 2025 estimates $8.5 billion in 2026, growing to $35 to $45 billion by 2030.

Every major forecast agrees on direction. None agrees on scale. The standalone agentic AI market lands between $7 billion and $8.5 billion. Gartner’s broader view, counting agentic capabilities embedded across enterprise software, reaches $201.9 billion in 2026. That 25x gap is not a contradiction. It is a measurement problem, and the takeaways below reflect both realities. The following are the key takeaways from agentic AI forecasts published in 2026 so far:

Key takeaways

Worldwide AI spending will reach $2.52 trillion in 2026, growing 44% year-over-year. That number jumped roughly $500 billion from the September forecast, which had pegged the market just above $2 trillion. Infrastructure takes $1.37 trillion, 54% of total spend. AI software follows at $452.5 billion, up 60%. AI services add $588.6 billion. AI-optimized servers alone account for $421.6 billion, growing to 49%. Gartner expects spending to grow by another 30% in 2027 and surpass $3 trillion. I have tracked these forecasts through multiple iterations. The revisions keep going in one direction. Source: Gartner press release, January 15, 2026

 

Gartner projects $4.71 trillion in global AI spending by 2029. The fastest growth isn’t in infrastructure. Synthetic data generation leads all categories at 178% CAGR, followed by the broader AI Data market at 155%. Agentic AI compounds at 119%, expanding from $15 billion to $753 billion by 2029. AI Infrastructure, the largest category by dollars, grows at just 29%. The money is following the bottlenecks. Source:  Gartner 4Q25: $4.71T AI Market Proves Agentic AI and Data Readiness Are the Only Race That Matters, Software Strategies Blog, January 22, 2026 Link: https://softwarestrategiesblog.com/2026/01/22/gartner-4q25-agentic-ai-data-readiness-4-71t-market/

 

The AI cybersecurity market is predicted to hit $51.3 billion in 2026, nearly doubling from $25.9 billion in 2025. But the category masks a structural imbalance. AI-amplified security, where AI defends the enterprise, captures 94.5% of spending at $48.5 billion. Securing AI, where the enterprise defends its own AI systems, gets $2.8 billion. Enterprises are investing 17x more in using AI as a security tool than in protecting the AI itself. Both sub-segments grow at similar CAGRs (74% vs. 72%), which means the dollar gap widens every year. By 2029, AI-amplified security reaches $160.4 billion, while securing AI hits just $11.6 billion. One is a tool. The other is the thing that needs protecting. Source: Gartner Forecasts Agentic AI Will Overtake Chatbot Spending by 2027, Software Strategies Blog, February 16, 2026 Link: https://softwarestrategiesblog.com/2026/02/16/gartner-forecasts-agentic-ai-overtakes-chatbot-spending-2027/

 

AI Data sits alone in the upper-right quadrant of Gartner’s spending map, compounding at 155% CAGR with 277% growth in 2026. AI Cybersecurity and AI Models cluster above 67% CAGR. AI Infrastructure anchors the chart as the largest bubble, but grows at just 29%. Global AI spending reaches $1.8 trillion in 2025 and $4.7 trillion by 2029. The acceleration is not in compute. It is in data readiness, security architecture, and agentic capabilities. By 2028, software with agentic capabilities crosses 50% of total application software spend, up from 2% in 2024. Non-agentic software spending starts declining in 2027. Source:Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Gartner’s AI spending forecast reaches $2.53 trillion in 2026 and $4.71 trillion by 2029. Eight markets. One pattern. AI Infrastructure dominates absolute dollars at $1.37 trillion in 2026 but grows at just 29% CAGR. AI Data, the smallest segment at $3.1 billion, compounds at 155%. AI Cybersecurity nearly doubles to $51.3 billion. AI Software hits $452.5 billion, growing 60% year-over-year as agentic capabilities reshape the category. The growth rates tell you where the bottlenecks are breaking. Source: Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Nearly nine in ten organizations now use AI in at least one business function, up from 78% a year ago, but nearly two-thirds have not begun scaling it across the enterprise. Only 6% qualify as high performers where AI contributes more than 5% to EBIT. Sixty-two percent of organizations are at least experimenting with AI agents, yet in no individual business function are more than 10% scaling them. High performers are three times more likely than peers to fundamentally redesign workflows and three times more likely to have senior leaders demonstrating ownership of AI initiatives. More than one-third of high performers commit over 20% of their digital budgets to AI, and about three-quarters have reached the scaling phase, versus one-third of other organizations. Source: McKinsey / QuantumBlack, The state of AI in 2025: Agents, innovation, and transformation, November 2025

Valued at $638.23 billion in 2024, the global AI market is projected to reach $3,680.47 billion by 2034, expanding to a CAGR of 19.20%. North America holds 31.80% market share. The software segment dominates at 51.40%, while machine learning leads by technology at 36.70%. Healthcare is expected to record the highest CAGR of 36.50% across end-use segments. Among regions, Asia-Pacific is expected to grow at 19.8% CAGR from 2025 to 2034, with AI projected to add up to $3 trillion to the region’s GDP by 2030, driven by national AI strategies in China, India, and Japan. Source: Precedence Research, AI Market Size, Growth & Trends, September 2025

Nearly $7 trillion. That’s the capital outlay data centers will require by 2030 to keep pace with demand for compute power. Of that, $5.2 trillion goes toward AI-ready facilities and $1.5 trillion toward traditional IT workloads. Global demand for data center capacity could almost triple by 2030, with about 70% of new demand coming from AI workloads. Three investment scenarios range from $3.7 trillion (constrained demand) to $7.9 trillion (accelerated demand, adding 205 incremental GW). The 60% majority of investment—$3.1 trillion—flows to technology developers and designers producing chips and computing hardware. Source: McKinsey, The cost of compute: A $7 trillion race to scale data centers, April 2025

Inference already consumed half of all AI compute in 2025. That number will grow to two-thirds in 2026 and reach 75% of all AI compute needs by 2030. Global data center capacity is projected to nearly double from 103 gigawatts to 200 GW by 2030, yet U.S. data centers already face a capacity shortfall exceeding 11 GW, with the cumulative gap expected to exceed 40 GW by 2028. North American data center capacity alone will increase eightfold, from 5.6 GW in 2024 to 44 GW by 2030. Operators are increasingly deploying edge facilities closer to end users to reduce latency as inference-dominated workloads drive a fundamental redesign of data center architectures. Source: Avid Solutions, 13 Data Center Growth Projections, January 2026

 

Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to the global economy, increasing the projected impact of all AI by 15 to 40%. About 75% of the value falls across four areas: customer operations, marketing and sales, software engineering, and R&D. Half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045—roughly a decade earlier than previously estimated. When embedding effects in existing software are included, the total economic benefit rises to $6.1 trillion to $7.9 trillion annually. Source: McKinsey, The economic potential of generative AI, June 2023

The global AI market hit $294.16 billion in 2025 and is projected to grow to $2,480.05 billion by 2034, at a CAGR of 26.60%. The Banking, financial services and insurance (BFSI) segment holds 18.90% market share, while healthcare is expected to record the highest CAGR of 36.50%. In the U.S. alone, the AI market was estimated at $146.09 billion in 2024 and is predicted to reach $851.46 billion by 2034. The number of AI companies funded globally in 2024 totaled 2,049, with U.S.-funded companies accounting for 1,143, signaling strong investor confidence in the sector’s expansion potential. Source: Fortune Business Insights, AI Market Size, Growth & Trends by 2034

