Reed J. Stepleman

Enhancement requests accumulate in design workshops, one at a time, and each looks reasonable on its own.

• The initial development effort, they say, is usually the smallest part of the investment.
• The real costs come later: regression testing during upgrades, documentation and knowledge transfer, production support, future modifications, code ownership, and long-term maintenance.
• The post offers a Customization Decision Framework and five questions executives should ask before approving custom development: Does the request solve a measurable business problem?
• Could standard configuration achieve the same result?
• Is it a must-have or a user preference?
• Has full lifecycle ROI been evaluated?
• Who owns the code five years from now?
• Stepleman says the goal is not to eliminate customization, but to make each one earn its place through measurable business value and sustainable lifecycle economics.

Stepleman describes the design-workshop pattern directly — "Can we add one more screen?"

• "We need another status."
• "Let's modify this workflow." — and draws on nearly three decades of implementations to outline what happens after go-live: some customizations are never enabled or adopted by operations, some become obsolete when a later software release provides the capability out of the box, and others become permanent change requests requiring continuous maintenance.

Stepleman describes the framework as the foundation of the first paper in an Executive Insights series covering customization governance, ROI analysis, software ownership, and warehouse investment assurance. The argument begins a longer body of work rather than standing as a single post.

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The issue, they argue, is not a lack of warehouse data but the time spent finding and interpreting it — time that could go toward leading the operation.

• Their proposed remedy is an agentic "digital supervisor" that detects operational risks in real time, investigates labor, inventory, and execution impacts, recommends recovery actions, executes approved changes, and verifies that the operation has recovered.
• The goal, Stepleman says, is not to replace supervisors but to give them a support team so they can focus on decisions, coaching, and operational outcomes.

Replenishment falls behind, putting 230 UPS orders at risk of missing the carrier cutoff.

• In most warehouses, the supervisor then gets a phone call or radio message, opens reports, walks the floor, validates the issue, gathers context, coordinates teams, makes a decision, follows up, and checks whether recovery worked — losing valuable minutes at every step.
• Stepleman planned to present the Accenture agentic approach at Manhattan Exchange, using APIs and the Manhattan Agent Foundry.

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Different warehouse situations warrant different levels of engagement, and the modes give implementation teams a way to discuss them. The product is positioned inside the IBM Warehouse Navigator Platform, spanning warehouse design and configuration through testing, deployment, optimization, and ongoing support.

IBM Supply Chain AI Ops is described as an operational support solution focused on Manhattan Active Warehouse Management.

• It integrates with enterprise ticketing systems and uses agentic AI to help teams triage, analyze, and accelerate resolution of warehouse execution and workflow problems.
• Its three modes let teams choose the right level of human involvement.

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They connect the announcement to practical outcomes: using AI agents to help clients automate warehouse operations, accelerate decision-making, improve issue resolution, and improve the implementation and support experience across the Manhattan Active platform. IBM's Supply Chain Navigator is positioned as a differentiator supporting organizations throughout the Manhattan Active Warehouse Management journey, from design and configuration through testing, deployment, optimization, and ongoing support.

Stepleman quotes Manhattan CTO Sanjeev Siotia — not offering their own view — saying, "The future belongs to software that adapts to us." They also credit Manhattan Associates and the shared customer base for the work.

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They say building the Warehousing & Logistics practice and working with talented colleagues and clients was one of the most rewarding experiences of their career, name team members individually, and thank IBM colleagues, clients, and friends for their partnership and friendship. Leaving is hard, the post says, precisely because the working relationships were genuine.

Stepleman names several team members and colleagues, thanks them for their talent, commitment, and willingness to tackle difficult challenges, and says they look forward to the next chapter without specifying what it is.

Stepleman says they have decided to leave IBM for a new opportunity. The next employer and role are not named; ask for details if those matter to your decision.

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Newcomers should start here; the post explains the lens they apply to everything else they write about implementations.

Stepleman describes how requests pile up in design workshops, one screen, status, or process modification at a time.

