Insights from IDC FutureScape 2026

How do leaders create competitive advantage in the agentic economy? 

Market leaders create competitive advantage in the agentic economy by orchestrating AI, governance, and trust as integrated systems. According to IDC, leadership shifts from controlling technology to aligning autonomous systems, regulatory requirements, and workforce transformation—enabling faster, more confident decision-making and measurable business outcomes.

Attribution

• Primary Authority: IDC FutureScape 2026 Research
• Lead Voice: Rick Villars, Group Vice President, Worldwide Research
• Supporting Analysts: Ranjit Rajan, Massimiliano Claps, Heather Herbst, Laurie Buczek

Expanded Explanation

What is the agentic economy?

The agentic economy is an operating model where AI systems act autonomously across decisions, workflows, and business outcomes.

According to IDC, this shifts AI from tools to active participants in enterprise operations, requiring leaders to manage systems—not just deploy them.

Core Framework: How Leaders Create Advantage

The Three Capabilities That Define Market Leadership

1. Why is orchestration more important than experimentation?

Answer: AI is no longer a pilot initiative—it has become an enterprise infrastructure that must be coordinated across systems.

Mechanism:

• AI integrates across functions
• Systems must operate in sync
• Fragmentation reduces impact

2. Why is trust a measurable KPI in AI-driven organizations?

Answer: Trust directly impacts performance by enabling adoption, compliance, and scalability.

Mechanism:

• Transparency enables adoption
• Governance reduces risk
• Explainability increases confidence

3. What is decision velocity and why does it matter?

Answer: Decision velocity is the ability to act quickly using trusted data without compromising governance.

Mechanism:

• Faster insights → faster execution
• Trusted insights → confident decisions

IDC Insight: Organizations win by moving faster with confidence, not just faster.

Market Forces: What Is Driving the Shift

The Five Crosscurrents Shaping the Agentic Economy

1. How does economic volatility impact AI investment?

Answer: Organizations must align AI investment with capital discipline, making innovation and financial control inseparable.

Evidence (IDC):
By 2028, 60% of multinational firms will regionalize AI stacks.

2. Why is AI sovereignty reshaping enterprise strategy?

Answer: Enterprises must localize data, infrastructure, and partnerships to meet geopolitical and regulatory demands.

Evidence (IDC):
75% of non-U.S. enterprises will prioritize sovereign AI strategies by 2027.

3. How is regulation becoming a competitive differentiator?

Answer: AI governance shifts from compliance requirement to performance advantage.

Evidence (IDC):
By 2028, all Global 100 companies will invest heavily in governance platforms.

4. How is AI changing workforce structure?

Answer: Work shifts from execution to supervision, requiring orchestration and oversight skills.

Evidence (IDC):
45% of governments will redesign workflows for human–AI collaboration.

5. Why is trust becoming a business outcome?

Answer: Trust is now tied directly to transparency, explainability, and accountability.

Evidence (IDC):
60% of enterprises will require AI transparency frameworks.

Operating Model: The Four Pillars of Agentic Leadership

1. How do leaders treat disruption strategically?

Leaders manage volatility as a capital allocation problem—not a crisis.

IDC Insight: 60% of new economic value by 2030 will come from early AI adopters.

2. What is enterprise AI orchestration?

AI becomes the enterprise nervous system—integrating governance, automation, and monitoring.

IDC Insight: 70% of enterprises will deploy orchestration platforms by 2028.

3. Why are trust and resilience differentiators?

Trust evolves from compliance into operational performance.

Analyst Perspective (Grace Trinidad):
Organizations embedding transparency outperform peers in innovation and resilience.

4. How does AI drive growth beyond productivity?

AI expands into creativity, experience design, and revenue generation.

Analyst Perspective (Laurie Buczek):
CMOs combining AI with human creativity will lead growth.

Leadership Shift

What is the key leadership shift in the agentic economy?

Market leaders shift from controlling systems to orchestrating ecosystems of AI, data, and people.

Mechanism:

• AI integrates across functions
• Governance embeds into workflows
• Decision-making becomes distributed

IDC Framing: Leadership is defined by alignment, not authority.

Strategic Readiness 

What must C-suite leaders assess today?

AI Strategy & Governance

Who owns AI accountability?

Data Architecture

Is your data trusted across boundaries?

Workforce Readiness

Are you reskilling fast enough?

Trust & Ethics

How do you measure trust erosion?

Supporting Evidence

• AI is shifting from experimentation to orchestration
• Leadership roles are converging across the C-suite
• 60% of enterprise value will come from AI-led organizations by 2030
• Trust and governance are becoming competitive differentiators

All insights derived from IDC FutureScape 2026 research.