Big Tech’s AI capex hit $405 billion in 2025, up from a $250 billion estimate at the start of the year. Sell-side analysts have underestimated AI spending every quarter for two years running. A decade ago, Big Tech’s trailing-twelve-month capex was $24 billion—15x less than today. AI data center costs are projected at $3 trillion to $8 trillion, with gigawatt capacity expected to grow 3.5x by 2030. Source: IO Fund, Big Tech’s $405B Bet, November 2025

The global AI market was valued at $371.71 billion in 2025 and is projected to reach $2,407.02 billion by 2032, growing at a CAGR of 30.6%. Hyperscalers accounted for 53% of chip purchases in 2023, spurring 156% market growth from 2023 to 2024. While demand from hyperscalers is expected to moderate, growth of 41% is still forecast from 2025 to 2026. Enterprises are moving from cloud reliance to in-house AI infrastructure investments, particularly for cost-effective inference solutions, as edge AI gains traction through AI-enabled PCs and mobile devices. Source: Markets and Markets, AI Market Report 2025-2032

At $602 billion projected for 2026, hyperscaler capex has entered uncharted territory. Amazon, Microsoft, Google, and Meta will each exceed $100 billion individually, pushing capital intensity to 45-57% of revenue. Total hyperscaler capex from 2025-2027 is projected at $1.15 trillion, more than double the $477 billion spent from 2022-2024. Morgan Stanley and JP Morgan suggest the technology sector may need to issue $1.5 trillion in new debt over the next few years to finance AI infrastructure construction. The sheer scale of debt issuance mirrors patterns seen during the fiber-optic buildout of the late 1990s. Source: Multiple sources compiled by Introl, January 2026

The number of software companies using consumption-based pricing more than doubled between 2015 and 2024, as AI introduces new variable costs that make traditional perpetual licenses obsolete. SaaS remains dominant, but the next wave is outcome-aligned pricing that scales with actual AI usage. Software businesses that successfully adopt consumption-based pricing aligned with usage and outcomes may be better positioned to capture AI-driven value and differentiate themselves in a rapidly evolving market where the cost of each AI inference adds a new variable to the P&L. Source: McKinsey, AI adjusts the software bill, January 27, 2026

Data center capacity needs for AI and non-AI workloads could almost triple by 2030, with AI capacity increasing 3.5 times and making up roughly 70% of the total. Under a continued-momentum scenario, total capacity demand rises from 82 GW in 2025 to 219 GW by 2030, with incremental AI capacity ranging from 13 GW in 2025 to 31 GW in 2030, totaling 124 GW of new AI capacity. Non-AI workloads grow from 38 GW to 64 GW over the same period. Average power densities in AI-ready data centers have more than doubled in just two years and are expected to rise nearly four times by 2027. Source: McKinsey, Data center demands (Week in Charts), May 2025

U.S. data-center spending exceeded half a trillion dollars in 2025. The U.S. and China drove a massive expansion in AI-related computing capacity through 2024, with the U.S. pulling further ahead in the first half of 2025. AI-related trade accounted for nearly half of all merchandise trade growth in that period, despite representing only 15% of total trade volume. The infrastructure boom is reshaping international commerce, with surging demand for servers, graphics cards, and related components essential to AI training and inference now a dominant force in global supply chains. Source: Federal Reserve Board, FEDS Notes: The Global Trade Effects of the AI Infrastructure Boom, February 2026

The generative AI market is expanding from $71.36 billion in 2025 to $890.59 billion by 2032, at a CAGR of 43.4%. North America accounted for 43.05% of global revenue in 2025. Text remains the dominant data modality due to its foundational role in enterprise workflows, while the services segment is gaining traction for scalability and cost-effectiveness. Foundation model delivery platforms verticalized adoption across industries, and the rapid scaling of AI-native infrastructure are the three key forces driving the market as of 2025. The 43.4% CAGR makes this one of the fastest-expanding technology subsegments in history. Source: MarketsandMarkets, Generative AI Market Report, Global Forecast to 2032

The generative AI market reached $37.89 billion in 2025 and is projected to hit $1.2 trillion by 2035, a 37% compound annual growth rate. Transformer architectures account for more than 42% of technology revenue, driven by text-to-image and text-to-video applications. Software captures over 65% of total revenue. North America holds 41% of the market. Asia-Pacific is the fastest-growing region at a 27.6% CAGR through 2035. Financial services is expected to lead sector growth at 36.4%, fueled by fraud detection, risk management, and regulatory compliance demands. Source: Precedence Research, Generative AI Market Size, January 2026

GPUs captured 89% of AI processor revenue in 2025, but FPGA and ASIC alternatives are growing at a 17% CAGR through 2031. Hardware accounted for 68% of all AI infrastructure spending last year. North America held 40% of the market, backed by $52.7 billion in CHIPS Act grants and hyperscalers operating roughly 60% of global AI compute capacity. Liquid cooling reached 18% of AI server racks as power densities crossed 100 kilowatts per rack, the threshold where air cooling fails. Asia-Pacific is projected to grow fastest at 16.4% CAGR through 2031, driven by China’s $50 billion semiconductor fund and $15 billion in hyperscaler commitments across India. Source: Mordor Intelligence, AI Infrastructure Market Size, Trends & Growth Drivers 2031

Nearly one in four Americans has already made a purchase through AI. Morgan Stanley Research estimates agentic shoppers will drive $190 billion to $385 billion in U.S. e-commerce spending by 2030, capturing 10% to 20% of market share. Grocery and consumer packaged goods lead adoption, with 49% of AI-assisted buyers transacting in those categories. AI shopping agent users are projected to reach 126 million by 2030, up from near zero today, while traditional e-commerce users decline from 264 million to 149 million over the same period. Source: Morgan Stanley Research, Agentic Commerce Market Impact Outlook, December 2025 Link: https://www.morganstanley.com/insights/articles/agentic-commerce-market-impact-outlook

Feb 16

1 Comment

Gartner forecasts agentic AI will overtake chatbot spending by 2027

 

Agentic AI spending grows 141% in 2026 to $201.9 billion. By 2027, it will overtake chatbot and assistant spending for the first time. Then chatbot spending starts declining. I’ve tracked Gartner’s AI forecasts through multiple iterations. This crossover changes where security risk concentrates for every security professional reading this.

The crossover is in the segment-level data tables of Gartner’s Forecast: AI Spending, Worldwide, 2024–2029, 4Q25. The headline number is well known: $2.53 trillion in 2026, $4.7 trillion by 2029 at 33% CAGR. The segment breakdowns are not. Eight markets. Nineteen sub-segments. The sub-segment data tells a different story than the top line.

