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Praerit Garg, Smartsheet’s president of product and innovation, says generative AI is a “gift” because it can remove repetitive software work and leave people more time for judgment, creativity and problem-solving. In a January 2025 Shift AI podcast interview reported by GeekWire, he pointed to natural-language formulas, questions about project data and a possible future of AI agents. Smartsheet also reported a 40% increase in adoption of its AI capabilities and a 20% reduction in formula-related support costs—but the article did not publish the definitions, time period or independent validation behind those figures.
What Garg’s “gift” argument actually means
Garg’s claim is a theory about how software changes work, not proof that every employee will get more free time. He argues that AI lowers the barrier to using business software, absorbs repetitive tasks and lets people spend more of their working day on activities that require judgment, creativity and problem-solving.
That perspective comes from an executive who has worked through several technology shifts. The GeekWire profile says Garg spent more than 12 years at Microsoft, later ran the startup Symform, worked at Amazon Web Services and joined Smartsheet in 2019. Those experiences explain why he compares generative AI with earlier platform changes such as personal computers, the internet, mobile technology and cloud computing. They do not establish that AI will have the same economic or social effect.
The optimistic outcome depends on how an employer uses saved time. It could support deeper analysis and new projects, but it could also become faster deadlines, higher workloads, staff reductions or more intensive performance monitoring.
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What Smartsheet is using AI to do
Generate and explain formulas in ordinary language
Smartsheet’s audience includes business users who need calculations but do not want to learn formula syntax as an end in itself. Garg described an AI experience in which a user states the desired result in natural language and receives help generating or understanding a formula.
That can make advanced spreadsheet and work-management functions more accessible. It does not make the output automatically correct. A user should inspect the ranges, date logic, assumptions and aggregation before relying on a formula for financial, scheduling, compliance or operational decisions. The GeekWire interview does not provide an error rate or accuracy guarantee.
Ask questions about operational data
Garg gave the example of asking which projects are over budget and receiving a chart. Conceptually, the workflow is:
- The user asks a business question in natural language.
- Smartsheet interprets the question against structured work data.
- The service generates an answer or visualization.
- The user investigates the result or uses it as decision support.
The answer depends on the underlying budgets and status fields, the user’s permissions, the definition of “over budget,” and the query or calculation produced behind the scenes. Missing data, stale updates or inconsistent project names can produce a polished but misleading chart.
Move from assistance toward agents
Garg also discussed a future in which agents perform more multistep tasks that people handle today. It is important to separate three levels of capability:
- Assistants respond to a request or suggest an output.
- Automation executes a predefined workflow.
- Agents may plan and carry out several steps with limited supervision.
The interview records a vision, not a demonstrated roadmap for autonomous Smartsheet agents. Before an agent can change a budget, schedule, permission or customer record, an organization would need approval gates, action logs, rollback procedures and a named person accountable for exceptions.
What evidence did Smartsheet report?
Garg told GeekWire that Smartsheet saw these results after launching its AI capabilities:
| Reported result | What is known | What is not established |
|---|---|---|
| 40% increase in AI adoption | Garg reported an increase after launch. | The article does not define adoption, identify the customer cohort, give a denominator or state the measurement period. |
| 20% reduction in formula-related support costs | Garg reported lower costs associated with formula support. | The article does not show methodology, causation, customer accuracy, satisfaction or whether the result applies globally or to a particular tier. |
These are company-reported figures, not independently verified outcomes. A buyer evaluating them should request the baseline, cohort, time window, repeat-use definition, support-volume data and any effect on resolution quality. Fewer support costs could mean that users solved problems themselves, but it does not by itself prove that the product became easier or more reliable.
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Garg said Smartsheet’s “analyze data” architecture does not send customer data directly to the AI model. In his description, a prompt produces a SQL query inside Smartsheet’s service environment, where the query runs against the customer’s data.
That statement should be limited to the feature and workflow he described. It is not a blanket description of every Smartsheet AI function. Before enabling AI on sensitive workspaces, an IT or security team should ask:
- Which specific features use this architecture?
- Are prompts, generated SQL, results and logs retained, and for how long?
