Category: Technology

For decades, DRAM shortages followed a predictable script: overshoot, crash, recover, repeat. Demand spikes would hit, manufacturers would add capacity, and the market would flood with chips again.  This time is different.

At first glance, the global DRAM shortage may look like a typical semiconductor cycle, but it is actually one of the first pain points that we are seeing as AI collides with physical resource limits. Consumers, enterprises, and governments are already on the rollercoaster.

More important than the demand and supply imbalance, there are other factors like strategic capacity control, long term AI demand (that is just ramping up), and geopolitical constraints all converging at once. And it’s the first time AI is materially crowding out everyone else.

When DRAM tightens, PCs, laptops, phones, and tablets get more expensive.  Automotive electronics systems face supply risk and I think we all remember what that looked like post-COVID. DRAM access becomes more unequal because priority (and manufacturing nodes) go to hyperscalers, leaving everyone else to fight over the remaining capacity.  This means that consumer prices on all electronics will rise because the companies that make them are paying 2x, 3x, 4x more for the memory chips that allow them to work.

Samsung, SK Hynix, and Micron control ~95% of the DRAM market and they optimize profitability to focus on high margin node migration. A disproportionate number of manufacturing lines go to the newest technology that is used for AI, so the manufacturing capacity that is left is not enough to supply everyone else who needs DRAM for their products (which is basically everyone making any technology hardware product).  So prices rise dramatically as companies fight over the available chips. What is different about this cycle of shortages is that it’s not just the lagging technology nodes that are not enough to supply demand, the companies buying up DRAM for expand their AI capabilities can’t get nearly enough to support their build plans. This was highlighted by the recent firing of a top tech executive who failed to sign multi-year contracts with memory vendors to lock in supply.  These chips will affect the winners and losers in the AI race because without them, all progress stops.

Another interesting aspect, which is typical of every cycle, is that bringing up a new node technology has low early yield (for every 100 chips manufactured maybe only 75 of them work).  As the technology matures, these numbers improve but that takes months to years to get them where the manufacturing capacity reaches an optimal level. So the more the technology transitions, the lower the overall yield of the factory.  And the other contributing factor is the cost of a new FAB.  Building additional manufacturing capacity takes 7-10 years and around $50B. These are strategically planned and advertised for years before they become reality, so there are no fast response options to demand spikes.  And the final consideration here is the EUV technology required to manufacture the advanced nodes that is prohibitively expensive, in extremely high demand, and also controlled by government policy and export controls limiting other potential suppliers from entering the market.  Here, Micron has the key home court advantage.

The demand curve is also changing, likely for the long-term, not just a short term bump.  GPU’s requiring high bandwidth memory (HBM), DDR5 RDIMM density required for training clusters combined with long qualification cycles locks supply to only a handful of customers. HBM is particularly destabilizing because it requires leading edge DRAM wafers, advanced  packaging capacity and in-demand manufacturing tools (like EUV).

In conditions like these, memory suppliers stop selling memory and they start allocating supply.  This is also not new and not specific to this AI-driven spike, but what is different is the allocation hitting the largest customers so hard that they are firing executives over it. This is a new level of scarcity.

Memory has always been the market leading commodity indicator for the semiconductor industry.  I am not totally convinced that this is changing but I do think there is a significant and fundamental shift that has already started because competing for memory is the first battle ground in the AI fight for resources.  The next will likely be water and energy but the global implications of the fight for memory may give us an estimate of scale of what is to come.

AI has started to change distribution economics in ways the industry has never experienced. McKinsey estimates that end-to-end AI adoption can reduce inventory by as much as 30 percent, cut logistics costs by double digits, and drive major improvements in service levels and working capital efficiency. Across the broader supply chain, early adopters are seeing significant gains in speed, resilience, and operating margin. And with AI-powered logistics projected to more than triple in market size by 2032, the direction is clear: the next era of distribution will be dominated by companies that can make the leap to operate like expert software platforms and maintain while optimizing their valuable warehouse networks.

