| Constellation Research

Why Smartsheet Leads in Work Coordination Platforms for 2026

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

Work coordination platforms are becoming critical to modern digital work. Discover why Smartsheet earned a spot on the 2026 Constellation ShortList for Work Coordination Platforms.

In this video, R “Ray” Wang, Principal Analyst and Founder of Constellation Research, unpacks the growing disconnect between everyday collaboration tools and the real work employees need to get done. He explains how the work coordination platform market is projected to reach $46.3B by 2031, and how these platforms help teams organize, prioritize, and stay accountable across an expanding number of projects, colleagues, and stakeholders. Ray highlights Smartsheet as a cloud-based work management platform that delivers:

Centralized project and task management
Automated workflows, rules, notifications, and updates
Flexible views (grid, Gantt, Kanban, calendar) to match how teams prefer to work
Real-time collaboration, shared dashboards, and resource management
AI-driven insights and integrations with Slack, Microsoft, and Google Workspace

If you’re evaluating tools to better coordinate projects and business processes at scale, this overview will help you understand where Smartsheet fits in the broader work coordination landscape—and why these platforms drive efficiency, visibility, and project success.

View the full Work Coordination Platforms ShortList here.

ShortList Future of Work Data to Decisions

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Human Agency, China’s Tech Titans, and India’s Rise: Inside DisrupTV Episode 432

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

Elle DeRosa

Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Human Agency, China’s Tech Titans, and India’s Rise: Inside DisrupTV Episode 432

On Episode 432 of DisrupTV, hosts R “Ray” Wang (CEO & Founder, Constellation Research) and Vala Afshar (Chief Evangelist, Salesforce) explored three major forces shaping the global tech landscape:

How algorithms and AI are reshaping human agency
How China’s tech titans and EV makers are changing global competition
Why India is emerging as a third major tech superpower

The episode featured two insightful guests: Marcus Fontoura, Microsoft CTO for Azure Core and author of Human Agency in the Digital World, and Rebecca Fannin, journalist and author of Tech Titans of China.

Human Agency in a World of Algorithms – With Marcos Fontoura

Algorithms amplify—and constrain—our choices. Marcus traced the evolution of computing from replacing human calculators on the Manhattan Project to today’s algorithms shaping social media, job networks, and consumer behavior. While efficiency improves lives, it can also reduce human control and unintentionally cause harm.

Auditing Your Digital Life
Marcos recommends a practical framework:

Identify tasks that amplify humanity: mentoring, caregiving, creative work
Identify tasks that can be delegated to AI: repetitive, formulaic, or administrative tasks

The guiding principle: decide what only humans should do, and let AI handle the rest.

Human-Led, Machine Assist
Marcus emphasized that technology’s purpose is empowerment, not replacement. AI breakthroughs—like DeepMind’s AlphaFold—demonstrate how intelligence can create tangible societal value without displacing human agency.

AI and Value-Producing Work
Drawing on lean management principles, Marcos distinguishes between:

Value-producing work: tasks directly tied to creative output or revenue
Overhead: administrative or repetitive tasks

AI should reduce overhead, freeing humans for higher-value, meaningful work.

China’s Tech Titans, EV Disruption, and Venture Capital Realignment – With Rebecca Fannin

A total rewrite: Rebecca explained why the new edition of Tech Titans of China required a full overhaul. China’s tech landscape has shifted across batteries, drones, semiconductors, and venture capital, with geopolitical tensions redefining cross-border collaboration.

The VC Split
US VCs are largely withdrawing from China, creating “China for China, US for US” investment arms. Cross-border VC is now constrained, altering funding flows for tech startups.

DeepSeek and AI Efficiency
Rebecca highlighted highly efficient, low-capital AI models emerging from China. These “DeepSeek” efforts force Silicon Valley to rethink the relationship between capital intensity and innovation quality.

Chinese EVs: Market and Security Implications
China dominates EV production, with companies like BYD surpassing Tesla. Their low-cost, high-quality vehicles pose both commercial and national security challenges for the US and Europe.

State-Led Capital Model
With US VCs pulling back, China’s government now drives strategic tech investments, funding semiconductors, AI, and industrial technology. This raises questions about efficiency, risk appetite, and long-term competitiveness.

US Response: Rebuilding the Industrial Heartland
Rebecca advocates for:

Reinvesting in domestic manufacturing
Focusing on strategic tech sectors (semiconductors, AI, batteries)
Accepting a more active government role in tech and infrastructure

India: The Third Tech Superpower

India is emerging as a cost-efficient, talent-rich alternative to the US–China duopoly:

Third-largest producer of unicorns
Hundreds of millions of internet users, with rapid growth
Innovation at 1/10th to 1/100th the cost of Western incumbents in areas like space and digital infrastructure

Challenges remain—energy infrastructure, capital flight—but India is increasingly capable of supporting independent innovation ecosystems.