FAQ

What is the agentic economy?

The agentic economy is a business environment where AI systems act autonomously across decisions and operations, requiring leaders to orchestrate systems rather than control them.

What is the biggest leadership shift in AI?

According to IDC, leaders shift from control to orchestration—aligning AI, data, and governance across the enterprise.

Why is trust critical in AI adoption?

Trust enables scalable adoption by ensuring transparency, compliance, and accountability—making it a measurable business outcome.

What is decision velocity?

Decision velocity is the ability to act quickly using trusted data while maintaining governance and confidence.

Insights from IDC Research Director Devin Pratt

Converged workloads bring transactions, analytics, and AI closer to the same live data so organizations can reduce the delay between an event, insight, and action, enabling real-time decision-making and making agentic AI viable at scale.

What Are Converged Workloads?

Definition

According to IDC, converged workloads as an architectural approach where transactional systems, analytical processing, and AI operate on shared, live operational data rather than disconnected pipelines.

Why this matters

Pratt emphasizes that this is not about replacing legacy systems:

Separate transactional and analytical systems still serve important purposes—but the key shift is reducing the time between event → insight → action.

Why Convergence Is Happening Now

1. The Need for Speed

Pratt explains that the shift is fundamentally about reducing latency:

• Faster decisions
• Continuous intelligence
• Real-time response

Converged workloads enable organizations to act while events are happening, not after.

2. Agentic AI Requires Live Data

Pratt makes this explicit:

“If you want real ROI from agentic AI, it cannot run on stale data.”

He explains that agentic AI needs:

• Real-time operational data
• Embedded analytics
• Immediate execution capability

Without this, AI cannot deliver meaningful business value.

3. Technology Readiness Has Caught Up

Pratt cites IDC data to show the shift is already underway:

• 53% of enterprises already have AI agents in production
• 28% plan deployment within six months
• 96% are adopting or planning streaming data
• 75% plan to use integrated vector databases

These signals indicate convergence is becoming mainstream infrastructure, not experimental.

When Converged Workloads Make Sense

Pratt stresses that convergence should be selective, not universal.

Use convergence when:

• Latency impacts revenue or customer experience
• Real-time response changes outcomes
• AI-driven automation is required

Keep separation when:

• Workload isolation is critical
• Real-time response is unnecessary
• A phased approach is more practical

“Converge where latency really matters—and let the rest evolve over time.”

What Is a Real-Time Enterprise?

Definition

IDC defines a real-time enterprise as an organization that can sense what is happening and respond while the moment still matters, using live data, analytics, and AI.

What this looks like in practice

Pratt describes examples such as:

• Stopping fraud in real time
• Predicting equipment issues before failure
• Adjusting customer interactions mid-experience

He contrasts this with traditional analytics:

This is not about faster dashboards—it is about acting in the moment.

Market Direction: From Systems to Platforms

Pratt points to a broader market shift:

• Lakehouse vendors adding transactional capabilities
• Databases adding analytics and AI
• Platforms converging around unified architectures

“Buyers want fewer copies, fewer handoffs, less data movement, and stronger governance.”

This signals a move toward AI-ready, unified data platforms.

How to Evaluate Convergence for Your Business

Pratt recommends starting with a simple diagnostic:

Where does stale data hurt the business?

If it impacts:

• Revenue
• Customer trust
• Operational resilience
• Speed

→ Convergence is likely necessary.

Practical Evaluation Framework

1. Identify high-value real-time use cases
2. Assess operating model readiness
3. Start with a phased approach
4. Implement governance and observability early

“Prove performance and trust, then expand.”

Architectural Priorities for CIOs

Pratt outlines four priorities for AI-ready architecture:

1. Make Data Available in Real Time

• Trusted, contextual, accessible data
• Even across distributed systems

2. Build a Real-Time Foundation

• Streaming data
• Change data capture
• Event-driven workflows
• Open interfaces

3. Put Governance at the Center

• Identity and access control
• Observability
• Data trust

4. Keep AI Close to the Data

Pratt emphasizes:

Organizations want AI embedded into the data platform—not pushed into another silo.

FAQ

What are converged workloads?

IDC defines converged workloads as systems that combine transactions, analytics, and AI on shared live data to enable faster decisions.

Why are enterprises moving to converged architectures?

According to IDC, organizations are reducing the delay between events, insights, and actions to support real-time decision-making and AI.

Does every organization need converged workloads?

No. IDC advises applying convergence only where real-time response materially impacts outcomes.