• Each seems reasonable alone; together, they add months to implementation, raise costs, complicate upgrades, and create technical debt.
• Drawing on nearly three decades of warehouse implementations, they identify a pattern: some customizations are never enabled or adopted by operations, some become obsolete when a later software release delivers the same capability out of the box, and others become permanent change requests.
• The initial development effort is often the smallest part of the investment.
• Later costs include regression testing during upgrades, documentation and knowledge transfer, production support, future modifications, code ownership, and long-term maintenance.
• The post ends with five questions executives should ask before approving custom development, including whether the request solves a measurable business problem, whether standard configuration could achieve the same result, and who owns the code five years on.
• Stepleman also references a Customization Decision Framework attached to the post and an Executive Insights paper series.
• Ask for the attachment and papers before using the framework.

For brands planning a campaign

For vendors selling warehouse software or implementation services, the five questions make ready-made decision criteria for a webinar or advisory session on customization governance. The framework also gives a co-marketing partner a natural series structure rather than a one-off event.

This is the clearest example of the problem framing behind their agentic warehouse work.

Stepleman argues that supervisors are expected to work as data analysts, sifting reports, reconciling multiple systems, and chasing information before responding to a problem.

• The example: replenishment falls behind, putting 230 UPS orders at risk of missing the carrier cutoff.
• In most warehouses, the supervisor gets a call or radio message, opens reports, walks the floor, validates the issue, gathers context, coordinates teams, decides, follows up, and checks whether recovery worked—losing minutes at each step.
• The problem, Stepleman argues, is not a lack of data but the time spent finding and interpreting it.
• The post previews an Accenture presentation at Manhattan Exchange on an agentic approach using APIs and the Manhattan Agent Foundry.
• It lists what a digital supervisor would do: detect risks in real time, investigate labor, inventory, and execution impacts, recommend recovery actions, execute approved changes, and verify recovery.
• Stepleman says the goal is not to replace supervisors but to support them so they can focus on decisions and coaching.

For brands planning a campaign

The 230-order example gives a co-marketing webinar a concrete opening for making the case for agentic warehouse operations without leading with product language. It also signals the operational specificity an audience would expect from Stepleman in that format.

It spells out what the agentic tooling does and how humans are involved.

This conference promotion describes IBM Supply Chain AI Ops as an MAWM-focused operational support solution that integrates with enterprise ticketing systems and uses agentic AI to help teams triage, analyze, and accelerate resolution of warehouse execution and operational problems.

• Stepleman outlines three operating modes: Copilot, where AI assists with recommendations and humans drive resolution; Advisor, where AI guides analysis and orchestrates next steps; and Autonomous, where defined operational scenarios are resolved through automated processes.
• The product sits within an IBM Warehouse Navigator Platform spanning warehouse design and configuration through testing, deployment, optimization, and ongoing support.
• Stepleman frames it as a combination of Manhattan expertise and AI-driven automation.
• The technical detail reflects the vendor's own description, not an independent evaluation.

For brands planning a campaign

The Copilot/Advisor/Autonomous tiering offers a useful comparison for vendors packaging agentic capability by degree of autonomy. Partners considering Stepleman for an advisory engagement can use the post to test whether their product story fits the same register.

This is the clearest example of the partner-facing register a co-marketing event would need.

In this recap of the Manhattan Associates Momentum conference in Las Vegas, Stepleman highlights the Manhattan Marketplace announcement, describing a shared ecosystem where partners and customers can discover and deploy AI agents, extensions, and accelerators running natively on ActivePlatform, with no bolt-ons or heavy integrations.

• The post quotes Manhattan CTO Sanjeev Siotia: “The future belongs to software that adapts to us.”
• Stepleman says IBM is looking to leverage AI agents to help clients automate warehouse operations, accelerate decision-making, improve issue resolution, and improve the implementation and support experience across the Manhattan Active platform.
• They single out IBM's Supply Chain Navigator as a differentiator supporting organizations across the entire Manhattan Active Warehouse Management journey, from warehouse design and configuration through testing, deployment, optimization, and support.
• The post credits Manhattan Associates and the shared customer base for the work.

For brands planning a campaign

This post offers a template for ecosystem positioning in a partner pitch: tie a conference moment to a named implementation journey. It also shows Stepleman publicly crediting a partner platform, useful context for vendors considering a co-marketing relationship.

Confirm their current role before discussing a position or partnership.

Stepleman announces a move from IBM to a new opportunity, without naming the next role or employer.