Key Takeaways

Human agency matters: Audit your digital life, delegate tasks to AI wisely, and protect what only humans can do.
AI as empowerment, not replacement: Focus on increasing value-producing work and reducing drudgery.
China’s tech rise is structural: EVs, AI efficiency, and state-led capital reshape global competition.
US industrial strategy must adapt: Investment in strategic sectors and infrastructure is urgent.
India is a third tech pole: Cost efficiency and talent are driving a new global tech player.

Final Thoughts

Episode 432 of DisrupTV illuminates a world where technology, geopolitics, and human agency intersect. Leaders, policymakers, and individuals alike must deliberately design for empowerment, efficiency, and opportunity in a landscape increasingly mediated by algorithms and global competition. The future will reward those who understand where AI can support humans, where human judgment remains irreplaceable, and how nations position themselves in the evolving global tech hierarchy.

Related Episodes

If you found Episode 432 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work Tech Optimization New C-Suite AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

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From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

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Cybersecurity, Nvidia GTC, CEO Mandate for AI | ConstellationTV Episode 126

Liz Miller

Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

Hannah Mason

Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

AI Is Now the CEO’s Job: Key Trends from Enterprise Tech, Nvidia GTC, and Constellation’s CFF/AIF

Artificial intelligence has moved from experimental projects at the edge of the business to the center of boardroom strategy. Recent conversations across enterprise technology, Nvidia’s GTC conference, and Constellation Research’s own Future Forum (CFF) and AI Forum (AIF) show the same pattern: AI is no longer just an IT concern. It is a structural force reshaping security, infrastructure, operating models, and even geopolitics.

This blog highlights three major trend areas reflected in CRTV Episode 126 and the surrounding events:

Enterprise technology and security news
Nvidia GTC and the next phase of AI infrastructure
CEO- and board-level themes from Constellation’s CFF and AIF

Enterprise Tech & Security: “Security Is a Data Problem”

The platformization of cybersecurity

A major trend in enterprise technology is the rapid convergence of security and data platforms. Traditional cybersecurity vendors have long focused on endpoints and perimeter defense. Now, data-native platforms are moving aggressively into security:

Databricks is positioning itself as a security player by building security capabilities directly on top of its data platform, reducing the need for costly data ingestion into separate SIEM tools.
Elastic is embedding security natively into its search and observability stack, again emphasizing proximity to data.
ServiceNow has expanded into security through acquisitions, bringing a workflow-centric approach that ties incident response and security operations into broader business processes.

The emerging pattern: “security is a data problem.” Rather than shipping telemetry to specialized tools and paying for duplicated storage, enterprises are increasingly looking to secure data where it already lives and use AI on that data to detect and respond to threats.

Agentic AI and the next security challenge

As agentic AI systems proliferate—autonomous or semi-autonomous agents acting on behalf of users and applications—security thinking is shifting again. At events like RSA, this is showing up as:

A focus on securing agents and their toolchains rather than just securing static applications.
Increased emphasis on governance of data flows into and out of agents, including prompt injection defenses, data leakage controls, and policy enforcement at the data layer.

Non-traditional security vendors with strong data and AI capabilities are using this moment to undercut endpoint-centric incumbents by promising:

Less data movement
Lower ingestion and storage costs
Tighter integration between analytics, AI, and security controls

Advertising and AI: from “digital” to “answer engines.”

On the marketing and media side, enterprises are preparing for an era of “answer engine marketing” or “generative engine marketing”—where AI systems surface answers, not pages, and where ads may be injected directly into those answers.

Key trends:

Generative AI in discovery: Platforms like Apple Music (in partnership with Ticketmaster), Google TV (with Gemini), and Amazon’s ad stack are using AI to drive content discovery and personalization across connected devices.
CTV and data-driven targeting: Connected TV has become a prime advertising surface, powered by granular audience data and predictive models.
Resurgence of “old” channels: Direct mail (e.g., Valpak) and outdoor advertising are benefiting from precise data-driven segmentation and dynamic content, contradicting past assumptions that these channels were “dead.”

The core driver behind all these developments is the same: better use of audience and behavioral data, appropriately secured and governed, to place the right content in front of the right person at the right moment—across digital, physical, and hybrid surfaces.

Nvidia GTC: The AI Factory Era

Pivot to inference and the Vera Rubin platform

Nvidia’s GTC conference—as some have called it, the “Woodstock of AI”—highlighted a clear shift: from a narrow focus on training large models to a broader emphasis on inference at industrial scale.

A central symbol of this shift is the Vera Rubin system, designed as a next-generation AI “factory”:

It is optimized around inference workloads, not just training.
It introduces specialized components such as the Language Processing Unit (LPU) to improve efficiency and responsiveness for language-heavy inference and agentic systems.
It combines GPUs, CPUs, storage, and ultra-fast networking (e.g., NVLink and associated interconnects) in a tightly integrated fabric.