This is Gartner’s first dedicated AI spending forecast, and I’ve been waiting for it. Gartner states that comparisons to previous AI estimates are not meaningful because the scope widened, adding AI cybersecurity, agentic AI as a separate segment from chatbots, AI data technology, and expanded infrastructure coverage. Gartner writes, “This is the first iteration of the forecast on AI spending that Gartner has published. Gartner has significantly expanded and modified its AI forecast coverage. Spending comparisons to previous iterations are therefore not meaningful as the scope has widened. This includes both coverage of new markets and broadened definitions of the types of AI spending that are reflected in some market segments.”

Forrester’s Predictions 2026: Cybersecurity and Risk arrives at the same warning from a different angle: an agentic AI deployment will cause a publicly disclosed breach in 2026, leading to employee dismissals. Two firms. Same conclusion. The spending data explains why.

CAPTION: Total worldwide AI spending, 2024–2029. $1.14T to $4.71T. 33% CAGR. Growth decelerates from 54% (2025) to 16% (2029) as the base expands. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).

The full market breakdown

AI infrastructure dominates at $1.37 trillion, 54% of the total. AI software follows at $452.5 billion, growing 60% year-over-year. AI services add $588.6 billion. AI cybersecurity and AI data are the outliers: growing at 74% and 155% CAGR, respectively, rates that dwarf everything else in the forecast.

Source: Gartner Forecast: AI Spending, Worldwide, 2024–2029, 4Q25 (December 19, 2025). All figures in U.S. dollars. CAGR = 2024–2029. Gartner press release: https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

Infrastructure takes 54% of every AI dollar

AI-optimized servers alone account for $421.6 billion in 2026, growing to $699.7 billion by 2029. AI processing semiconductors add $289.4 billion. AI-optimized IaaS hits $38.3 billion at 71% CAGR, the fastest-growing infrastructure sub-segment. AI network fabric, a new category in this forecast, reaches $28.7 billion.

Infrastructure’s share drops from 54% to 48% by 2029 as software and services scale faster. The capital-intensive build-out phase is not over.

The agentic crossover nobody is planning for

Gartner now splits AI software into chatbots/assistants and agentic AI. The spending lines cross in 2027.

CAPTION: Agentic AI spending overtakes chatbot/assistant spending by 2027. Chatbots peak at $264.7B then decline. Agentic AI grows at 119% CAGR to $752.7B by 2029. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). AI Software segment, Table 1-2.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.

Chatbots talk to people. Agents act on behalf of people. They access databases, execute transactions, chain multi-step workflows without human approval at each step. The attack surface has moved well beyond conversation windows. Agents are autonomous decision engines with production access.

Gartner’s Top Trends in Cybersecurity for 2026 lists agentic AI oversight as the number-one trend. Forrester’s Predictions 2026: Cybersecurity and Risk goes further: an agentic AI deployment will cause a public breach this year, and employees will lose their jobs for it. Forrester senior analyst Paddy Harrington calls it a “cascade of failures,” not a single point of error. Two analyst firms. Different methodologies. Same conclusion. Security strategies built for chatbot-era risk have a shelf life measured in quarters, not years.

AI cybersecurity is two markets, not one

Gartner created a dedicated AI cybersecurity market for the first time in this forecast. It nearly doubles in 2026. But the category name hides a structural split that matters more than the growth rate.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.

Two sub-segments. Two very different problems.

AI-amplified security ($48.5 billion, 94.5% of the market) is what most enterprises mean when they say “AI cybersecurity.” This is AI working for your security team. Machine learning models that analyze network traffic patterns and flag anomalies faster than a human analyst can. Natural language processing that reads threat intelligence feeds and correlates indicators of compromise across millions of data points in seconds. Automated triage systems that prioritize which of the 11,000 daily alerts actually need a human response. AI-powered endpoint detection that identifies malware variants that signature-based tools miss. Behavioral analytics that learn what normal looks like for each user and flag deviations. Security orchestration platforms that automate incident response playbooks, reducing mean time to containment from hours to minutes.

This is the category where enterprises are spending aggressively. And for good reason. The math on analyst workloads demands it. Security operations centers are drowning in alerts, facing a persistent talent shortage, and defending attack surfaces that expand every quarter. AI-amplified tools address all three.

Securing AI ($2.8 billion, 5.5% of the market) is the other problem. AI-amplified security puts AI to work defending the enterprise. Securing AI reverses the relationship entirely — defending the AI itself. Protecting the models, the training data, the inference pipelines, the agent workflows, and the decision outputs that enterprises are deploying at $2.53 trillion in 2026. Prompt injection defenses. Model access controls. Training data poisoning detection. Output validation. Agent permission boundaries. Audit trails for autonomous decisions.

The distinction matters because they protect different things. AI-amplified security protects your enterprise using AI. Securing AI protects the AI itself. One is a tool. The other is the thing that needs protecting. Enterprises are investing 17 times more in the tool than in protecting the thing the tool runs on.

Shadow AI is not just employees using ChatGPT

Gartner names the mechanism driving AI software growth: vendor push. Software providers are integrating GenAI and agentic AI into existing product lines. AI software grows from $143 billion in 2024 to $981 billion by 2029 at 47% CAGR.

For CISOs, vendor push changes the equation. AI capabilities are being added to tools already in production. Often without explicit procurement decisions. The AI features embedded in your existing ERP, CRM, and developer platforms may already exceed what your security team has inventoried. Shadow AI is vendors activating AI inside products you already own.

The smallest market with the biggest growth rate

AI data technology: $134 million in 2024. $3.1 billion in 2026. $14.6 billion by 2029. The 155% CAGR is the highest in the forecast. The 277% year-over-year growth in 2026 is the steepest single-year jump of any segment.

Synthetic data generation is the standout sub-segment, going from $41 million to $6.8 billion by 2029. Gartner is direct: enterprises need AI-ready data with proper labeling, quality checks, and compliance. For organizations running AI projects on ungoverned data, the readiness gap compounds every quarter.

CAPTION: AI spending markets ranked by five-year CAGR. AI Data (155%) and AI Cybersecurity (74%) lead. AI Infrastructure is the largest by absolute dollars. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).

Indirect services are the governance blind spot

Indirect AI services, where AI is a supporting component in a larger project, grow from $78.4 billion in 2024 to $255.9 billion in 2026 at 50% CAGR. Direct AI services hit $332.8 billion. By 2028, indirect overtakes direct.

Indirect AI means capabilities embedded in consulting and implementation projects that procurement does not classify as AI. If you cannot see it in your AI inventory, you cannot govern it.

Servers are a bigger market than AI software

AI-optimized servers alone hit $421.6 billion in 2026, just below the entire AI software market at $452.5 billion. By 2029, servers reach $699.7 billion. Cloud providers are building capacity for AI workloads that have not materialized at scale. The infrastructure is ahead of the applications.

The enterprise agentic stack is showing up in spending data

Gartner’s DSML segment includes a dedicated agent builder platforms sub-segment at $5.0 billion in 2026, reaching $13.7 billion by 2029. AI observability and governance adds $1.3 billion, growing to $4.0 billion. The xOps sub-segment (MLOps, DataOps, ModelOps) is the largest at $15.0 billion.

Together, these form the tooling layer for building, monitoring, and governing agents in production. The enterprise agentic stack is materializing in the spending data. Most organizations have not formalized it in their architecture.

