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Can C3.ai's IPD-Led Sales Reset Support a More Durable Growth Path?
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Key Takeaways
C3.ai is shifting sales to IPDs, using smaller deployments to prove value before scaling enterprise-wide.
AI signed 20 new IPDs in fiscal Q2, bringing total IPDs to 394, with 269 currently active.
C3.ai is absorbing near-term margin pressure to improve IPD execution and long-term conversions.
C3.ai, Inc. (AI - Free Report) is entering a new phase of commercial execution, with Initial Production Deployments (IPDs) emerging as the central driver of its sales reset. Management is repositioning IPDs as the primary entry point for customers, emphasizing smaller, high-impact deployments that demonstrate measurable economic value before scaling across the enterprise. This shift reflects an emphasis on disciplined, conversion-focused deployments rather than large, upfront expansions.
Activity levels around IPDs showed signs of stabilization in the second quarter of fiscal 2026. During the quarter, C3.ai signed 20 new IPDs, including six generative AI IPDs, bringing cumulative IPDs to 394, with 269 currently active across pilots, extensions or conversion discussions. The company noted that IPDs increasingly serve as the proving ground where customers validate outcomes and build internal confidence for broader production rollouts. Several of AI’s largest accounts — including GSK, Dow and Holcim — followed this exact path, starting with focused IPDs before expanding adoption across multiple business units.
Operationally, the company has tightened execution standards around IPDs. Management outlined stricter upfront qualification, milestone-based delivery requirements and increased executive oversight. These measures are intended to align each deployment with a clearly defined economic objective and improve conversion outcomes. Management characterized recent performance challenges as primarily execution-related rather than reflective of weakening enterprise AI demand.
From a financial perspective, the IPD-led approach carries near-term implications. A higher mix of IPDs and associated delivery requirements has contributed to moderated gross margins, as initial deployments typically involve higher upfront costs and a heavier services component. Management described this margin impact as a deliberate trade-off, prioritizing conversion quality and long-term customer value over near-term margin expansion.
Looking ahead, management indicated that improving IPD execution and conversion discipline will remain central to restoring more consistent growth. Leadership emphasized that demonstrating measurable economic value early in deployments is intended to support broader production adoption over time. As the sales reset progresses, IPD-to-production conversions are likely to be monitored as an important indicator of execution progress.
How It Stacks Up to Competitors
C3.ai’s IPD-centric approach contrasts with the enterprise AI strategies employed by peers such as Palantir Technologies Inc. (PLTR - Free Report) and Snowflake Inc. (SNOW - Free Report) .
Palantir has emphasized rapid production deployments through its Artificial Intelligence Platform, converting early engagements into large, multi-year enterprise agreements within relatively short timeframes. Management highlighted accelerating U.S. commercial growth, expanding total contract value and quick scale-ups as customers deploy AI broadly across operations. This approach prioritizes speed and scope of adoption but assumes a high degree of organizational readiness and executive alignment early in the sales cycle.
Snowflake follows a different model, centered on consumption-based expansion within its AI Data Cloud. AI capabilities increasingly influence bookings and customer engagement, with revenue growth driven by sustained usage across existing workloads rather than discrete conversion milestones. Strong retention metrics and expanding AI adoption underpin this model, though revenue realization remains closely tied to customer consumption behavior.
C3.ai’s framework sits between these approaches. Unlike Palantir’s rapid scale-up strategy, C3.ai emphasizes smaller, well-defined deployments intended to validate economic outcomes before expansion. At the same time, IPDs differ from Snowflake’s organic consumption ramp by embedding explicit objectives, milestones and conversion intent at the outset. Management views this structure as a way to improve conversion quality and reduce execution risk over time.
AI’s Price Performance, Valuation & Estimates
Shares of C3.ai have declined 21.5% in the past three months compared with the industry’s fall of 3.1%.
AI Three-Month Price Performance
Image Source: Zacks Investment Research
From a valuation standpoint, AI trades at a forward price-to-sales ratio of 6.03, significantly below the industry’s average of 16.47.
Image Source: Zacks Investment Research
The Zacks Consensus Estimate for AI’s fiscal 2026 earnings per share (EPS) implies a year-over-year downtick of 195.1%. However, the EPS estimates for fiscal 2026 have increased in the past 60 days.