The path to becoming an AI-first distributor begins with the foundational move of structuring all operational data in the cloud, even if the ERP and core systems remain on-prem. Using modern API and event-driven sync technologies, distributors can stream everything (quotes, purchase orders, shipments, transactions, supplier feeds, customer signals) into a cloud database that becomes the company’s first unified, real-time model of its business. This likely will have to be done step by step so it does not disrupt ongoing operations. It will take time and patience but will set the foundation of clean, structured, accessible data from which all future AI systems will be driven.

Once that cloud data spine exists, distributors can begin deploying specialized, AI-enabled point applications that immediately close capability gaps in their legacy systems. Pricing engines become more precise and governed. Quoting cycles shrink from hours to seconds. PO intake and document processing become automated. Predictive inventory models run independently of the ERP. Codified rules will be able to govern operational workflows with consistency. The hardest part is getting an entire organization to agree on what the operational workflow rules are.  This is where your experts on the ground come in.  Only the people that execute these tasks daily are the ones who can write the rules in such a way that will set up success. But the reality is that these systems will replace their daily functions, and for many (or dare I say most), this creates a lot of fear.  Upskilling, paths to the AI-enabled future, and genuine organizational trust as just as critical to the success of this stage of the transformation as the clean cloud data. These targeted applications function as intelligence layers grafted onto the existing architecture, delivering quantifiable ROI while quietly preparing the organization for a much larger transformation.

Over time, this incremental shift gives rise to the operating model of the AI-first distributor. In this future state, data becomes the actual system of record, not the ERP. Every operational event is captured in the cloud as a time-stamped, query-able event and rule-bounded AI engines sit on top to optimize decision making and routing. Instead of siloed systems making isolated choices, a coordinated AI-driven orchestration layer determines what to buy, where to place inventory, how to route orders, how to price, and most importantly, when and what to escalate to people that can make nuanced, impactful decisions. People transition from repeatable, transactional tasks to managing policies, exceptions, and relationships, the work that our teams are uniquely equipped for.

This vision requires cloud-native architecture as both a hosting choice and also an operating philosophy. Microservices, event buses, elastic compute, and API-first integrations create the speed, visibility, and flexibility needed to respond to volatility with intelligence rather than brute force. In this model, the distributor becomes a software platform that also happens to move physical goods. The physical network is still the core competency but the center of gravity shifts so everything orbits with precision around the cloud.

Digital inventory becomes an engine for growth. When a distributor has a reliable, real-time digital representation of stock, availability, and conditions across suppliers, 3PLs, customers, and global nodes, it can orchestrate far more inventory than it physically owns. This unlocks new business models such as marketplace fulfillment, selective inventory in high-value categories, and the ability to price and promise with unprecedented precision. Growth becomes constrained not by warehouse space or working capital but by how much of the ecosystem’s inventory the platform can see, model, and influence.

There are a couple potential paths to be the first AI-first global distributor. One is the SaaS platform that steps into distribution by combining cloud-native workflows with behavioral intelligence, then taking selective inventory risk where it can generate leverage. While technologically feasible, this route will struggle due to a lack of deep domain expertise. Distribution is a relationship driven, reputation sensitive, extremely complex ecosystem. Outsiders often underestimate how genuinely difficult it is to scale inside it.

The second path is an incumbent distributor that successfully shifts to cloud based and data first operation. These companies have unmatched supplier relationships, operational expertise, and physical networks. And many are already experimenting with AI in logistics, warehousing, sales, and operations. Their challenge is not vision. Rather, it is the massive amount of technical, process, and organizational debt. They must standardize data models across business units, move intelligence out of the ERP, and redesign workflows so AI-enabled decision engines operate within clear, compliant guardrails. The success stories here will be those who take these steps early, deliberately, and with long-term discipline, who can implement these changes without disrupting the flow of business.