The direction is clear: enterprises are expected to build or rent AI factories—clusters of systems like Vera Rubin—to serve as the backbone for running fleets of agents and AI applications in production.

Software, open models, and vertical LLMs

Nvidia continues to consolidate its position not only as a hardware provider, but as a software and ecosystem orchestrator:

A growing catalog of vertical LLMs and domain-specific models spans areas such as healthcare, robotics, simulation, synthetic data generation, and financial services.
Many of these models are made available through Nemo and related initiatives that emphasize open or open-weighted approaches, helping enterprises jumpstart projects without building everything from scratch.
The Nemo Tron Alliance and similar collaborations are creating an ecosystem of startups aligned around Nvidia-centric tooling, hardware, and deployment patterns.

While Nvidia does not position itself as an enterprise software vendor in the traditional sense, it is building a de facto AI operating environment that reduces friction for adopting its hardware. In this model, “software is free” in the sense that it helps drive demand for infrastructure.

Energy, scale, and the move toward space-based AI

A more experimental but increasingly serious thread involves energy constraints and new compute geographies:

There is growing recognition that large-scale AI will be constrained by power availability as much as by chips or models.
Concepts such as space-based data centers powered by solar have moved from science fiction to early-stage architectural thinking, with renderings and prototypes being discussed publicly.
Ambitions around custom fabs (e.g., “Terra fab”) underscore a trend toward vertical integration: from chip design to fabrication to deployment in specialized environments (including space, automotive, robotics, and beyond).

While these scenarios may be years away from mainstream enterprise adoption, they reflect a larger reality: AI strategy is increasingly inseparable from energy strategy and geopolitics.

CFF & AIF: AI as a CEO and Board Mandate

Constellation Research’s Future Forum (CFF) and AI Forum (AIF) brought together CEOs, board members, CIOs, and technology leaders. Across these events, a single message cut through the noise:

AI is the CEO’s job.

From “digital transformation” déjà vu to real accountability

The notion that “AI is a CEO issue” echoes earlier eras when customer experience (CX) and digital transformation were framed as top-of-the-house responsibilities. In many organizations, those prior mandates never fully materialized:

CX initiatives remained fragmented across marketing, sales, and service, hampered by siloed data and misaligned incentives.
Digital transformation often turned into incremental digitization rather than true business model reinvention.

The AI moment feels similar—but with higher stakes. This time:

Boards are directly asking CEOs, “What is our AI strategy?”
CEOs are expected to own enterprise-wide alignment, not delegate AI entirely to IT or innovation labs.
Leaders who fail to steer AI in a coherent way may face personal accountability faster than in past transformation waves.

Culture, operating models, and the pace of change

Participants at CFF and AIF consistently highlighted culture and operating model as the real bottlenecks, not technology:

Organizations with entrenched functional silos and conflicting incentives (e.g., sales, marketing, and service) struggle to align on data and AI.
AI adoption requires rethinking processes and “sacred cows”—from compensation models to decision rights.
There is a growing knowledge gap between boards, CEOs, and AI practitioners, turning basic AI discussions into a game of telephone.

Simultaneously, the pace of change in AI models and platforms makes strategic planning more complex:

A decision to standardize on one model provider (e.g., OpenAI) can look outdated within months as competitors like Anthropic or open-source ecosystems catch up or leap ahead.
Enterprises are wary of becoming locked into any single foundation model, vendor, or cloud platform, driving interest in model-agnostic architectures and multi-model strategies.

Open source, “claws,” and safe experimentation

At the AI Forum in particular, there was an animated discussion around open AI stacks, including tools like OpenCLIP / Open-weights models and various “claw” frameworks that allow organizations to orchestrate and govern multiple models and agents.

Key enterprise concerns include:

How to experiment safely with open and local models without exposing sensitive data or critical systems.
How to define governance boundaries for internal innovators who will inevitably bring in new tools and frameworks.
How to leverage open ecosystems to avoid vendor lock-in, while still meeting requirements around support, compliance, and security.

Many leaders are adopting a tiered experimentation strategy:
using isolated, low-risk environments (e.g., dedicated machines or sandbox infrastructure) for early trials, before integrating new tools into core data and application stacks.

Geopolitics, power, and sovereignty

Finally, CFF and AIF discussions reinforced that the AI strategy is now entangled with geopolitics:

Differences in energy costs, regulatory regimes, and data sovereignty are shaping where and how AI can be deployed at scale.
Regions such as China and India are developing their own models, data practices, and infrastructure approaches, which may diverge meaningfully from North American and European patterns.
Executives are increasingly aware that power availability, chip supply, and national policy can materially impact AI roadmaps, even though these factors are largely outside their direct control.