• The post thanks IBM for the experience, opportunities, and people, and describes building the Warehousing & Logistics practice as one of the most rewarding experiences of their career.
• They thank several colleagues by name, along with IBM colleagues, clients, and friends.
• Stepleman writes that leaving is not easy when you genuinely enjoy the people you work with, and says they await the next chapter with excitement.

For brands planning a campaign

Confirm Stepleman's current affiliation directly before discussing a campaign, speaking, or advisory engagement: this post establishes a transition but not their destination. It also offers a public, generous departure statement as context for anyone weighing a long-term partnership.

In design workshops, requests accumulate—one more screen, another status, a modified workflow.

• Individually they seem reasonable; collectively they can add months to an implementation, increase costs, complicate upgrades, and create years of technical debt.
• After go-live, some customizations are never enabled or adopted by operations; others become obsolete as processes evolve or new software releases provide the capability out of the box, while some become permanent change requests.
• The real costs come later through regression testing during upgrades, documentation and knowledge transfer, production support, future modifications, code ownership, and long-term maintenance.
• Reed proposes a Customization Decision Framework and executive questions: Does this solve a measurable business problem?
• Can standard configuration accomplish the same objective?
• Is it truly a must-have or simply a user preference?
• Has the full lifecycle ROI been evaluated?
• Who owns the code five years from now?

Uses a design-workshop pattern, go-live outcomes, and a short set of executive questions to turn a technical approval decision into an investment review.

Reed does not argue against customization. They say each customization should earn its place through measurable business value, sustainable lifecycle economics, and long-term operational flexibility.

For brands planning a campaign

A concrete executive-education topic for a vendor webinar or advisory engagement: customization governance and lifecycle ROI, with a usable framework as the takeaway. It lets the vendor address a common WMS implementation pain point without leading with product features.

Examples: Source 1

In one example, replenishment falls behind, putting 230 UPS orders at risk of missing the carrier cutoff.

• In most warehouses, the supervisor gets a phone call or radio message, opens reports, walks the floor, validates the issue, gathers context, coordinates teams, makes decisions, follows up, and checks whether recovery actions worked.
• By then, valuable minutes are lost.
• The challenge, Reed says, is not a lack of data; supervisors spend too much time finding and interpreting information instead of leading the operation.
• At Manhattan Exchange, Reed planned to share how Accenture is delivering an agentic approach to warehouse operations using both APIs and the Manhattan Agent Foundry.
• They describe a digital supervisor that can detect operational risks in real time, investigate labor, inventory, and execution impacts, recommend the best recovery actions, execute approved changes, and verify that the operation has recovered.
• The goal is not to replace supervisors but to give them a digital warehouse support team.

Starts with an operational scenario, quantifies the order risk, lists the supervisor’s real steps, then maps each step to a proposed agent capability.

For brands planning a campaign

A strong topic for a co-marketing webinar on agentic AI in warehouse execution.

• The vendor can focus on practical orchestration workflows rather than general AI claims: risk detection, impact investigation, recommended actions, approved execution, and verification.
• It also lends itself to an advisory discussion on designing human-in-the-loop operational AI.

Examples: Source 1

Reed quotes Manhattan CTO Sanjeev Siotia saying, “the future belongs to software that adapts to us.”

• They say IBM is excited about what this means for AI-enabled warehouse operations and supply chain transformation, and about leveraging AI agents to help clients automate warehouse operations, accelerate decision-making, improve issue resolution, and enhance the implementation and support experience across the Manhattan Active platform.
• Reed positions IBM’s Supply Chain Navigator as a key differentiator within Active Warehouse implementations, supporting organizations across the entire Manhattan Active Warehouse Management journey from warehouse design and configuration through testing, deployment, optimization, and ongoing support.

This is a conference announcement and partner perspective, not an independent product evaluation.

For brands planning a campaign

Useful for a vendor evaluating Reed as a speaker or advisor on partner ecosystems and platform-native AI.

• A co-marketing session could focus on how partners build and deploy agents inside an existing WMS platform, using the Marketplace announcement as a concrete news hook.
• The post also shows Reed can connect an ecosystem launch to implementation and support outcomes.

Examples: Source 1

They list three operational modes: Copilot Mode for AI-assisted recommendations with human-driven resolution, Advisor Mode for AI-guided analysis and orchestrated next steps, and Autonomous Mode for automated resolution workflows in defined operational scenarios.

