Tag: agentic AI

Wealth Management Firms Chase AI Adoption, Yet Most Remain Unprepared

A recent HCLTech study highlights a significant disconnect in the wealth management industry: while nearly all leadership teams are pursuing artificial intelligence, very few have built the systems needed to truly transform their operations. The report, titled Hidden In Pl(AI)n Sight, suggests that firms recognize the need for change but are still far from executing it.

AI Ambitions Versus Agentic Readiness

According to the study, 98% of wealth management leadership teams are actively advancing an AI agenda. However, only slightly more than 7% are developing agentic AI capabilities — systems that can independently perform a sequence of tasks toward a defined goal, rather than simply responding to individual prompts.

While 84% of respondents acknowledged that their operating models require a fundamental redesign to fully leverage AI, fewer than 10% are prepared for such a transition. These figures indicate that although firms understand the magnitude of the work ahead, most are still in the early stages of implementation.

The research was conducted using a synthetic methodology. HCLTech, in collaboration with Evidenza, created 1,066 AI personas modeled on senior wealth management decision-makers across 17 markets. Industry practitioners, researchers, and subject experts helped design and validate the approach, meaning the percentages reflect modeled personas rather than a traditional survey of executives.

Funding Efficiency While Ignoring Revenue Impact

The report identifies three critical gaps between AI plans and business outcomes. First, firms tend to fund projects aimed at operational efficiency even when they acknowledge the need for broader change. Second, technology spending often outpaces investment in proprietary client data and insights. Third, measurement remains a weak point: companies track AI adoption but rarely measure whether these initiatives generate revenue or improve client value.

Although 84% of leaders favor a major redesign of their operating models, only 12% measure the new revenue generated by these changes. Srinivasan Seshadri, HCLTech’s chief growth officer and global head of financial services, emphasized that clarity is essential. “The industry doesn’t have an investment problem. It has a choice problem,” he said.

Executives in the study placed greater importance on client-derived data and behavioral insights than on cloud platforms, technology infrastructure, or AI partnerships as sources of competitive advantage. Nearly 80% also expect future leaders to combine AI with human expertise and external collaboration.

Regional Confidence Varies Widely

The study found notable regional differences in AI readiness confidence. The Asia-Pacific region led with 89% confidence, followed by North America at 84%. Europe trailed significantly at 38.3%. These figures reflect perceived readiness rather than actual deployment of agentic AI systems.

HCLTech suggests that firms must connect AI investments with operational changes and measurable business outcomes. The research offers a snapshot of where modeled decision-makers see gaps today, not a prediction of how quickly those gaps will close.

For wealth managers, the message is clear: adding AI tools is not the same as transforming the work those tools support. The industry’s enthusiasm for AI is evident, but meaningful progress in agentic capabilities remains limited.

November 6, 2026
Snapdragon X2 AI PCs: Five Capabilities Set to Reshape Everyday Computing

Qualcomm’s Snapdragon X2 generation is pushing AI PCs beyond marketing language. The platform pairs dedicated neural processing with efficient CPUs and GPUs, aiming to make AI-assisted work feel more native on laptops. Here are five areas where the new Snapdragon AI PCs are expected to make a difference.

1. More capable on-device AI

The centerpiece is the NPU. Snapdragon X2 Elite processors are listed with up to 85 TOPS of NPU performance in some configurations, while other listings cite 80 TOPS. The NPU works alongside the CPU and GPU, so supported apps can run AI workloads locally instead of sending every request to the cloud. That opens the door to multimodal tasks involving text, images, video, and audio, with faster responses and smoother AI tools when software is optimized.

2. AI agents that can handle multi-step tasks

Snapdragon X2 platforms are built to support agentic AI experiences. Rather than only answering a question, supported applications can break a user’s goal into steps and use available tools to carry out parts of the workflow. Qualcomm has demonstrated native agentic apps for document search, content creation, web research, and workflow automation. The caveat: real-world usefulness will depend on developer support and app integration.