Image: Bigstock
Can C3.ai's IPD-Led Sales Reset Support a More Durable Growth Path?
Key Takeaways
C3.ai, Inc. (AI - Free Report) is entering a new phase of commercial execution, with Initial Production Deployments (IPDs) emerging as the central driver of its sales reset. Management is repositioning IPDs as the primary entry point for customers, emphasizing smaller, high-impact deployments that demonstrate measurable economic value before scaling across the enterprise. This shift reflects an emphasis on disciplined, conversion-focused deployments rather than large, upfront expansions.
Activity levels around IPDs showed signs of stabilization in the second quarter of fiscal 2026. During the quarter, C3.ai signed 20 new IPDs, including six generative AI IPDs, bringing cumulative IPDs to 394, with 269 currently active across pilots, extensions or conversion discussions. The company noted that IPDs increasingly serve as the proving ground where customers validate outcomes and build internal confidence for broader production rollouts. Several of AI’s largest accounts — including GSK, Dow and Holcim — followed this exact path, starting with focused IPDs before expanding adoption across multiple business units.
Operationally, the company has tightened execution standards around IPDs. Management outlined stricter upfront qualification, milestone-based delivery requirements and increased executive oversight. These measures are intended to align each deployment with a clearly defined economic objective and improve conversion outcomes. Management characterized recent performance challenges as primarily execution-related rather than reflective of weakening enterprise AI demand.
From a financial perspective, the IPD-led approach carries near-term implications. A higher mix of IPDs and associated delivery requirements has contributed to moderated gross margins, as initial deployments typically involve higher upfront costs and a heavier services component. Management described this margin impact as a deliberate trade-off, prioritizing conversion quality and long-term customer value over near-term margin expansion.
Looking ahead, management indicated that improving IPD execution and conversion discipline will remain central to restoring more consistent growth. Leadership emphasized that demonstrating measurable economic value early in deployments is intended to support broader production adoption over time. As the sales reset progresses, IPD-to-production conversions are likely to be monitored as an important indicator of execution progress.
How It Stacks Up to Competitors
C3.ai’s IPD-centric approach contrasts with the enterprise AI strategies employed by peers such as Palantir Technologies Inc. (PLTR - Free Report) and Snowflake Inc. (SNOW - Free Report) .
Palantir has emphasized rapid production deployments through its Artificial Intelligence Platform, converting early engagements into large, multi-year enterprise agreements within relatively short timeframes. Management highlighted accelerating U.S. commercial growth, expanding total contract value and quick scale-ups as customers deploy AI broadly across operations. This approach prioritizes speed and scope of adoption but assumes a high degree of organizational readiness and executive alignment early in the sales cycle.
Snowflake follows a different model, centered on consumption-based expansion within its AI Data Cloud. AI capabilities increasingly influence bookings and customer engagement, with revenue growth driven by sustained usage across existing workloads rather than discrete conversion milestones. Strong retention metrics and expanding AI adoption underpin this model, though revenue realization remains closely tied to customer consumption behavior.
C3.ai’s framework sits between these approaches. Unlike Palantir’s rapid scale-up strategy, C3.ai emphasizes smaller, well-defined deployments intended to validate economic outcomes before expansion. At the same time, IPDs differ from Snowflake’s organic consumption ramp by embedding explicit objectives, milestones and conversion intent at the outset. Management views this structure as a way to improve conversion quality and reduce execution risk over time.
AI’s Price Performance, Valuation & Estimates
Shares of C3.ai have declined 21.5% in the past three months compared with the industry’s fall of 3.1%.
AI Three-Month Price Performance
Image Source: Zacks Investment Research
From a valuation standpoint, AI trades at a forward price-to-sales ratio of 6.03, significantly below the industry’s average of 16.47.
Image Source: Zacks Investment Research
The Zacks Consensus Estimate for AI’s fiscal 2026 earnings per share (EPS) implies a year-over-year downtick of 195.1%. However, the EPS estimates for fiscal 2026 have increased in the past 60 days.
Image Source: Zacks Investment Research
AI stock currently has a Zacks Rank #3 (Hold). You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.