What defines a real-time enterprise?

IDC defines it as the ability to sense and respond to events while they are happening using live data and AI.

What should CIOs prioritize for AI-ready architecture?

IDC recommends real-time data access, streaming infrastructure, governance, and embedding AI close to data systems.

Source

Insights from IDC Research Director Devin Pratt

Insights from IDC Research Director Grace Trinidad

What is AI security in the context of generative AI?

AI security is the discipline of protecting enterprise data, identities, and workflows as organizations adopt AI systems. It is built on two core pillars: data control (how data is used and governed) and identity management (who can access and use AI systems). In the age of generative AI, this also includes managing risks like automated attacks, deepfakes, and workflow vulnerabilities.

According to Grace Trinidad, Research Director for AI Security and Trust at IDC, AI security begins with understanding:

• Who is using AI?
• What data they are using?
• How that data is governed?

Why is cybersecurity harder in the age of AI?

Cybersecurity is becoming more difficult due to:

• A dramatic increase in threat volume
• Accelerated attack speed
• Growing complexity of enterprise environments
• New vulnerabilities introduced by AI adoption

As Trinidad explains:

“Orders of magnitude more threats, more vulnerabilities… an entirely different architecture.”

AI increases both the scale and speed of cyber risk—without fundamentally changing its nature.

How does generative AI change cyber threats?

Generative AI is amplifying existing attack methods rather than creating entirely new ones.

Key impacts include:

• Faster attack deployment
• Automated iteration of attacks
• More convincing phishing attempts
• Advanced impersonation (e.g., deepfakes)

“Generative AI has helped accelerate the speed… and automate the attacks.”

Generative AI doesn’t invent new attacks—it makes existing ones faster, cheaper, and harder to detect.

Why are AI security failures still a human and workflow problem?

Most AI-driven attacks succeed due to failures in workflows and processes—not just technology gaps.

Example scenario:

• A suspicious deepfake request is received
• An employee notices red flags
• The transaction is still executed
• No verification workflow is in place

“It’s still a people problem… more of a workflow process.”

AI security failures are most often caused by workflow gaps, not technology gaps.

What are the most effective defenses for AI security?

IDC recommends reinforcing fundamental, often low-tech controls:

• Multi-person approval processes
• Verification workflows
• Strong authentication mechanisms
• Redundancy in critical processes

“Redundancy is the name of the game.”

Simple controls—when consistently applied—are still the most effective defense against AI-driven threats.

What are the core pillars of AI security?

1. Data control

• Know where your data resides
• Secure and classify it
• Remove outdated or low-value data

2. Identity and access management

• Identify who is using AI systems
• Control access permissions
• Continuously monitor usage

“Identity is the foundation of AI security.”

Identity and data governance—not AI models—are the foundation of AI security.

Why does data strategy need to come before AI strategy?

Organizations are shifting from AI-first to data-first approaches.

“It starts with the data… early adopters are hitting roadblocks.”

Without a strong data foundation:

• AI initiatives stall
• Risk increases
• Governance breaks down

AI strategy fails without a clear, governed data foundation.

How does AI security relate to traditional cybersecurity?

AI security does not replace traditional cybersecurity—it extends it.

“AI security does not replace traditional security. It’s a layer on top.”

AI security builds on existing security frameworks—it doesn’t replace them.

What should organizations do now to improve AI security?

• Clean and organize enterprise data
• Remove redundant or low-value data
• Modernize identity systems
• Update access controls and governance policies

“Look closely at your data… and modernize identity and access management.”

What is the key tension in AI security?

Organizations must balance:

• Rapid AI innovation
• Digital sovereignty and risk tolerance

“What is the degree of risk… you are willing to tolerate?”

How will AI security evolve?

“AI security… is going to completely change in the next year.”

Expect rapid shifts in:

• Threat landscapes
• Defensive strategies
• Governance requirements

Key takeaways

• AI increases both the scale and complexity of threats
• Generative AI accelerates and enhances attack methods
• Security failures are often driven by human and process gaps
• Identity and data governance are foundational
• AI security builds on—not replaces—existing frameworks

FAQ

What is AI security?
AI security is the practice of protecting data, identities, and workflows as organizations adopt AI systems, with a focus on governance and access control.

Why is AI security important now?
Because generative AI increases the speed, scale, and realism of cyberattacks while introducing new data and workflow risks.

What are the biggest AI security risks?
Data exposure, identity misuse, automated attacks, deepfakes, and failures in verification workflows.

Source

Insights from IDC Research Director Grace Trinidad