3. Battery life and efficient processing

Performance matters only if a laptop can sustain it. Snapdragon PCs continue to emphasize power efficiency, and Qualcomm says Snapdragon X Series devices can reach up to 22 hours of battery life. Actual mileage will vary by hardware, software, display, and workload. The architecture spreads tasks across processing units, so AI work can use the NPU instead of overloading the CPU. That can help laptops stay responsive while cutting unnecessary power draw.

4. Smarter cameras, audio, and connectivity

AI features are also moving into everyday communication. Snapdragon platforms support AI-enhanced camera and audio processing, which can improve video calls, background effects, voice handling, and related tools. Snapdragon X2 systems include Wi-Fi 7 support and advanced display capabilities, with some configurations able to drive up to three 5K displays at 60Hz. That makes these PCs relevant for mobile productivity and workstation-style setups.

5. Local processing with more privacy

On-device AI can offer more than speed. When suitable tasks are processed locally, less sensitive data has to leave the device. Qualcomm also highlights chip-to-cloud security across Snapdragon X2 platforms. This does not remove the cloud from the picture. Demanding workloads can still use cloud services, while the PC follows a hybrid model: local processing for appropriate tasks and cloud capacity when needed.

What the next Snapdragon AI PCs could mean for users

The next generation of Snapdragon laptops is becoming more AI-focused. Dedicated NPUs are getting faster, and software is catching up. Agentic AI may be one of the most noticeable shifts, moving AI tools from simple Q&A toward workflow assistance. Battery efficiency remains a major advantage for mobile users, and local processing could make some AI experiences faster and more private. Still, hardware specifications alone do not guarantee a better AI experience. The final result will depend on the device, the applications, and how well developers use the platform.

FAQ

What are Snapdragon AI PCs?

They are computers powered by Qualcomm Snapdragon platforms with dedicated AI processing. Their NPUs can handle supported AI workloads alongside the CPU and GPU, enabling local AI features and AI-assisted applications.

What is the Snapdragon X2 Elite NPU?

It is a dedicated neural processing unit for AI workloads. The article cites up to 80 TOPS of NPU performance, with some configurations listed at up to 85 TOPS. Actual capabilities depend on the specific device and software.

What can on-device AI do on Snapdragon PCs?

On-device AI can process supported workloads directly on the computer, including tasks involving text, images, audio, and other content. This can reduce dependence on cloud processing for suitable workloads.

What is agentic AI on a Snapdragon PC?

Agentic AI goes beyond simple question-and-answer interactions. Supported applications can break a user’s goal into multiple steps and use applications and tools to complete parts of that workflow.

Do Snapdragon AI PCs have better battery life?

Snapdragon PCs emphasize power efficiency alongside performance. Qualcomm says Snapdragon X Series devices can deliver up to 22 hours of battery life. Actual battery performance varies by device, software, workload, display, and usage conditions.

November 3, 2026
Qualcomm Unveils Agentic Smartphone Vision: On-Device AI to Automate Tasks Across Apps

Qualcomm has unveiled its ambitious vision for the next generation of smartphones at the Snapdragon Summit 2026 in Maui. The company’s handset chief, Christopher Patrick, and premium mobile leader Dino Bekis introduced the concept of the ‘agentic smartphone’ — a device that acts as an intelligent assistant capable of understanding user intent and autonomously completing multi-step tasks across different applications.

For example, if someone emails you their contact details, instead of manually copying that information into your contacts app and forwarding it, the phone’s AI agent could handle the entire process automatically, only pausing to ask for your approval when necessary.

However, this level of automation raises significant privacy concerns. To function effectively, the AI needs access to personal data such as emails, photos, and app accounts. Qualcomm’s solution is to process as much of this data as possible on the device itself, minimizing reliance on cloud servers. Users would have clear control over permissions, able to grant or revoke access at any time.

