AI-Driven S&OP for Financial Impact: Key Takeaways From Our Latest Webinar

AI is reshaping how organizations plan, align, and make decisions, and S&OP is emerging as one of the functions most affected by this shift. In our recent webinar, Natalie Eksi, Chief Growth Officer at Streamline, and Adam Basson, CEO of FlexChain Holdings & President of CSCMP South Florida, explored what AI-driven S&OP looks like in practice, why many initiatives stall, and how teams can unlock meaningful financial impact by approaching automation the right way.

Below is a recap of the core insights.

AI-Powered S&OP: A New Stage of Evolution

Planning cycles are becoming faster, more data-heavy, and more interconnected than ever before. AI is stepping in not to replace planners, but to help them keep pace with complexity and make better, more informed decisions.

Industry data reinforces this direction. According to McKinsey, agentic AI is projected to generate more than 60% of the incremental value from AI across commercial functions such as marketing and sales. Their research also shows that companies adopting automated planning approaches achieve 3–5% annual productivity improvements and more than 10% growth uplift at scale. EY’s 2024/2025 study adds further context: while 73% of supply chain and operations leaders plan to implement GenAI, only 7% have fully deployed it, highlighting a clear execution gap.

The takeaway:

The technology is ready, but organizational readiness varies widely.

The AI Automation Paradox

Despite significant interest in AI, many initiatives struggle to deliver business-level results.

Gartner’s research highlights a “productivity paradox”: individual workers save an average of 4.11 hours per week using GenAI tools, but these gains rarely translate into team-level outcomes or measurable ROI. At the same time, EY reports that 62% of leaders have already reassessed or paused their GenAI projects due to unclear scope, misaligned expectations, or lack of governance.

This reality underscores a simple truth:

AI alone doesn’t transform S&OP. The workflows, decision structures, and cross-functional integration around it do.

A Practical Framework for AI-Based Automation

Streamline presented a practical, three-zone framework for organizations looking to adopt AI responsibly while maximizing impact.

The first zone focuses on low-consequence tasks – the “white space” where AI can support work that is useful but not business-critical. This includes generating product copy, creating content variations, translating materials, or summarizing research. Because errors here carry minimal risk, this zone is ideal for experimentation and early wins. AI becomes a way to scale activities teams would never fully staff.

The second zone is supervised automation, where AI operates as a co-pilot rather than an autonomous engine. It can suggest forecast refinements, routing decisions, or early planning inputs, but every recommendation still passes through human validation. This stage relies on intentional oversight – the quick internal check of “Does this make sense?” Human judgment remains central as AI accelerates analysis and expands scenario possibilities.

The third zone covers strategic, high-impact automation – areas where AI directly influences decisions tied to financial performance, such as inventory optimization, production scheduling, pricing logic, or capacity-driven S&OP recommendations. These functions offer outsized value but require careful, staged implementation. Clean data, governance, and a multi-phase rollout are essential.

The principle is straightforward: the more critical the process, the more deliberate the automation should be.

This is where AI can deliver the largest financial uplift, but only when teams are prepared.

Building Confidence and Financial Impact

AI drives value when teams trust the workflow that surrounds it. This trust comes from transparency, not complexity. Planners must understand how AI generates recommendations, be able to challenge or adjust them, and compare multiple scenarios.

Organizations that combine AI with strong governance and disciplined processes consistently see the strongest results, including better cash flow, reduced COGS, and improved asset utilization, as highlighted by EY’s research.

Ultimately, AI creates impact when teams have both the confidence and competence to apply it effectively.

The Human Advantage in an AI-Driven Era

A core theme throughout the webinar was that AI elevates human expertise; it doesn’t replace it.

AI excels at what machines do best: processing large datasets, recognizing patterns, and running simulations. Humans excel at managing trade-offs, aligning cross-functional priorities, evaluating risk, and making final decisions.

As  noted,

“AI expands your options – the human decides which option.”  

– Adam Basson

This balance between automation and human judgment is quickly becoming the defining characteristic of modern S&OP.

Looking Ahead: S&OP in 2026 and Beyond

EY predicts that by 2035, nearly 45% of supply chains may operate with mostly autonomous processes. This doesn’t imply hands-off planning, but it does signal a future defined by faster decision cycles, AI-assisted scenario switching, and deeply integrated processes between finance, supply, and demand.

The S&OP landscape ahead will be shaped by:

• Real-time, AI-assisted decision flows

• Unified, cross-functional plans

• Rapid scenario modeling

• Greater resilience and predictability across operations

Organizations that begin building these capabilities today will be best positioned to capture the performance advantages of the AI-driven decade.