The most likely winner is a hybrid: a company that blends the architectural ambition of a software firm with the operational mastery of a distributor. It will start in a narrow vertical where data advantages matter most, build a cloud-native data and events platform as the backbone, layer AI-driven decision engines on top, and maintain a thin but strategically important physical layer supported by a world-class exceptions and relationship team. Once the digital foundation is strong, it can plug into new physical networks globally and expand with less capital than those who have not made the leap.  I envision a possibility where an established distributor separates it’s cloud based operating system as an independent venture that operates like a software company, leverages the physical inventory of said distributor, and creates a broad network of partners to maximize access to physical inventory with minimal operational overhead.

Begin building your cloud data foundation now, even if your ERP remains on-prem for years. This single step unlocks all subsequent innovation and allows your organization to begin deploying the AI-enabled applications that will define the next decade of distribution. And then decide strategically how you want to participate in the evolving landscape: either double down on physical assets while partnering with cloud-native software platforms, or invest directly in the digital orchestration layer yourself. Either path is viable, what matters most is conviction.

The first true AI-first global distributor will treat data, intelligence, and decision engines as the core business, orchestrating a vast network of digital inventory while remaining capital-light in physical assets. They will expand through integrations, partnerships, and software and they will redefine how distribution flows.

I’m excited to see who builds it.

Procurement is a value engine under extreme pressure. McKinsey finds that managed spend per FTE is up ~50% vs. five years ago, while AI (including “agentic” systems) can lift procurement efficiency 25–40% if embedded correctly. Mature operating models correlate with ~5 percentage-point EBITDA impact. Yet core systems like P2P (Procure-to-Pay), SRM, and e-sourcing are still underused, leaving real money on the table.

Below are the high-leverage shifts for distributors and manufacturers, especially where BOMs, MRO, long-tail spend, engineering collaboration, and compliance make or break margins.

1) Redesign how work flows before you add “AI agents”

What most miss: leaders jump to tools; winners rewire work first.

• Split strategic vs. transactional work (explicit role design, not just a new org chart). Two-thirds of leaders already segregate these tracks and see cost, on-time delivery, and supplier performance gains.
• Stand up a Procurement COE that owns method and math, not only tools. Leading COEs codify cost-engineering (e.g., should-cost), analytics standards, and AI/e-sourcing methods; one chemicals firm cut 13% in raw-materials spend by industrializing should-cost.
• Engineer the “human + rules + AI” interface. Decide where humans approve, where deterministic rules hard-gate, and where AI proposes. Treat this like safety-critical design, not a chatbot bolt-on. McKinsey’s evidence links operating-model maturity—not model novelty—to profitability.

Why it matters for you: Without these seams defined, agentic AI becomes theater. With them, you convert busywork into governed automation and free scarce talent for category strategy.

2) Monetize the unsexy: P2P, e-sourcing, and tail-spend hygiene

What most miss: the ROI is hiding in under-adopted basics.

• P2P is still under-deployed (only ~60% of large orgs and ~30% of small have it), despite 2–5% cost-reduction potential. If your P2P UX is clunky, adoption dies. Start there.
• E-sourcing is used by only ~1/3 of orgs, yet a manufacturer achieved 20% savings in the notoriously messy MRO category by making it standard. Tail-spend automation in distribution is often the fastest dollar.
• Invoice-to-contract reconciliation with AI can expose silent value leakage; one global pharma found >$10M in weeks and renegotiated. If you ship parts, manage spares, or run DCs, this is low-risk/high-yield.

Action you won’t regret: make P2P + e-sourcing + AI reconciler a single 90-day program with adoption targets, not three tools.

3) Build “data products” that your agents can actually use

What most miss: poor signal = “AI agents” that meander.

Create small, owned “data products” that are stable interfaces for people and machines:

• Supplier Master (parent/child roll-ups, risk flags, payment terms, ESG attestations).
• Category & spec taxonomy (parts, alternates, cross-references, criticality codes).
• Contract clause library (fallbacks and redlines tied to risk/criticality).