The strategy is powered by Qualcomm’s newest processors: the Snapdragon 8 Elite Extreme Gen 6 and the Snapdragon 8 Elite Gen 6. Both chips feature the company’s Oryon CPU and Hexagon NPU, along with a low-power Sensing Hub that continuously monitors context without draining the battery. The Extreme version offers enhanced performance for graphics, camera, and video workloads.

Qualcomm is also collaborating with Liquid AI to develop a system that builds a user profile from device data (with permission), giving AI agents the context they need without constant cloud access. Heavier AI workloads will still be offloaded to the cloud, but the goal is to split tasks based on privacy, speed, and cost considerations.

While the chips are ready, the full realization of this vision depends on smartphone manufacturers and app developers building the necessary permission systems and app connections. Qualcomm has laid the foundation, but the ecosystem must catch up to make the agentic smartphone a reality in everyday life.

November 2, 2026
How to Govern Agentic AI: Identity, Permissions, and Audit Trails
Why Agentic AI Demands a New Approach to Governance

Agentic AI systems don’t just generate text—they plan tasks, invoke tools, and take actions with limited human oversight. This shift from content generation to autonomous execution changes the entire risk landscape. A chatbot that merely answers questions is governed by rules for output accuracy, but an agent that books transactions, writes code, or reroutes payments requires controls over identity, permissions, and auditability. The core challenge: accountability becomes far harder to trace when an AI system acts on its own.

Regulatory and Standards Landscape

Formal law dedicated solely to agentic AI remains scarce. Regulators around the world are still adapting broader AI regulations to these autonomous systems, and the approach varies significantly by region.
- European Union: The EU AI Act provides comprehensive, risk-based regulation. It imposes documentation, transparency, and human oversight duties on high-risk uses, including certain employment and credit decisions—but only when the specific application qualifies, not automatically because the system is agentic.
- United States: No single federal AI law exists. Instead, sectoral rules apply—consumer protection statutes via the FTC, anti-discrimination laws, and state-level initiatives like Colorado’s AI accountability law.
- Standards Bodies: Frameworks like the NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001 are filling legislative gaps. These provide structured processes for mapping, measuring, and managing AI risk, and they offer a certifiable management-system standard. Neither replaces applicable law, but both help organizations demonstrate a credible governance process.
Five Practical Questions for Agentic Compliance

Enterprise governance can be distilled into five actionable inquiries:
1. Identity: Which agent performed which action, under whose authorization, using which credentials, and against which system? This becomes critical when agents interact with ERP, CRM, HR, or finance platforms—an unlogged action can create a compliance gap.
2. Permission Scoping: Apply least-privilege access. An agent should only receive the permissions required for its assigned task. For instance, a customer-service agent might issue refunds under a certain amount but cannot change payment credentials or reset passwords.
3. Audit Trails: Record inputs, outputs, tool calls, permissions, approvals, timestamps, and actions so you can reconstruct events later. When a loan-processing agent denies an application, regulators may ask for the decision’s basis—system logs hold up in review, not the agent’s internal reasoning.
4. Human Checkpoints: Insert a person into high-stakes moments. A financial firm could require human sign-off before an agent executes transactions above a threshold, even if the agent can technically do it alone.
5. Continuity: Governance isn’t a one-time certification. Regularly test for bias before launch, monitor behavior after deployment, and review permissions as the agent’s role expands.
Responsible AI as an Ongoing Discipline

Beyond initial testing, agentic systems need mechanisms to pause, restrict, or disable execution when unexpected behavior is detected. You cannot rely on the agent to recognize its own errors. Scenario testing—running the agent through ambiguous instructions, conflicting goals, unexpected tool responses, and attempts to exceed permissions—is often skipped but essential. Testing only routine tasks leaves harder failure modes undiscovered until they occur in production.

Governance Must Evolve With the Agent

The most difficult governance problem isn’t writing policy—it’s keeping it current as agents become more capable. A rulebook designed for an email-answering agent won’t suffice when the same agent starts negotiating contracts. Organizations that treat governance as a fixed checklist will fall behind. Instead, build review cycles directly into deployment, revisiting identity, permissions, and oversight every time capabilities expand. This proactive approach catches problems before they become incidents.