The numbers that belong in your next board deck

If you take one thing from this forecast into a budget meeting, take this table. I built it from the raw spreadsheet data. Six years of AI deployment spending next to AI security spending. The bottom row is the one that gets the questions.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). All percentages derived from Gartner’s published data tables (Tables 1-1 and 1-2).

The ratio improves over time. Securing AI goes from 0.07% in 2024 to 0.25% by 2029. But watch the absolute numbers. In 2029, enterprises will spend $4.71 trillion deploying AI and $11.6 billion securing it. The percentage gets better. The dollar gap gets wider. Every year, the market grows its way into a larger exposure.

Where I think this lands

Three things worth tracking from the segment data:

The agentic crossover. Agentic AI overtakes chatbot spending in 2027. The enterprise risk profile shifts from conversational data leakage to autonomous decision-making at scale. CISOs who build agentic governance frameworks in 2026 position themselves before the inflection. The spending curve says the window is narrowing.

The securing-AI gap. $2.8 billion to protect AI systems in a year when $2.53 trillion deploys them. Enterprises are enthusiastic about using AI for defense. The investment in defending AI itself has not caught up.

Data readiness is the bottleneck. The 277% growth in AI data spending confirms that AI without governed data delivers diminished returns. Data classification investments directly enable or constrain AI ROI.

If your security budget is growing at 12% and AI deployment inside your enterprise is growing at 44%, the gap compounds every quarter. You cannot close it by holding steady. The organizations getting this right treat AI security as a proportion of AI deployment, not a fixed line item.

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Sources

Gartner, Forecast: AI Spending, Worldwide, 2024–2029, 4Q25, December 19, 2025, ID G00843179.

Gartner press release (January 15, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

Gartner, Top Trends in Cybersecurity for 2026 (February 5, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-05-gartner-identifies-the-top-cybersecurity-trends-for-2026

Gartner, IT Spending Forecast 1Q26 (February 3, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars

Forrester, Predictions 2026: Cybersecurity and Risk (October 2025): https://www.forrester.com/blogs/predictions-2026-cybersecurity-and-risk/

All dollar figures in U.S. dollars. Growth rates and CAGR derived from Gartner’s published data tables (Tables 1-1 and 1-2).

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.

Dec 30

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AI Security market 2025 funding data, top startups, and the ServiceNow factor

ServiceNow dropped $11.6 billion on security acquisitions in 2025 alone. Armis for $7.75 billion. Moveworks for $2.85 billion. Veza for roughly $1 billion. In 2025, just one company, ServiceNow, spent more on acquiring security startups than 175 startups raised in two years. Meanwhile, the entire AI security startup ecosystem raised $8.5 billion across 175 companies over 24 months. That single data point should reshape how security leaders think about vendor consolidation and how AI builders think about their exit paths.

I analyzed Crunchbase data covering every AI security startup that raised Series A, B, or C funding between January 2024 and December 2025. The patterns are striking.

The acceleration is real

Q1 2024: $274 million across 8 deals. Q4 2025: $2.17 billion across 28 deals. That’s 8x growth in quarterly funding over two years.

The full-year numbers tell the story more clearly. 2024 saw $2.16 billion in total funding. 2025 hit $6.34 billion, nearly tripling. Average deal sizes jumped from $34 million to $54 million. This isn’t a gentle upward trend. The market is restructuring in real time.

Where the money flows

Network and Zero Trust infrastructure captured $1.9 billion across 44 companies. Tailscale‘s $161 million Series C reflects what enterprises already know. VPN architectures are dying. Identity-based access is replacing them.

Threat Detection and SOC automation drew $1.2 billion across 28 companies. 7AI‘s $130 million Series A stands out as one of the largest A funding rounds in this category. The bet: AI agents can handle the full security operations lifecycle at a scale human analysts cannot match.

Identity and Access Management pulled $990 million. But here’s what matters: that money went to just 6 companies. Saviynt‘s $700 million Series B dominates the category. When one company captures 71% of a category’s funding at Series B, investors see platform consolidation ahead. ServiceNow’s Veza acquisition, three weeks later, validated that thesis.

Insights into deal sizes

Median tells a different story from average deal sizes. Series A median: $20 million. Series A average: $28 million. The gap widens at later stages. Series C median: $85 million. Series C average: $119 million.

Translation: mega-deals skew the data significantly. Eighteen companies raised $100 million or more. Those 18 deals represent 10% of companies but 40% of total funding. For every Saviynt raising $700 million, dozens of startups are raising $15-25 million Series A rounds.

The AI/LLM security gap

Only 13 companies focus specifically on securing AI systems, LLMs, and agentic applications. Total funding: $414 million. That’s less than 5% of the $8.5 billion total. For context: ServiceNow paid more for Veza alone than the entire AI/LLM security category raised in two years.

The players building in this space:

• Noma Security ($100M, Series B). Unified AI and agent security platform.

• Credo AI ($21M, Series B). AI governance and compliance automation.

• Lakera ($20M, Series A). Real-time GenAI security against LLM vulnerabilities.

• Prompt Security ($18M, Series A). Enterprise generative AI adoption platform.

• GetReal Security ($17.5M, Series A). Deepfake and AI-generated impersonation defense.

• Jericho Security ($15M, Series A). Training against generative AI-powered attacks.

Enterprises are deploying AI systems at unprecedented rates. Shadow AI breaches cost $4.63 million per incident. That’s $670,000 more than standard breaches, according to IBM’s 2025 Cost of a Data Breach Report. Model Context Protocol vulnerabilities. Prompt injection attacks. Data exfiltration through AI assistants. The attack surface expands while protection lags.

Either these 13 companies scale rapidly, established players acquire their way into the space, or CISOs face a protection gap without commercial solutions.

How spending breaks out geographically

The U.S. captured $6.1 billion across 119 companies. That’s 71% of total funding. Israel remains the second hub: 15 companies, $738 million. Germany, the UK, and Canada trail with single-digit percentages.

Within the U.S., California dominates: $2.7 billion across 62 companies. That’s more than all non-U.S. markets combined ($2.4 billion). Texas ($865M), New York ($667M), and Colorado ($295M) round out the top states.

The concentration creates vendor risk. Regulatory fragmentation between the U.S. and EU markets. Geopolitical tensions affecting Israeli companies. Single-region dependency in security infrastructure. These are fundamental considerations for enterprise security architects.

ServiceNow’s acquisitions signal large-scale consolidation

ServiceNow’s 2025 acquisition spree warrants its own analysis. Armis brings cyber-physical security and OT/IoT visibility. Moveworks adds agentic AI capabilities. Veza delivers identity security for the AI era. The company calls it an “AI control tower.” A unified security stack that sees, decides, and acts across the entire technology footprint.

The driver: ServiceNow’s Security and Risk business crossed $1 billion in annual contract value in Q3 2025. They expect Armis alone to triple their market opportunity. When a platform vendor invests $11.6 billion in its own security workflows, point solutions become acquisition targets or competitors.

What this means for 2026

For security leaders: Map your vendor portfolio against both funding momentum and M&A activity. Startups with strong backing will survive consolidation. Others won’t. Audit your AI deployment pipeline against available protections. The gap between AI adoption and AI security is widening. Accelerate zero-trust adoption while solutions mature.