For many enterprises, this means AI planning must include scenarios for supply chain volatility, regulatory shifts, and regional model ecosystems, not just technology capabilities.

From Hype to Hard Choices

Across enterprise news, Nvidia’s GTC announcements, and Constellation’s CFF and AIF, a consistent picture emerges:

Security is converging with data and AI, pushing enterprises toward platforms that can analyze and protect information in place.
AI infrastructure is industrializing, with “AI factories” and specialized hardware/software stacks designed for always-on inference and agents.
AI has become a CEO and board mandate, forcing organizations to confront culture, operating models, vendor lock-in, power constraints, and geopolitics—not just tools and models.

The organizations that navigate this moment successfully will treat AI not as a set of disconnected pilots, but as a strategic, cross-functional transformation that redefines how they create value, manage risk, and compete.

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Global Growth, Status, and AI Agents: What’s Shaping the Next Decade of Business | DisrupTV Ep. 431

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

Elle DeRosa

Director of Marketing, Communications & Operations
Constellation Research

Global Growth, Status, and AI Agents: What’s Shaping the Next Decade of Business | DisrupTV Ep. 431

In DisrupTV Episode 431, hosts R "Ray" Wang and Vala Afshar explored two forces that will shape the next decade of business: global expansion in an AI-first world and the powerful role of social status in decision-making.

Featuring Russell Haworth, CEO of Acclaro, and Toby Stuart, UC Berkeley professor and author of Anointed, the conversation highlighted how companies scale globally, build trust, and navigate influence in an era where AI agents increasingly mediate interactions between people, products, and brands.

Together, the discussions revealed an emerging reality: globalization, AI, and status dynamics are becoming deeply intertwined.

Global Expansion Starts at Design, Not Launch

Russell Haworth emphasized a common mistake many companies make when expanding internationally.

Most organizations build products for their home market first—often the U.S.—and only consider localization once domestic success arrives. By then, adapting product design, UX, and go-to-market strategies becomes costly and slow.

His advice: design global from day one.

Localization isn’t just translation. It touches nearly every part of the business:

User experience: Interface layouts built for English can break when translated into languages with longer text or different structures.
Payments and regulation: Local payment systems, legal requirements, and compliance vary widely across markets.
Customer trust: If users cannot interact in their own language or cultural context, retention suffers.

As Haworth put it, if customers can’t understand your company, you don’t have a translation problem—you have a churn problem.

Hyper-Localization and the AI + Human Model

Modern global growth requires something deeper than simple localization: hyper-localization.

This means adapting messaging, imagery, campaigns, and channels to specific audiences—even within the same country. Cultural nuance matters across regions, demographics, and social groups.

AI plays a key role in scaling this effort.

AI systems can rapidly translate and adapt content across multiple markets, enabling companies to operate globally at speed. But human experts remain critical for cultural nuance, market testing, and ensuring messaging resonates authentically.

The winning formula is clear: AI for scale, humans for nuance.

Organizations that combine both can dramatically increase engagement and conversion in international markets.

Social Status in a Winner-Take-Most Economy

The second half of the episode shifted focus to social status and influence, a topic explored in Toby Stuart’s book Anointed.

Stuart explained that status shapes many of our decisions—often unconsciously. When we face uncertainty or too many choices, we rely on signals like reputation, affiliation, and brand prestige.

Status comes from multiple sources:

Achievement: expertise or performance valued by a group
Occupation: roles with built-in prestige
Personal reputation: generosity, collaboration, or leadership within communities
Ascribed traits: characteristics like gender, race, or background
Network associations: status gained through relationships and affiliations

These signals often determine who gets attention, funding, or opportunity—even when the underlying quality of ideas or products is similar.

The “Anointment” Effect

Stuart illustrated this dynamic with the art market.

A painting attributed to a student of Rembrandt might sell for thousands. The exact same painting, if authenticated as a Rembrandt, could be worth millions.

Nothing about the object changes—only the name attached to it.

This same pattern appears in venture capital, hiring, media influence, and product perception. Once a person or brand is recognized as high-status, the market tends to treat their output as higher quality.

Status becomes self-reinforcing.

AI Agents and the Future of Trust

Both conversations converged around one emerging reality: AI agents are rapidly becoming intermediaries in decision-making.

Consumers increasingly rely on AI systems to recommend products, plan travel, evaluate vendors, and even prepare for meetings.

This shift could reshape how status works.

Today, humans rely heavily on reputation and pedigree because we cannot evaluate everything ourselves. AI agents, however, can analyze massive amounts of data and potentially assess quality more directly.

In the near term, this may actually increase reliance on status signals, as people look for trusted sources in an increasingly complex digital landscape.

Over time, as AI evaluation improves, traditional prestige signals—like brand reputation or elite credentials—may become less dominant.

But new hierarchies will emerge around which agents, platforms, and AI systems people trust to act on their behalf.