Why It Matters

Agents now move money, write code, and access sensitive systems without supervision. Without clear accountability, permission limits, and audit trails, errors scale quickly and remain invisible until damage occurs. Strong governance determines whether enterprises can trust and safely deploy these systems.

The central question is no longer whether an AI system produces a safe answer—it’s whether your organization can control, observe, and stop what the system does. Design governance into the agent from its first deployment, with controls built to evolve alongside its permissions, tools, and responsibilities.
November 1, 2026
Agentic AI Transforms Enterprise Workflows: Key Insights from Decimal Point Analytics

AI agents are moving beyond basic automation and reactive interactions to support more complex enterprise workflows. Advances in foundational models and computing power are enabling AI systems to process large volumes of information, interact with multiple data sources, and execute tasks within defined boundaries.

For enterprises, this shift is particularly relevant because employees often spend significant time collecting information from different applications, processing data, and following established workflows. Agentic AI can connect these activities and support faster execution while keeping human review within the process where required.

In this episode of the Analytics Insight Podcast, Priya Dialani speaks with Gaurav Gupta of Decimal Point Analytics to examine how agentic AI is changing enterprise workflows, where organizations can apply these systems, and what businesses need to prepare before deploying them at scale. Here are the excerpts of the interview:

How Does Agentic AI Differ From Generative AI in Enterprise Workflows?

I see generative AI as largely reactive. We ask it a question or give it a prompt, and it provides an answer. It is extremely useful for creating content, summarizing documents, answering questions, and supporting productivity.

Agentic AI adds another dimension. I see it as an extension of generative AI where the system understands an objective, plans the required actions, interacts with different information sources, and completes multiple tasks within defined boundaries.

The system can work with databases and enterprise applications, make decisions based on established rules, and execute one step after another. It can also be designed to return to a human or analyst for confirmation when necessary. That is what makes agentic AI more applicable to complex enterprise workflows.

Which Business Functions Can Benefit Most From Agentic AI?

I believe the greatest impact will come in areas that require large amounts of information to be processed and analyzed. In many organizations, employees have to collect information from 15 or 20 different applications before they can complete a task.

Research and audit are good examples. At Decimal Point Analytics, we have built systems that source information from company filings and calendars, identify quarterly results, download transcripts, and summarize the information. An analyst can then review the output before it is released to customers.

I also see strong applications in risk and compliance. Agents can monitor regulations, compare them with internal policies, identify gaps, and alert compliance teams. Customer service, sales, business development, accounting, and reconciliation can also benefit from these workflows.

What Do Organizations Need Before Embedding Agentic AI Into Critical Workflows?

I would start with enterprise knowledge. An agent needs context to understand how the organization operates. That knowledge may exist in emails, standard operating procedures, internal documents, or even in the experience of managers and employees.

We need to bring that knowledge into structured repositories so the system can use it. I consider this knowledge base an important part of the organization’s intellectual property.

The second requirement is data. We need efficient pipelines that allow agents to access information from ERP, CRM, accounting, HR, and other enterprise systems. We also need clearly defined workflows and standard operating procedures. These foundations are particularly important when agentic AI is introduced into time-sensitive or mission-critical processes.

What Challenges Do Enterprises Face When Implementing Agentic AI?

I think the challenges are closely connected to the foundations required for deployment. Organizations first need to ask whether their data is properly structured and whether their workflows and standard operating procedures are clearly defined.

A lot of important knowledge may still exist outside formal databases. It may need to be organized and converted into a form that an AI system can use effectively.

There are also concerns around confidentiality. Organizations need to understand how their data will be handled and whether sensitive information could be exposed beyond the intended environment. Cost is another consideration because enterprises need to evaluate the economics of operating these systems alongside their technical and data requirements.

How Should Enterprises Approach the Adoption of Agentic AI?

I believe organizations should start considering agentic AI as early as possible because the technology is moving quickly and adoption is already taking place across different organizations.