For AI builders: Security isn’t a feature to add later. The $414 million flowing into AI/LLM security represents smart money recognizing that unprotected AI systems are enterprise liabilities. Build with guardrails or build vulnerabilities.

Analysis based on Crunchbase data covering 175 AI security startups that raised Series A, B, or C funding between January 2024 and December 2025. ServiceNow acquisition data from the company’s press releases dated December 2025.

Oct 28

Top ten cybersecurity startups to watch in 2025 according to $3.21B in investor bets

Top Ten Cybersecurity Startups to Watch in 2025 According to $3.21B in Investor Bets

While the industry still debates whether AI will transform cybersecurity, investors have already made up their minds.

Based on an analysis of the latest Crunchbase data compiled recently that spans January 2024 to October 2025, ten standout startups captured $1.41 billion in new funding, signaling that machine-speed defense against AI-driven threats is no longer optional; it’s an operational reality. Together, these ten startups have raised $3.21 billion, which represents one of the heaviest capital concentrations in cybersecurity startups to date.

Investors are gravitating to cybersecurity startups that solve complex problems

CrowdStrike’s Falcon 2025 event, held earlier this year in Las Vegas, showcased a series of new agentic AI developments that, taken together, reflect how cross-platform and cross-competitor collaboration aimed at shutting down increasingly complex weaponized AI threats leads to faster innovation. VentureBeat’s analysis of the many announcements there explains how the cybersecurity company is betting on agentic AI to defeat adversaries.

Interested in quantifying how AI is impacting investors’ decisions, I completed an analysis using Crunchbase data covering 342 verified cybersecurity startups with active funding. Selection was weighted toward recent momentum, total funding scale, stage maturity, AI integration, and proof through multiple rounds.

The key takeaway: Institutional capital is consolidating around companies that make autonomous security practical, and agentic AI is at the core of that direction. But AI is not enough; investors are looking for the ability to scale in enterprises once they have AI integrated into their core platforms.

AI in cybersecurity: Tablestakes, not a ticket to premium valuation

Sixty percent of startups integrate AI into their core technology. Yet contrary to hype, that hasn’t bought them higher valuations.

  • AI-integrated startups average $283M in funding.
  • Non-AI specialists average $378M.

Crunchbase data shows investors reward defensible specialization as much as AI capability. Quantinuum’s $925M for post-quantum cryptography and Zama’s $139M for homomorphic encryption prove that solving foundational security problems often supersedes AI as a differentiator.

Still, AI holds weight in investment decisions. Six AI-driven startups pulled $1.70B (52.8%), while four non-AI companies captured $1.51B (47.2%). Both models earn trust by underscoring AI for operational speed and deep tech for architectural resilience. And with seven of ten now at Series B maturity, investors are backing platforms that have already demonstrated enterprise traction, not experiments.

1. Quantinuum ($925M, Series B) Post-Quantum Defense. Closed a $600M Series B in August 2025. The company is building the only mathematical safeguard against the inevitable collapse of RSA and ECC encryption under quantum computing.

2. Saronic ($845M, Series B) Autonomous Maritime Security, Raised $175M in July 2024 for AI-powered unmanned surface vessels. With 90% of trade moving across exposed waterways, Saronic brings AI defense to the physical infrastructure that most enterprises overlook.

3. Auradine ($314M, Series B) AI Silicon for Security. Raised $80M to expand custom silicon that accelerates cryptographic workloads 10x faster than general-purpose hardware, eliminating bottlenecks in AI-driven security deployments.

4. Tines ($271M, Series B) No-Code Automation. Secured $50M Series B. Turns analysts into automation builders, saving 40+ hours weekly with drag-and-drop workflows that are proving critical for overextended SOC teams.

5. Dream Security ($198M, Series B) Critical Infrastructure Defense. Closed $100M in 2025. Their sovereign AI platform equips critical infrastructure with defenses calibrated to nation-state-level threats, providing a layer that traditional enterprise tools cannot reach.

6. Upwind Security ($180M, Series A)  Runtime Cloud Visibility. Raised $100M in December 2024. Focused on runtime intelligence, detecting abnormal behavior live rather than flagging static misconfigurations. Reduces false positives, elevates real threats.

7. Zama ($139M, Series B)  Homomorphic Encryption. Raised $57M in June 2025 after a $73M Series A in March 2024. Provides production-ready fully homomorphic encryption, enabling AI models to compute securely on encrypted data.

8. Noma Security ($132M, Series B)  Securing AI Agents. Closed $100M in 2025. Built to harden AI systems against prompt injection and model poisoning as enterprises push decision-making into autonomous agents.

9. ZeroEyes ($107M, Series B)  Firearm Detection AI. Raised $53M in 2025. Eleven rounds in, their AI models detect firearms on video feeds in seconds—cutting active shooter response time dramatically.

10. Upscale AI ($100M, Seed)  AI Networking Infrastructure. Raised a $100M Seed round in 2025. Building AI-native networking with hardware-accelerated encryption, aimed at high-performance compute environments.

The Bottom Line

Series B dominance (70%) shows that capital is flowing into platforms with market traction, not speculative bets. Forty-six rounds across these ten companies demonstrate durability and enterprise validation. The signal to security leaders is becoming clear based on the escalating nature of weaponized AI attacks: manual security processes are now liabilities. Defending at human speed against AI-enabled attackers is untenable. Investors understand this. $1.41B in recent capital confirms it.

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.

May 19

1 Comment

Gartner’s 2024 CEO Survey Reveals AI as Top Strategic Priority

Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

75% of CEOs used ChatGPT in the first half of 2023, with 44% incorporating it into their jobs.

Gartner’s 2024 CEO survey finds that CEOs are on board with AI to a much greater extent than previously believed. 87% of CEOs agree that AI’s benefits to their business outweigh its risks. “Digitalization, in general, and AI, in particular, will be core innovative elements in revised business strategies, as will environmental-sustainability-based growth ideas,” writes Gartner in the report.

CEOs experimenting with synthetic video

Almost a third of CEOs have considered making and using a synthetic video of themselves. Gartner notes that Estelle Brachlianoff, CEO of the European utility services company Veolia, has posted an AI-augmented video of herself on LinkedIn and X appearing to speak in multiple languages.

Driving AI adoption

CEOs who adopt new technologies immediately drive their adoption enterprise-wide because everyone immediately sees those technologies as critical to their jobs. Seasoned CEOs know the quickest way to get a new enterprise app’s adoption rate to go up is to use it themselves and demonstrate their mastery quickly. What’s happening with AI’s adoption is faster than many CEOs expected.