- Which model providers and subprocessors are involved?
- Is customer content excluded from model training?
- Do row-, sheet-, workspace- and account-level permissions apply to AI answers?
- Can administrators audit prompts, outputs, changes and agent actions?
- What happens when generated SQL or a formula is wrong?
- Are capabilities different by plan, region or data-residency arrangement?
- Are regulated or particularly sensitive data types subject to additional controls?
Garg emphasized visibility for IT and security teams. Visibility is useful, but it is not the same as regulatory compliance or a guarantee that an implementation is secure; those questions require current product documentation and legal review.
Will AI improve jobs—or intensify them?
Garg’s stated view is that AI should make work more meaningful and give people room to pursue new missions instead of merely completing existing tasks. Potential benefits include:
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- Less manual formula construction.
- Faster exploration of project and budget data.
- Easier access to advanced functions for nontechnical employees.
- Less repetitive support work.
- More time for analysis, creativity and judgment.
The opposing possibilities are just as practical:
- Employees may lose the ability to audit formulas or data logic if they stop learning how the system works.
- Fluent explanations and attractive charts can hide incorrect assumptions.
- Productivity gains may be converted into headcount reductions or faster targets rather than shorter workweeks.
- Workers may face new monitoring and review duties.
- Organizations may need new AI-supervision skills, creating work rather than eliminating it.
- Employees may resist deployments framed as surveillance or cost cutting.
Whether AI is a gift therefore depends on governance and job design, not only on model capability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the acquisition context matters
GeekWire’s January 29, 2025 article described Smartsheet as having been acquired by Blackstone and Vista Equity Partners in an $8.4 billion transaction. That is historical context from the interview period, not a statement about the company’s ownership on any later date.
AI investment can improve the customer product, support retention and expansion, reduce support costs and differentiate a work-management platform. Those are reasonable business incentives; the article does not establish management’s motives or the transaction’s current status.
Questions buyers should answer before enabling AI
- Accuracy: Can users inspect the formula, query, source rows, assumptions and transformations?
- Permission fidelity: Does an answer expose only data the requesting user is authorized to see?
- Auditability: Are prompts, outputs, edits, approvals and automated actions logged?
- Reversibility: Can an administrator undo or roll back AI-generated changes?
- Human approval: Are high-impact actions gated by a person?
- Data governance: What are the retention, training-use, residency and subprocessor rules?
- Usability: Does natural language genuinely help the intended nontechnical users?
- Economics: Are savings measured in customer outcomes, vendor support costs or both?
- Availability: Is the feature included in the organization’s plan, sold as an add-on or restricted by region?
- Change management: Who trains employees to validate outputs and handle failures?
How Smartsheet fits among alternatives
These are positioning distinctions, not independent performance rankings. Features, prices and AI entitlements change, so verify them on each vendor’s official site.
| Platform | Likely fit | Important distinction |
|---|---|---|
| Smartsheet | Structured work management, sheets, portfolios, dashboards, formulas and workflow governance. | Best investigated when operational data and portfolio control are central; configuration may be excessive for small teams seeking only general office AI. |
| Microsoft 365 Copilot | Organizations standardized on Microsoft 365 that want AI across documents, email, meetings and collaboration. | Broader workplace assistance rather than a direct substitute for Smartsheet-style portfolio modeling. |
| Asana | Task management, project coordination and execution visibility. | Generally task- and workflow-oriented rather than spreadsheet-centered operational modeling. |
| monday.com | Visual, highly configurable team workflows. | Compare data structures, reporting depth, governance and AI entitlements for complex portfolios. |
| Airtable | Database-like records, flexible schemas and lightweight internal applications. | More application-building oriented, often requiring more schema design than a ready-made portfolio system. |
Bottom line
Garg’s thesis is credible as a direction: natural-language access to structured work data can reduce friction, particularly for people who do not want to master formulas. Smartsheet’s reported adoption and support figures are encouraging but remain company claims without published methodology. The decisive test for buyers is whether the product improves decisions and removes low-value work while preserving permission controls, explainability, audit logs, human accountability and employee trust.
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