If Gartner’s latest supply chain research makes anything clear, it’s this: AI and advanced planning are no longer experiments; they’re becoming the operating system of modern supply chains.

Gartner reports that 72% of supply chain organizations are already implementing AI, signaling a shift from “Should we adopt AI?” to “How do we implement AI in a way that elevates decisions, workflows, and business outcomes?”

At Streamline, that question hits home. Our mission has never been to plan for a perfect world. It’s to help companies plan better together in a very imperfect one.

“We don’t plan to achieve perfection. We plan to achieve common goals.”

Below are five key takeaways from Gartner and how they align with Streamline’s vision for the future of AI-powered supply chain planning.

1. From Single-Number Plans to Scenario Planning 4.0

Gartner is clear: static, single-number plans no longer work in today’s volatile environment. The future lies in Scenario Planning 4.0, a continuous, dynamic, and iterative approach to decision support.

What Scenario Planning 4.0 Looks Like

• Continuous scenario experimentation, not just a few what-ifs

• A shift from “accuracy at all costs” to resilience, adaptability, and opportunity capture

• Always-on decision support instead of annual planning cycles

How Streamline Is Building This Future

• Enabling teams to stress-test demand, supply, lead times, and constraints under many possible futures

• Helping planners shift from “we hope this plan holds” to “we know how to respond when it doesn’t.”

• Giving leaders clear, explainable trade-offs: service vs. cost, inventory vs. risk, growth vs. resilience

Scenario planning isn’t just a feature – it’s a mindset: plan to learn, not to be right once.

2. Agentic AI: From Forecasting to Prescience, Decisioning, and Tracking

Gartner highlights the rise of agentic AI – not just predictive models, but systems that perceive, decide, and act within planning workflows. At Streamline, we express it simply:

Agentic AI = Prescience + Decide + Track

• Prescience: Detect shifts, bottlenecks, and service risks before they escalate

• Decide: Recommend (and eventually execute) actions such as prioritizations, reallocations, and sourcing changes

• Track: Learn from outcomes and feed insights back into the next decision cycle

Where Streamline Is Headed

• Evolving from dashboards to AI teammates for planners

• Moving from static rules to intelligent agents that orchestrate routine decisions

• Building an AI layer that is transparent and explainable, not a black box

AI shouldn’t replace planners – it should amplify what humans can see, decide, and improve.

3. Start With High-Impact, Measurable, Quickly Solvable Use Cases

Gartner underscores that AI success relies on choosing the right first use cases. The winning organizations start with impact, measurability, and rapid time-to-value, not experimental science projects.

Streamline’s Use Case Filter

• Impactful: Direct effect on service levels, working capital, planner time, or customer experience

• Measurable: KPIs set upfront (forecast bias, stockouts, expediting cost, planning cycle time)

• Quickly solvable: Value emerging within weeks, not years

One of our core principles: Identify KPIs before deploying AI. Teams shouldn’t wait months to find out whether a new capability helped – they should know by the next planning cycle.

4. The Foundation Matters: Bad Data = Bad AI

Gartner is truthful: AI can’t outperform the quality of its data. Even the best models will fail when fed:

• Incomplete or messy master data

• Inconsistent hierarchies

• Gaps in transactional history

• Poorly maintained planning parameters

Streamline’s Data-First Philosophy

• Centralizing clean, coherent planning data in one environment

• Surfacing data issues before they distort forecasts and decisions

• Providing full transparency so planners can ask, “Why did AI recommend this?” – and see the logic behind it

There is no shortcut here. Trustworthy AI requires a trustworthy data foundation.

5. People, Collaboration, and AI Upskilling as Core Competencies

Gartner stresses that AI fails when organizations treat people as an afterthought, when there’s no upskilling, no collaboration, and no clear ownership model.

Streamline’s People-First Approach

Start With AI Upskilling. Before scaling AI, teams must understand:

• What AI can and cannot do

• How to collaborate with AI as a co-pilot

• How to challenge, refine, and validate AI-driven recommendations

Upskilling isn’t the final stage – it’s the foundation of AI transformation.

Use AI to Give People Time to Think.  AI should reduce operational noise by automating:

• Data gathering

• Repetitive analysis

• Routine decision checks

So humans can focus on:

• Cross-functional alignment

• Scenario interpretation

• Strategic trade-offs

• High-impact decision-making

Build an AI Center of Excellence. A center of excellence doesn’t need to be large – it needs to be intentional:

• Own use case selection

• Establish governance and guardrails

• Track KPIs and measure value

• Share best practices and internal success stories

Over time, this becomes the engine of continuous optimization.

Where We’re Headed Together

Across Gartner’s findings, several themes emerge:

• Scenario-driven planning is replacing static plans

• Agentic AI introduces prescience, decisioning, and tracking

• High-quality data becomes non-negotiable

• Focused use cases outperform vague AI ambitions

• People and collaboration remain at the center of success

This aligns perfectly with Streamline’s trajectory.

We’re building a platform that empowers teams to plan around uncertainty – not despite it – using AI that’s transparent, collaborative, and trustworthy.

Because in the end: We don’t plan to achieve perfection. We plan to achieve common goals.

The future of planning belongs to teams where humans, data, and AI operate as one.