Key takeaways from Gartner’s 2024 CEO survey include the following:

  • Growth dominates CEO agendas, reaching a new record in Gartner’s annual survey. “CEOs’ top business priority of growth is up 25% and is at the highest level since 2014,’ writes Financial considerations increased by 25%, cost management by 11%, and customer priorities grew by 22%. The survey points towards CEOs being more focused on profitability and margins, two signs of internal process gains to reduce operating costs and improve efficiency. The survey results point to more CEOs looking at how to get greater returns from the most expensive assets their businesses operate.
75% of CEOs used ChatGPT in the first half of 2023, with 44% incorporating it into their jobs.Gartner's 2024 CEO survey finds that CEOs are on board with AI to a much greater extent than previously believed. 87% of CEOs agree that the benefits of AI to their business outweigh its risks. "Digitalization, in general, and AI, in particular, will be core innovative elements in revised business strategies, as will environmental-sustainability-based growth ideas," writes Gartner in the report. CEOs experimenting with synthetic video Almost a third of CEOs have considered making and using a synthetic video of themselves. Gartner notes that Estelle Brachlianoff, CEO of the European utility services company Veolia, has posted an AI-augmented video of herself on LinkedIn and X appearing to speak in multiple languages. Driving AI adoption CEOs who adopt new technologies immediately drive their adoption enterprise-wide because everyone immediately sees those technologies as critical to their jobs. Seasoned CEOs know the quickest way to get a new enterprise app's adoption rate to go up is to use it themselves and demonstrate their mastery quickly. What's happening with AI's adoption is faster than many CEOs expected. Key takeaways from Gartner's 2024 CEO survey include the following: • Growth dominates CEO agendas, reaching a new record in Gartner's annual survey. "CEOs' top business priority of growth is up 25% and is at the highest level since 2014,' writes Gartner. Financial considerations increased by 25%, cost management by 11%, and customer priorities grew by 22%. The survey points towards CEOs being more focused on profitability and margins, two signs of internal process gains to reduce operating costs and improve efficiency. The survey results point to more CEOs looking at how to get greater returns from the most expensive assets their businesses operate. Ceo growth 1 • CEOs mentioning AI as one of their top two technology priorities jumped from 4% in 2023 to 24% in 2024. Technology innovation also increased from 7% to 11%, and the use of digital transformation for growth increased from 9% to 11%. It's interesting to see how CEOs are focusing on how to improve, integrate, and modernize their strategic use of technology. That category jumps from 1% in 2023 to 5% in 2024. "AI is explicitly mentioned a lot more in 2024 than it was in the 2023 survey. At the same time, mentions of "digitalization" have declined significantly, and so have mentions of e-commerce and omnichannel," writes Gartner. CEO two top strategic business priorities 2 • 34% of CEOs say that the next business transformation their enterprises will pursue after digital is AI. CEO's intentions to pursue AI as their next business transformation are nearly four times greater than their interest in operations efficiency and agility. Sustainability and ESG are a distant third priority. Just 5% of CEOs say customer experience/centricity will be a priority. the theme of the next transformation after digital • 59% say AI is the technology that will most impact their industry. AI has a four-year track record of being the top category, starting in 2020, with the percentage of CEOs mentioning it ranging between 18% to 29%. Gartner mentions in the survey results that in 15 years of asking this question and comparable ones to it, there's never been a category that emerges as dominant as AI has. In the past, CEOs believed cloud and big data technologies would be the most impactful. Previous technologies have had nowhere near the extent of impact that AI does today. "Eighty-six percent of CEOs expect AI will help maintain or grow their revenue in 2024-2025, and when asked exactly how that would happen, the top answer category was an improvement to customer experience and relationships," writes Gartner. Use AI to Help Maintain or Grow Company Revenue

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • CEOs mentioning AI as one of their top two technology priorities jumped from 4% in 2023 to 24% in 2024. Technology innovation also increased from 7% to 11%, and the use of digital transformation for growth increased from 9% to 11%. It’s interesting to see how CEOs are focusing on how to improve, integrate, and modernize their strategic use of technology. That category jumps from 1% in 2023 to 5% in 2024. “AI is explicitly mentioned a lot more in 2024 than it was in the 2023 survey. At the same time, mentions of “digitalization” have declined significantly, as have mentions of e-commerce and omnichannel,” writes Gartner.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • 34% of CEOs say that the next business transformation their enterprises will pursue after digital is AI. CEO’s intentions to pursue AI as their next business transformation are nearly four times greater than their interest in operations efficiency and agility. Sustainability and ESG are a distant third priority. Just 5% of CEOs say customer experience/centricity will be a priority.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

  • 59% say AI is the technology that will most impact their industry. AI has a four-year track record of being the top category, starting in 2020, with the percentage of CEOs mentioning it ranging between 18% to 29%. Gartner mentions in the survey results that in 15 years of asking this question and comparable ones to it, there’s never been a category that emerges as dominant as AI has. In the past, CEOs believed cloud and big data technologies would be the most impactful. Previous technologies have had nowhere near the extent of impact that AI does today. “Eighty-six percent of CEOs expect AI will help maintain or grow their revenue in 2024-2025, and when asked exactly how that would happen, the top answer category was an improvement to customer experience and relationships,” writes Gartner.
Gartner's 2024 CEO Survey Reveals AI as Top Strategic Priority

Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches

Mar 24

BCG Shares Their Insights On What Sets GenAI’s Top Performers Apart

BCG Shares Their Insights On What Sets GenAI's Top Performers Apart

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The top 10% of enterprises have one or more GenAI applications in production at scale across their organizations. 44% of these top-performing organizations are realizing significant value from scaled predictive AI cases. 70% of top performers explicitly tailor their GenAI projects to create measurable value.

Boston Consulting Group (BCG) estimates that an organization with $20 billion in revenue can achieve gains of $500 million to $1 billion in profit using GenAI, with nearly a third of those gains coming in the first 18 months. Their recent analysis of what sets GenAI’s top performers apart, What GenAI’s Top Performers Do Differently, looks at the factors that most differentiate enterprises excelling with GenAI today.

What further differentiates these top performers from others is how they’re looking to use GenAI to redefine the functional areas of their organizations. They’re far more likely to have a solid foundation in predictive AI and four times more likely to increase their investment in AI and digital-first strategies and technologies.

Half of the enterprise leaders BCG interviewed say their organizations are testing GenAI in pilot projects today but have not achieved full-scale implementation. The remaining 40% haven’t taken any action on GenAI yet.

BCG Shares Their Insights On What Sets GenAI's Top Performers Apart

Source: Boston Consulting Group, What GenAI’s Top Performers Do Differently.

What Sets The Top 10% Apart

Two-thirds of GenAI’s top-performing enterprises aren’t digital natives like Amazon or Google but instead leaders in biopharma, energy, and insurance. BCG found that a U.S.-based energy company launched a GenAI-driven conversational platform to assist frontline technicians, increasing productivity by 7%. A biopharma company is reimagining its R&D function with GenAI and reducing drug discovery timelines by 25%.

Top GenAI performers have their greatest lead over peers across five main capabilities. These capabilities include having a clear link to business performance, modern technology infrastructure, strong data capabilities, leadership support, and a grounding in responsible AI. The steep curves shown in the graphic below suggest how these five most differentiated capabilities are essential for successful GenAI adoption at scale.

BCG Shares Their Insights On What Sets GenAI's Top Performers Apart

Source: Boston Consulting Group, What GenAI’s Top Performers Do Differently.