• IBM Supply Chain AI Ops is part of the IBM Warehouse Navigator Platform, supporting organizations across the entire MAWM journey from warehouse design and configuration through testing, deployment, optimization, and ongoing support.
• The platform combines deep Manhattan expertise with AI-driven automation to accelerate implementations, improve operational performance, and simplify warehouse operations.

Uses a booth/demo format and a three-mode taxonomy to make an AI support product understandable to warehouse and implementation audiences.

For brands planning a campaign

Gives a vendor a clear framework for an advisory or webinar segment: match the level of AI engagement to operational risk and human oversight. The three modes provide a ready-made agenda for a session on implementing agentic support without over-automating.

Examples: Source 1

They say they are grateful to IBM for the experience, the opportunities, and most importantly the people, and that building the Warehousing & Logistics practice and working with talented colleagues and clients has been one of the most rewarding experiences of their career.

• Reed thanks named team members and IBM colleagues, clients, and friends for partnership, support, and friendship, and says leaving is never easy when you genuinely enjoy the people you work with.
• They are excited about what is next but will always be grateful for the IBM chapter.

The post is a farewell and does not describe the new opportunity.

For brands planning a campaign

Reed’s posts show a recent career transition and a practice-building background.

• Before naming Reed in a co-marketing engagement, confirm their current affiliation and approval path.
• Their emphasis on team-building and client relationships could support an advisory angle on standing up or scaling a warehousing practice, if their current role is compatible.

Examples: Source 1

Recurring approaches include naming a familiar warehouse problem, giving a concrete scenario or pattern, translating it into executive or architectural choices, pointing to a framework or mode taxonomy, and closing with a question or a call to a booth or session.

• The customization post stands apart as a vendor-neutral framework.
• The IBM and Manhattan posts translate partner announcements and product support modes into implementation and support outcomes.
• The career-transition post adds a practice-building and team-leadership dimension.

Short posts that move from a practical problem to a structured decision aid or product capability, often with numbered questions, bullet lists, or named modes.

For brands planning a campaign

A vendor can evaluate Reed for webinar panels, conference sessions, and advisory conversations. The customization post shows they can carry a topic without a product announcement; the product-announcement posts show they can connect partner news to implementation outcomes.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

They promote IBM Supply Chain AI Ops at the Momentum conference, describe IBM Supply Chain Navigator as part of Manhattan Active Warehouse Management implementations, and frame IBM as a leader in agentic supply chain operations.

• A later post says they built IBM's Warehousing & Logistics practice and are leaving the company, thanking named teammates and colleagues.
• Ask where they are headed next.

View example

They also promoted their speaking slot and an Accenture booth demo at Manhattan Exchange.

View example

Ask how they are affiliated with Accenture.

View example

Regnier argues that agentic AI moves organizations from recommendations to completed work, and that projects fail when teams skip the groundwork of mapping how work gets done and defining what successful completion looks like.

• Stepleman makes a comparable case inside warehouse execution: a digital supervisor detects risk, recommends and executes approved recovery actions, then verifies recovery.
• Both use short LinkedIn posts to translate a technology shift into an operational decision for supply chain leaders.

Regnier focuses on the broader readiness question and frontline workforce performance, drawing on statistics from Gartner and MHI and a history of supply chain technology spanning transaction visibility and execution.

• Stepleman focuses on warehouse management software itself: customization economics, WMS implementation and support, and partner platforms such as Manhattan Active Warehouse Management.
• The detail is specific to a warehouse system’s lifecycle.

Read original · Original 2

They use short LinkedIn posts tied to conferences, panels, and webinars rather than long articles. Kallen writes about turning traceability mandates into practical data architectures; Stepleman writes about turning platform announcements and agentic AI claims into implementation and support outcomes.

Kallen focuses on regulatory compliance and traceability technology: the EU Digital Product Passport, RFID, serialization, and cross-border data interoperability.

• Stepleman focuses on warehouse execution software, including customization governance, lifecycle ROI, and agentic operations inside a WMS.
• Their subject matter differs, as does their format: Kallen leans toward event promotion and panel framing, while Stepleman more often carries a single argument through a post.

Read original · Original 2

White’s K³ framework separates structured data, trusted analytics, and unstructured judgment, and argues that AI can now capture that judgment. Stepleman’s Customization Decision Framework and Executive Insights series take a similar approach to WMS customization, turning accumulated implementation experience into a reusable decision aid.