At the same time, I would not treat deployment as simply adding another software system. The enterprise needs the right knowledge base, data pipelines, workflows, and operating structure first.

Once those pillars are established, agentic systems can be introduced into business processes where they can handle large amounts of information and time-sensitive work. I see the opportunity as moving beyond task automation toward systems that can coordinate multiple steps while still allowing people to review and guide important decisions.

Listen to the full discussion on the Analytics Insight Podcast.

October 30, 2026
Top 7 AI Trends Transforming Business Technology in 2026

Artificial intelligence is moving beyond chatbots and into the core of business operations. In 2026, companies are leveraging AI not just for automation but for autonomous workflows, specialized models, and measurable returns. Here are the seven key trends reshaping business technology this year.

1. Agentic AI Moves Into Business Workflows

AI agents are evolving from simple chatbots to autonomous systems that plan tasks, use business tools, and complete multi-step processes. Organizations are testing multiagent setups where different agents handle research, analysis, approvals, and execution—all under human oversight.

2. Smaller AI Models Drive Enterprise Efficiency

Enterprises are shifting away from relying solely on the largest AI models. Smaller, domain-specific models excel at document processing, customer support, internal search, and invoice extraction. These models reduce costs and latency while giving companies greater control over sensitive data.

3. AI Infrastructure Becomes a Business Priority

The growing demand for AI workloads is pushing companies to invest in robust infrastructure. Combining GPUs, CPUs, specialized chips, cloud systems, and edge computing helps manage latency, privacy, and large data volumes. Local and hybrid setups are gaining traction.

4. AI Strengthens Cybersecurity and Governance

As AI becomes more autonomous, businesses need stronger controls around access, data, and model behavior. AI can detect unusual activity and speed up threat response, while governance systems track agents, permissions, risks, and compliance. Security is now an integral part of AI development.

5. Physical AI Brings Intelligence to the Real World

AI is moving from screens into factories, warehouses, and logistics. Intelligent robots support quality inspection, predictive maintenance, warehouse operations, and manufacturing. This trend bridges AI software with real-world automation.

6. Multimodal and Voice AI Enter Core Operations

AI systems now handle text, images, audio, video, and documents in a single workflow. Businesses use these capabilities for call centers, document processing, field services, compliance, and regional-language support. Voice AI is also becoming more practical for customer and employee interactions.

7. Measurable ROI Defines the Next Phase of AI

Companies are moving from AI experiments to measurable outcomes. They track metrics like cost reduction, faster workflows, productivity gains, revenue impact, and customer experience. In 2026, the focus is on building reliable systems that deliver value at scale.

These trends highlight a shift from experimentation to strategic integration, where AI is embedded into every layer of business technology.

October 30, 2026
Agentic AI Explained: Autonomy, Planning, and a $290 Billion Market by 2035
What Is Agentic AI?

Agentic AI represents a fundamental shift from traditional artificial intelligence. Instead of simply responding to a single prompt, agentic systems set their own path toward a goal, adapt to changing conditions, and complete multi-step tasks with minimal human intervention. This autonomy, combined with planning, memory, and tool use, enables them to pursue complex objectives in dynamic environments.

The global market for agentic AI is projected to grow from USD 8.19 billion in 2025 to USD 290.62 billion by 2035, a compound annual growth rate of 42.88%. This rapid adoption reflects a real change in how enterprises view automation and intelligent systems.