Key takeaways from BCG’s analysis of GenAI top performers 

Top performers excel at creating strong links between GenAI initiatives and business value. Seven in ten enterprises who are high achievers know how to build a business case for their GenAI projects and pilots. They’re focused on measuring results and quantifying value. BCG found that in a typical GenAI portfolio, 60% of the initiatives are focused on reducing costs and 40% on increasing revenue.

An all-in mindset when it comes to maintaining and growing a modern technology infrastructure. GenAI top-performing enterprises are three times more likely to already have a modular, modern IT tech stack and supporting infrastructure in place. They’re focused on being prepared to develop new, GenAI-powered services on their current and future AI models while supporting DevOps. BCG says top performers are 1.5 times more likely to focus on building the GenAI stack internally over the coming three years, underscoring their desire to make the technology a core capability for the organizations.

Are pursuing and advanced data strategy that includes unstructured data. GenAI top performers are two times more likely to have data pipelines and data management practices in place to streamline data sourcing and storage. They’re also more likely to have unstructured data expertise. BCG observes that an advanced data strategy “is a critical element of GenAI, given that models are only as strong as the data on which they’re trained.”  Organizations have found success with less mature skills in these areas, although it may take longer as they need to address infrastructure and data strategy gaps or shortcomings.

Strong leadership support for innovation, including the willingness to champion GenAI. Senior executives’ support and prioritizing an innovative culture are the most differentiating factors in defining GenAI’s high performers. Gen AI high performers who are scaling use cases are three times more likely than no-action companies to have leaders who prioritize innovation and actively support GenAI. BCG notes that these leaders often have a deep understanding of the technology’s potential impact on their industry, and they are publicly committed to ensuring that the organization capitalizes on it in new ways that generate value. “Visible support and commitment from our leadership team has been crucial, as it provided the freedom to experiment and deal with failures along the way,” said the head of data and analytics at a global media company referenced in BCG’s report.

Have responsible AI guidelines, guardrails and processes in place. Top-performing enterprises are more likely to have responsible AI frameworks, guidelines, and guardrails in place. BCG observes that a common trait top-performing enterprises have is ensuring their AI systems and workflows put humans in the loop and use only factual data. “Our research shows that leading companies are far more likely to have developed guardrails, guidelines, and policies to ensure that they follow the principles of responsible AI. In the findings, the share of scaling companies that are cautious about the potential misuse of GenAI and taking proactive measures to address these risks is 20 percentage points higher than the share of companies taking no action in this area,” write BCG’s researchers.

Feb 11

Top 10 Insights From Forrester’s State of Generative AI in 2024 Report

Top 10 Insights From Forrester's State of Generative AI in 2024 Report

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Over 90% of enterprise AI decision-makers have concrete plans to adopt generative AI (gen AI) for internal and customer-facing use cases. Nearly half, 47%, say productivity gains are the primary goal. Those anticipated gains are closely followed by greater innovation (44%) and cost efficiency (41%). One in three, or 34%, expect AI to deliver greater revenue growth.

Productivity gains are happening faster than enterprises can track because Shadow AI is growing quicker than many IT and security teams anticipated. OpenAI says their enterprise adoption is soaring, with over 80% of Fortune 500 companies’ workers and departments having accounts.

Enterprise workers are achieving a 40% performance boost thanks to ChatGPT based on a recent Harvard study. A second study from MIT discovered that ChatGPT reduced skill inequalities and accelerated document creation times while enabling enterprise workers to be more efficient with their time. Seventy percent haven’t told their bosses about it.

Forrester: A Wave Of Disruption Is Coming

Forrester’s recent report, The State of Generative AI, 2024, warns enterprises not to discount the impact of generative AI on their operations and to start planning for greater experimentation, governance, and security now.

Get ready for the challenges of defining and managing bring your own AI (BYOAI) as workers are inventing and adopting gen AI tools and apps faster than IT or security can keep up. Forrester advises that enterprises need to have forward-thinking governance and security guardrails in place before launching their AI-based apps.

With nearly every enterprise app and platform integrating gen AI into its feature set, it’s up to IT, security, and senior management to have an AI plan that can adapt and flex fast to the fast-growing feature set gen AI is delivering.

Enterprise AI leaders also need to define where they stand on the controversial and complex state of model training data. Forrester says that questions regarding the quality of training data using copyright material, model and data bias, and frequent “hallucinations” by models.

“In 2024, as organizations embrace the generative AI (genAI) imperative, governance and accountability will be a critical component to ensure that AI usage is ethical and does not violate regulatory requirements,” writes Forrester in their cybersecurity predictions late last year. “This will enable organizations to safely transition from experimentation to implementation of new AI-based technologies,” the report continues.

Top 10 Insights From Forrester’s State of Generative AI

The ten most valuable insights from Forrester’s State of Generative AI report provide a comprehensive overview of the current and future landscape of generative AI. Noteworthy for its balance between opportunities and risks, the report explains the hurdles in front of enterprises looking to gain value from gen AI now and in the future.

Here are the top 10 insights from the report:

Gen AI shows strong potential to improve and scale enterprise operations. Forrester is optimistic regarding the potential of gen AI to increase productivity and deliver measurable business value. “Forrester expects that gen AI will add convenience to and remove friction from a variety of experiences, reshape jobs in ways we are only beginning to contemplate and disrupt organizations and industries,” writes Forrester’s research team in the report. The report explains that there are three broad tiers of generative AI suppliers, further supporting the market’s expansion and growth.

Top 10 Insights From Forrester's State of Generative AI in 2024 Report

Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024

 

Large Language Models will continue to dominate the gen AI narrative. Large language models (LLMs), including Anthropic, Google, Meta, and OpenAI’s GPT series, will continue to dominate the gen AI landscape. Forrester notes that significant data and infrastructure requirements make the task of creating and maintaining an LLM difficult for companies considering competing in the market. Open-source LLMs are redefining the market, a point Forrester touches on briefly.

Gen AI is becoming part of planning cycles in enterprises: Forrester found that over 90% of global enterprise AI decision-makers have definite plans to implement generative AI. Internal use cases are dominating the planning cycles of enterprise AI leaders today.

Top 10 Insights From Forrester's State of Generative AI in 2024 Report

Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024

Overcoming technical skills shortages and integration challenges stand in the way of achieving benefits. A third of enterprise AI leaders say that lack of technical skills in their organizations is the single greatest roadblock to their gaining the benefits they’re looking for from gen AI. Twenty-eight percent say they’re having difficulty integrating gen AI into their existing tech stacks and infrastructure. The potential to gain significant benefits from gen AI motivates enterprise AI leaders to look for new ways to overcome technical skills shortages and find new ways to integrate gen AI into their infrastructure.

Top 10 Insights From Forrester's State of Generative AI in 2024 Report

Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024

Ethical and safe gen AI use will require new cybersecurity and governance approaches. Enterprises need to decide how they approach the most challenging and controversial aspects of LLMs, gen AI, and the future of AI at scale in their companies now rather than later. The core message of Forrester’s report is for enterprises not to procrastinate but to start making plans now for their stance on the issues of model bias, data security, regulatory compliance, and ethics of model training.

Weaponized LLMs are a fact of life for every enterprise looking to adopt these technologies today. There’s also the growing threat of intellectual property and confidential data being accidentally shared with LLMs via ChatGPT and comparable chatbots. The intensity cybersecurity providers are putting behind this problem makes it one of the fastest evolving areas in the industry today.