White focuses on knowledge capture and the transfer of experienced judgment, including logistics questions such as container status and customs clearance across TMS, WMS, and rate engines. Stepleman focuses on warehouse software implementation and agentic warehouse operations, with less emphasis on retiring workforces and knowledge handoffs.

Read original · Original 2

Regnier frames the problem with two numbers: a prediction that 40% of agentic AI projects will be canceled by the end of 2027, alongside data that 65% of supply chain leaders plan to invest in AI.

• They divide supply chain technology into three eras: transactions (EDI, ERP, WMS), visibility (analytics, track-and-trace, control towers), and execution.
• For a vendor audience, the useful part is the readiness checklist: map how work actually gets done, connect planning and execution systems to frontline activity, capture the exceptions people handle every day, define what successful completion looks like, and treat the frontline as part of the intelligence loop rather than a receiver of AI instructions.
• A second post adds the workforce angle: turnover and labor costs as a performance leak, and claims that fulfillment and distribution operations with 20,000 to 200,000 frontline staff gain an average of 4% quality improvement and reach productivity goals weeks faster with new hires.
• Regnier is a stronger fit than Stepleman for a webinar aimed at operations leaders asking who will absorb an agentic system and how it will be adopted.

Regnier's two posts do not cover warehouse software customization governance, the lifecycle costs of custom development, or a framework for approving enhancement requests.

• They also name no warehouse platform, partner ecosystem, or agent product, unlike Reed's posts, which name Manhattan Marketplace, Manhattan Agent Foundry, and IBM Supply Chain AI Ops with Copilot, Advisor, and Autonomous modes.
• A vendor selling customization governance should supply that content or pair Regnier with a second speaker.
• The reviewed material spans four years—September 2022 and September 2026—so the posts read as separate arguments, not a continuing account of implementation work.

Examples: Source 1 · Source 2

Kallen's panel at VARTECH 2026, 'The Compliance Gold Rush: Monetizing Global Traceability Mandates,' examines EU Digital Product Passport requirements and asks which emerging technologies—blockchain, IoT, cloud serialization—hold up for cross-border compliance and audit readiness; what data-sharing architectures make the passport interoperable across multi-tier supply chains; and where US systems struggle to bridge to EU requirements.

• A second post covers a Beontag webinar on RFID and real-time visibility for manufacturers, including asset utilization, loss reduction, and the connected factory, tied to Pack Expo.
• Together, the posts address how compliance obligations become connected, auditable data rather than paperwork—a distinct buying conversation for supply chain software vendors, separate from customization governance.

Kallen's posts do not discuss warehouse software customization economics, an approval framework, or agentic AI operations—the material a vendor would need for a webinar on custom development ROI or matching autonomy levels to operational risk.

• Traceability and compliance also bring their own vendor landscape (RFID, serialization, interoperability), which could compete with the host's product story rather than reinforce it.
• Using Kallen means redesigning the session around regulatory mandates and connected data, with customization and agent topics dropped or assigned to another speaker.

Examples: Source 1 · Source 2

White describes forty years in the business and notes that answers usually exist in the TMS, WMS, rate engine, or someone's memory, but are filed by loads, lanes, and cost centers rather than by how a client asks.

• White argues that cognitive AI layered over those systems can answer routine lookups—container location, customs clearance—when asked, while judgment calls still reach people with the data already in hand.
• A second post develops the same theme: career judgment about a failing bearing or the carrier representative who picks up at 10 p.m. never makes it into a succession binder.
• White proposes a three-layer model (structured data, trusted analytics, unstructured judgment) and a three-part article series written for the RapidCanvas AI transformation series.
• White is the better choice for an audience of operations executives weighing institutional memory and first-year-versus-fifth-year planner performance, and the published articles give a vendor material to point attendees to.

White's posts do not cover the mechanics of customization approval: how enhancement requests accumulate in design workshops, which customizations go unused after go-live, or the lifecycle costs of regression testing, documentation, support, and code ownership.

• They also name no platform, partner ecosystem, or tiered autonomy model such as the Copilot, Advisor, and Autonomous structure.
• The two posts promote a series and reflect on the field rather than evaluate or compare products.
• A vendor seeking independent software assessment should ask another source to cover that, along with customization economics.

Examples: Source 1 · Source 2