AI-native supply chain planning is becoming a more visible part of the technology landscape. Over the past year, Alex Pradhan has spoken with a growing number of newer supply chain planning vendors and identified a number of characteristics and trends that distinguish AI-native platforms from traditional systems being retrofitted with AI.

In her original LinkedIn article, Pradhan describes what she means by AI-native and outlines nine trends she is seeing across the market. Steve Banker subsequently interviewed her for Forbes, exploring some of the ideas and challenging others.

This article provides a shorter overview of the key points. For the full analysis, see Alex Pradhan’s original LinkedIn article and Steve Banker’s Forbes interview with Alex.

What does AI-native mean?

AI-native is more than adding AI capabilities to an existing product. The idea is that the underlying architecture, data flows and user experience were designed around AI from the beginning.

Pradhan identifies several building blocks, including:

  • A unified data and context layer connecting data, business logic, decisions and real-world relationships
  • Composable and scalable architecture
  • Governed autonomous agents
  • Shared decision authority between people and AI
  • Continuous learning and adaptation
  • AI embedded in operations and workflows

The distinction matters because many established vendors are also modernizing their technology. The question is whether AI was designed into the underlying architecture or added to a system built for a different technology environment.

9 trends worth watching

1. Planning processes are being redesigned

Supply chain planning applications are moving beyond simply digitizing traditional planning processes such as monthly S&OP.

The focus is shifting toward continuously monitoring the supply chain and surfacing the right decision at the right time.

2. Decision intelligence is becoming more decision-centric

There is plenty of marketing around decision intelligence. But much of what is currently described that way still looks like workflows, dashboards and traditional planning processes.

The emerging opportunity is to explicitly model decisions, context, actions and the people or agents involved, and then orchestrate and govern them.

3. Decision quality is getting more attention

The focus is also shifting from measuring supply chain performance to asking whether decisions were appropriate given the information, priorities, risks and opportunities that existed at the time.

That becomes increasingly important as more decisions are supported or made by AI.

4. Planning is getting better at dealing with uncertainty

Probabilistic techniques are not new, but their use is expanding beyond demand planning into areas such as supply and inventory planning.

This is particularly interesting among newer AI-native vendors that are building digital representations of the supply chain and using them as a foundation for planning.

5. User experience is becoming more important

Supply chain software has historically been designed largely around complex processes and expert users. That is beginning to change.

The opportunity goes beyond chat interfaces and better-looking dashboards. Planners also need to understand why a system is making a recommendation, including the relationships, root causes, trade-offs and context behind it.

As organizations push toward greater automation, user experience becomes the foundation for trust, which drives adoption and, over time, greater degrees of autonomy.

6. Platforms are becoming more open and composable

More platforms are exposing how data, models, analytics, agents and business rules connect.

The same contextualized data can increasingly support planners as well as agents that monitor conditions, recommend actions and execute within defined guardrails.

Pricing models are changing too. Alongside traditional seat-based and application pricing, vendors are experimenting with consumption-based and hybrid models as AI introduces new compute costs.

7. Bring Your Own Cloud (BYOC) is emerging

Large enterprises with existing cloud agreements are looking for more flexibility in how AI-native applications are hosted and priced.

Pradhan expects this to develop primarily as a hybrid model rather than fully self-managed environments, with vendors supporting customer-owned cloud environments.

8. Traditional technology categories are blurring

The boundaries between supply chain planning, execution and other technology categories are becoming less distinct.

There is growing emphasis on decision-making, orchestration and agentic capabilities, while terms such as “decision layers” and “decision orchestrators” are emerging outside traditional market definitions.

9. The planning process itself may become more continuous

A broader theme running through these developments is a move away from planning as a sequence of predefined activities toward a more continuous decision process.

Rather than waiting for the next planning cycle, systems can increasingly monitor changing conditions and bring decisions forward as events occur.

What happens next?

AI-native supply chain planning is still a developing market. Some of the terminology is likely to change, and it remains to be seen which of these trends will prove durable.

The more important question may be what actually sits behind the label “AI-native”. Is AI genuinely embedded in the architecture, data and workflows, and can the system support the appropriate balance between human and automated decision-making?

For the full discussion of these trends, including Pradhan’s detailed perspective on AI-native architecture and the evolution of supply chain planning, read AI-Native Supply Chain Planning: 9 Trends Reshaping the Technology Landscape by Alex Pradhan.

For further discussion of the ideas, including Steve Banker’s questions and challenges, see What Is AI-Native Supply Chain Planning? in Forbes.