While not mentioned by Forrester in their report, these vendors are defining the state of the art when it comes to protecting confidential data being entered into LLMs. Cisco, Ericom Security by Cradlepoint’s Generative AI isolation, Menlo Security, Nightfall AI,  Wiz, and Zscaler have all developed and launched solutions to reduce the threat of confidential data making it into LLMs via chatbots.

The use of a virtual browser that is separate from an organization’s network environment in the Ericom Cloud distinguishes Ericom’s Generative AI Isolation. Data loss protection, sharing, and access policy controls are applied in the cloud to prevent confidential data, PII, or other sensitive information from being submitted to the LLM and potentially exposed.

“Generative AI websites provide unparalleled productivity enhancements, but organizations must be proactive in addressing the associated risks,” said Gerry Grealish, Vice President of Marketing Ericom Cybersecurity Unit of Cradlepoint. “Our Generative AI Isolation solution empowers businesses to attain the perfect balance, harnessing the potential of generative AI while safeguarding against data loss, malware threats, and legal and compliance challenges.”

Early adopters are implementing gen AI in a wide variety of use cases, ranging from operations to customer engagement and product development. Forrester notes that they’re seeing organizations use knowledge management bots to accelerate workflows, automate tasks, generate new ideas, and drive innovation across a broad base of use cases. Early adopters are also piloting gen AI for diverse use cases across operations, customer engagement, and product development. Forester emphasizes that for external use cases, they’re seeing enterprises adopt a main-in-the-middle workflow to strengthen model training with human intelligence – and avert potential errors in model response.

Top 10 Insights From Forrester's State of Generative AI in 2024 Report

Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024

Internal use cases precede external use cases as enterprises look to gain expertise in controlled environments. Companies will initially focus on internal use cases for generative AI to refine their models before slowly expanding to customer-facing applications, employing heavy human-in-the-loop management. Forrester is seeing gen AI being used internally to drive employee productivity and workflow optimization gains first. The top three use cases of employee productivity, knowledge management, and software development are all focused on internal improvements.

Legal and intellectual property uncertainty will continue to surround gen AI. Forrester implies there’s going to be an increasingly complex, controversial legal landscape regarding the data models are trained on, especially when it comes to copyrighted content.

Privacy and regulatory concerns are going to continue influencing adoption. Forrester is seeing enterprises in heavily regulated industries exercising extreme caution to protect company and customer data, with some even banning tools like ChatGPT over concerns about data protection and regulatory backlash.

Enterprises need to get a sense of urgency about preparing for gen AI governance and experimentation. Having a clear vision of how they’re going to adopt gen AI is essential if enterprises are going to succeed in governing these new technologies at scale. The combination of growing and recruiting in-house skills, having a solid plan for BYOAI, and defining guardrails for internal use cases are all needed to avoid being blindsided by risks.

Jan 24

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

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Predicting global spending on AI software will surge from $124 billion in 2022 to $297 billion in 2027, Gartner forecasts the market will grow at a 19.1% compound annual growth rate in the next six years.

Generative AI (GenAI) software spending is expected to skyrocket from 8% in 2023 to 35% by 2027. GenAI’s rapid growth is attributed to enterprise software vendors integrating AI tools into current and future releases, streamlining the widespread adoption of GenAI-based features and new apps, emerging as the fastest-growing category of AI software.

Gartner predicts the integration of AI tools will be most prevalent in marketing, product design, and customer service, reflecting a shift towards more personalized and efficient operations. The research firm provides an extensive analysis of AI software’s growth areas and opportunities in their recently published report, Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide (client access required). The forecast is based on over 500 AI use cases sourced from Gartner’s AI use case prisms and related research.

What Driving The AI Market Boom?

Key assumptions that are driving the forecast include the prediction that greater than 70% of independent software vendors (ISVs) will have embedded GenAI capabilities in their enterprise applications by 2026, a major jump from fewer than 1% today.

Thirty-nine percent of worldwide organizations will be in the experimentation phase of Gartner’s AI adoption curve by 2025, with 14% being in the expansion phase. Gartner predicts that by 2027, 36% of organizations in the experimentation phase will also start to adopt use cases with high business value but low time-to-financial impact (TOFI).

Another factor driving long-term market growth is how spending on AI software increases with organizational maturity. Gartner notes that organizational maturity is lower today versus the higher hype levels and market interest in AI technologies. As organizations gain greater maturity with AI experimentation, spending will increase.

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

Source: Gartner, Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide

“We expect to see ongoing demand for more AI enhancements within software applications and more opportunities for providers to deliver software to build AI. However, do not expect these markets to become saturated (where supply outstrips demand) during the forecast period,” Gartner’s analysts write in the forecast analysis.

Application areas growing the fastest

Gartner predicts AI spending on financial management system (FMS) components will be the largest application market overall. FMS supports the office of finance with capabilities for forecasting, planning, cash application and collections, balance reconciliation, and others.

FMS vendors are doubling down on AI already to provide proven productivity and optimization support, integrating AI-based features in their apps and platforms. AI’s inherent advantages quickly lead to quantified performance and productivity gains with FMS systems, which are table stakes for building a solid business case for potential customers.

Digital commerce applications are the highest-growth AI application market. Digital commerce applications are designed to streamline commerce operations and related areas, including optimization, customer segmentation, image categorization, and others. Gartner defines the AI capabilities in digital commerce to include personalization, automated execution, and content generation.

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

Source: Gartner, Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide

Predicting Generative AI’s Growth

Gartner is predicting that GenAI will eventually become a cornerstone of all AI software spending, reaching 35% of worldwide revenues by 2027.

The report’s authors point to the proliferation of AI copilots being integrated into a wide variety of enterprise systems as a primary catalyst driving this area of the market’s growth. Copilot systems are in use today within email systems, customer support chatbots, and a wide variety of marketing applications, with content creation and personalization vendors fast-tracking copilots into their apps and platforms.

One of the many data points that support the optimistic growth forecast for GenAI is Microsoft’s success with their Microsoft Dynamics 365 Copilot, launched in March of last year. Since then, more than 130,000 organizations have experienced copilot capabilities in Microsoft Dynamics 365 and Microsoft Power Platform.

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

Source: Gartner, Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide

Large Language Models (LLMs) are the growth catalyst natural language platforms need

Gartner is predicting that the data science and AI platform market will see the greatest amount of software spending in the forecast period. They’ve defined the market as including machine learning (ML) platforms and cloud AI developer services. “The data science and AI platforms market is accelerated by the growth of AI and the democratization of technology, where capabilities like ease of use, workflow, collaboration, and deployment provide support for citizen data scientists,” writes Gartner’s analysts in the report.

The leading AI platforms seeing the greatest growth are natural language technologies (which include LLMs), data science and AI platforms, computer vision platforms, and analytics and BI platforms. LLMs will be the fuel that keeps natural language technology-based platforms growing for the next three years. They’re the emerging workhouses of the AI software market.

Gartner Predicts AI Software Will Grow To $297 Billion By 2027

Source: Gartner, Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide

 

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