Core Concepts Behind Agentic Systems

Agentic AI is built on several key capabilities:
- Autonomy: Systems operate with minimal oversight once a goal is defined, though humans may still approve high-risk actions.
- Goal-oriented behavior: Large objectives are broken into smaller tasks through task decomposition.
- Reasoning and planning: The system compares options and settles on a workable sequence of actions.
- Decision-making: Actions are chosen based on current context, not fixed scripts.
- Adaptability: The system adjusts when conditions change mid-task.
- Environmental awareness: New inputs are read and plans updated accordingly, crucial in fast-moving fields like finance or healthcare.
- Learning from feedback: Reinforcement learning sharpens future choices through rewards and penalties.
- Tool use: Agents can query databases, run calculations, or trigger business actions beyond their training data.
- Memory: Agents recall earlier steps during long tasks, often enhanced by retrieval-augmented generation (RAG).
The Basic Agentic AI Process

Agentic systems operate on a continuous loop rather than a single exchange. A goal is defined, the system studies the situation, drafts a plan, and takes action. It then checks the results and learns from them, repeating the cycle until the objective is reached. This loop marks the clearest distinction between agentic AI and older, one-shot tools.

The Evolution of Agentic AI

The concept of agentic AI has roots stretching back eight decades:
- Early Foundations (1943-1990s): Neural network theory, the Turing Test, and the Dartmouth conference (1956) set the intellectual groundwork. Shakey the Robot combined perception and planning in the early 1970s. Expert systems, backpropagation, and early reinforcement learning followed through the 1980s and 1990s.
- Deep Learning and Reasoning (2012-2022): AlexNet (2012) demonstrated the power of deep neural networks for perception. DeepMind’s AlphaGo (2016) paired deep learning with reinforcement learning. The Transformer architecture (2017) reshaped language modeling. By 2022, research like ReAct fused reasoning with action, and ChatGPT brought large language models into everyday use.
- The Agentic Era (2023-2026): Toolformer (2023) showed that language models could learn when to call outside tools. AutoGPT and BabyAGI popularized goal decomposition. The Model Context Protocol (2024) gave agents a common way to reach tools and data. Google’s Agent2Agent protocol (2025) allowed agents built on different platforms to communicate. Enterprises are now integrating these capabilities into orchestration, identity, and governance frameworks.
Market Growth and Outlook

According to Analytics Insight’s Global Agentic AI Market Report, the market is expected to surge from USD 8.19 billion in 2025 to USD 290.62 billion by 2035, at a CAGR of 42.88%. This growth reflects enterprise confidence in agentic AI’s ability to deliver real value. However, success will depend not only on model quality but also on reliability, governance, and clean integration. Organizations that master these fundamentals now will be best positioned to capture value as the technology matures.

Frequently Asked Questions

1. What is the main difference between agentic AI and traditional AI?
Traditional AI generally completes one task at a time, such as answering a question or translating text. Agentic AI instead pursues a broader goal, planning steps and adapting to changing conditions on its own.

2. What are AI agents?
AI agents are self-contained software systems designed to gather information, make decisions, and act toward a specific objective. They serve as the individual building blocks that make up larger agentic AI systems and workflows.

3. What core capabilities define agentic AI systems?
Agentic AI relies on autonomy, goal-oriented behavior, reasoning, planning, decision-making, and adaptability. It also depends on memory, tool use, and learning from feedback, allowing systems to complete complex, multi-step tasks reliably over time.

4. When did agentic AI evolution begin?
Its roots trace back to early neural network research in 1943 and the founding of artificial intelligence in 1956. Recent progress accelerated after 2022, when reasoning models, tool use, and agent frameworks matured quickly.

5. How large is the global agentic AI market expected to become?
The global agentic AI market was valued at USD 8.19 billion in 2025. It is projected to reach USD 290.62 billion by 2035, growing at a CAGR near 42.88%.
October 30, 2026
How Agentic AI Is Revolutionizing Healthcare Operations

Agentic AI is poised to dramatically reshape healthcare operations by deploying intelligent agents that handle complex, multi-step tasks autonomously. From patient scheduling to revenue-cycle management, these AI agents reduce administrative burdens, streamline clinical workflows, and free up healthcare professionals to focus on patient care. Below are the key areas where agentic AI can make an immediate impact.

Patient Scheduling

Agentic AI can coordinate appointments, check real-time availability, manage cancellations, and reduce scheduling workloads for healthcare staff across departments.

Administrative Automation

AI agents handle repetitive tasks like documentation, data entry, claims processing, and routine patient communications, cutting down on manual overhead.

Clinical Workflows

These agents coordinate multi-step clinical processes, ensuring that relevant information, tasks, and notifications reach the right healthcare professionals promptly.

Patient Communication

AI agents manage routine patient queries, appointment reminders, and follow-ups, helping healthcare organizations maintain consistent communication throughout care journeys.

Medical Documentation

Agentic AI organizes clinical information, summarizes records, and assists with documentation, allowing clinicians to spend more time with patients.

Resource Management

AI agents monitor operational resources, coordinate staff requirements, and identify bottlenecks affecting hospital capacity, equipment, and workflow efficiency.

Revenue Cycle

Agentic AI supports billing workflows, identifies missing information, automates routine checks, and helps manage revenue-cycle processes efficiently.

Care Coordination

AI agents connect information across departments, coordinate follow-up tasks, and help ensure patients receive appropriate care at different stages.

Operational Insights

Agentic AI analyzes operational data, identifies recurring inefficiencies, and recommends workflow improvements while keeping human professionals involved in decision-making.

October 29, 2026
Qualcomm’s Agentic AI Vision Could Reshape Smartphones — But Raises Data Privacy Concerns

Qualcomm CEO Cristiano Amon recently outlined a future where smartphones evolve from simple command receivers into proactive AI agents capable of understanding user intent and completing tasks autonomously. Speaking at the Snapdragon Summit 2026 in Maui, Amon described the transition to an ‘agentic age,’ where artificial intelligence becomes the central driver of the mobile experience.

“We are going from a phone-centric model to an agent-centric model. Going forward, agents will become the center of experience. We can finally see what an AI phone truly means,” Amon said.

To support this vision, Qualcomm unveiled two new mobile platforms: the Snapdragon 8 Elite Gen 6 and Snapdragon 8 Elite Gen 6 Extreme. These chips are designed to handle substantial AI workloads locally, reducing the need to offload every task to the cloud. This local processing could enable faster, more private interactions — but it also requires the phone to access more personal data, such as calendars, messages, and shopping details, to anticipate user needs.

Amon acknowledged the importance of user permission and context, stating, “The permission to use will be very important. And, so is context. Because the phone is very personal and specific to you. Agentic AI will need your phone to do a lot more than just execute tasks for you.”

The shift could fundamentally change how users interact with their devices. Instead of manually controlling every step, users might only need to approve key actions while the phone handles the rest. Qualcomm expects this agentic model to extend beyond smartphones to PCs, smart glasses, wearables, and cars, creating a seamless ecosystem of interconnected devices.

As AI agents take on more responsibility, the balance between convenience and privacy becomes critical. Users will need clear visibility into what their devices access and when they are allowed to act. Transparent permission controls and user oversight will be just as important as AI performance in earning trust.

October 29, 2026
Agentic AI Market Forecast to Reach $290.62 Billion by 2035, Driven by Enterprise Adoption and Multi-Agent Systems

The global agentic AI market is projected to surge from USD 8.19 billion in 2025 to USD 290.62 billion by 2035, registering a compound annual growth rate (CAGR) of 42.88% between 2026 and 2035, according to the Global Agentic AI Market 2026 report by Analytics Insight. The market is expected to reach USD 12.13 billion in 2026, reflecting a shift from AI systems that mainly generate content and recommendations toward systems that can plan, reason, coordinate, and execute multi-step tasks.

The report emphasizes that the next phase of growth will depend on more capable models and their reliability, security, governance, integration, evaluation, and accountability. As enterprises increasingly deploy AI systems that can access business data, use software tools, and take action within defined limits, the market is expected to expand more than 5.8 times between 2030 and 2035.

Agentic AI Market Growth Accelerates

Investment conditions are helping push this forward. Stanford HAI’s 2026 AI Index lists global corporate AI spending at USD 581.7 billion, up 130% from the previous year. NVIDIA’s newest reports also point to strong growth in data center sales, with revenue up 117% year over year. At the same time, major cloud and tech firms are planning capital spending of about USD 745 billion for 2026.

Deloitte’s 2026 State of AI in the Enterprise study, which included 3,235 executives in 24 countries, showed that 74% of companies expect to run AI agents by 2027.

Adoption Is Still Behind Early Trials

The report calls out a clear gap: many firms want agentic AI, but fewer are using it at full scale. McKinsey data shows 88% of companies use AI in at least one area, but only 23% have scaled agentic AI across their business. In another McKinsey survey, 62% said they were trying AI agents, yet only 23% had scaled an agentic system. No single business area reached 10% adoption at scale, suggesting most organizations are still in pilots and early testing rather than full rollout.

Solutions Lead as Enterprises Build Agent Platforms

The market is expected to remain solution-led during the forecast period. Agentic AI solutions are projected to increase from USD 5.12 billion in 2025 to USD 168.56 billion by 2035, while services are forecast to rise from USD 3.07 billion to USD 122.06 billion. The report attributes the strength of solutions to enterprise spending on agent platforms, orchestration software, development tools, and application-specific agents. Services are expected to gain ground as businesses require systems integration, security, governance, workflow redesign, and managed AI services for production deployment.

Multi-Agent Systems Set to Reshape Architecture

The report identifies a shift from standalone agents toward coordinated multi-agent environments. Single-agent systems are projected to grow from USD 5.01 billion in 2025 to USD 139.50 billion by 2035, while multi-agent systems are forecast to rise from USD 3.19 billion to USD 151.12 billion, registering a 47.09% CAGR. The transition remains in its early stages, with current enterprise deployments largely concentrated in one or two business functions. However, 85% of enterprises expect to customize agents to meet their specific requirements.

Hybrid Deployment Records Fastest Growth

On-premises deployment is expected to remain the largest category, increasing from USD 4.74 billion in 2025 to USD 124.96 billion by 2035. Cloud deployment is forecast to reach USD 110.43 billion, while hybrid deployment is expected to reach USD 55.22 billion. Hybrid deployment has the fastest projected CAGR at 51.05%, supported by enterprises seeking scalable AI capabilities alongside private infrastructure and controlled environments.

IT, Cybersecurity, and BFSI Drive Expansion

Customer support tools and virtual assistants remain the top application category, growing from about USD 2.28 billion in 2025 to about USD 61.03 billion by 2035. Other categories also grow fast: IT operations and software development are forecast to reach around USD 68.29 billion, data and analytics about USD 29.06 billion, and cybersecurity approximately USD 21.80 billion. The report also points to fast risers including cybersecurity, legal and compliance, operations and supply chain, data and analytics, and IT operations. BFSI is expected to remain the largest end-use industry in 2035.

North America Leads, Asia Pacific Grows Faster

North America remains the biggest market by value, at about USD 3.18 billion in 2025 and forecast to reach around USD 98.81 billion by 2035. Asia Pacific is expected to grow faster, starting at USD 2.22 billion in 2025 and reaching nearly USD 107.53 billion by 2035, with a CAGR of 47.40%. Europe is also expected to rise, from about USD 2.08 billion in 2025 to around USD 66.84 billion in 2035.

Reliability and Governance Remain Key Hurdles

Despite the market’s growth trajectory, reliability remains a major constraint. Stanford HAI’s 2026 AI Index indicated that agents complete around 66% of tasks successfully on OSWorld, meaning one in three attempts still fails. Gartner reported that a Fortune 500 enterprise could run more than 150,000 agents by 2028, up from fewer than 15 in 2025, while only 13% of organizations believed they had adequate governance.

The report expects the next phase to move agentic AI beyond standalone software into enterprise workflows, digital infrastructure, and eventually physical environments. Multi-agent systems, persistent memory, robotics, vertical AI agents, and human-agent collaboration are expected to shape that transition. The broader outlook is that commercial value will increasingly depend on whether agents can perform important tasks reliably, securely, and at scale, rather than simply demonstrating what AI can do.

October 26, 2026