McKinsey’s evidence: analytics and genAI pilots are widespread (~40% have piloted), but value shows up where the inputs are governed and reusable across use cases.

Distribution-specific win: thread your line-card → alternates → qualified suppliers into one product so agents can propose substitutions that ops and quality trust on day one.

4) Target non-obvious use cases that move industrial P&L

Skip the “AI writes my RFP” demos. Prioritize:

• BOM should-cost + design-to-value with engineering. McKinsey cites 11% cost reduction when sourcing partners directly with engineering. Make it a joint ritual.
• Policy-driven dynamic buying channels (catalogs with rule-based rails). This is how you harvest the 25–40% efficiency potential from agentic AI without losing control.
• Supplier-performance “closed loop.” Convert PO exceptions, late deliveries, NCRs, and expedite fees into training data. Then let agents pre-empt risk by recommending alternates with proven OTIF. (McKinsey shows leaders emphasizing partnership and flexibility—not just price.)
• Contract variance heat-maps. Use genAI to surface clauses driving leakage (payment terms, warranty carve-outs, surcharges tied to commodity indices) and push standardization. (See McKinsey’s genAI-in-procurement guidance.)

5) Prepare for the hype cycle without becoming a statistic

Gartner says GenAI for procurement has already hit hype peaks and later the “trough,” with fragmented data, complex integration, and unclear value stalling many programs. They also warn of “agent-washing” and project scrap rates for immature agentic initiatives. Translation: scrutinize claims, bound autonomy, and demand outcome contracts.

Guardrails to adopt now

• Bounded autonomy: start with constrained agents (catalog intake, 3-bid-and-buy, variance checks) before negotiation or commitment authority.
• Red-team your agents: test for supplier hallucinations, clause drift, and bias.
• Outcome-based vendor SLAs: pay for realized savings/cycle-time reductions, not pilots.
• Human-in-loop by design: Deloitte’s 2025 CPO survey ties performance to tech and talent, not tech alone.

6) Build the operating system, not a tool zoo

From McKinsey’s cross-sector survey of 300+ procurement leaders: the organizations making procurement a strategic driver are the ones that reorganize around it (reporting lines to CEO/CFO/COO, COEs with accountability at CPO level, and center-led category strategy). Then technology amplifies the new design.

A practical 180-day roadmap (built for distributors & manufacturers)

1. 30 days – Baseline & backlog: P2P adoption audit; tail-spend map; clause library seed; Supplier Master gaps. (Name 3 categories for impact: MRO, packaging, indirect logistics.)
2. 60–90 days – Industrialize the basics: Fix P2P UX; switch 3 categories to e-sourcing; deploy invoice-to-contract reconcilers; publish category taxonomies; establish bounded agents for intake and 3-bid-and-buy.
3. 90–180 days – Scale value work: Launch should-cost playbooks with engineering on 2 BOM families; codify alternates and cross-refs; turn exception codes into supplier-risk signals; tie weekly value dashboards to CFO-visible KPIs (savings, cycle time, OTIF, leakage recovered).

For distribution and manufacturing, architecture beats algorithms. The durable winners don’t chase every new model; they design procurement as a governed system where humans, rules, and AI cooperate inside the flow of work. That’s how you capture the 25–40% efficiency step-change and convert it into EBITDA.

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Sources

• McKinsey, Transforming procurement functions for an AI-driven world (Oct 27, 2025). Key facts on agentic AI efficiency (25–40%), P2P/e-sourcing underuse, EBITDA impact, COEs, and operating-model maturity. (Transforming procurement for an AI-driven world | McKinsey)
• McKinsey, Making the leap with generative AI in procurement (Mar 20, 2024). Practical genAI use cases and scaling lessons. (Gen AI value in procurement | McKinsey & Company)
• Gartner releases on GenAI for procurement—hype cycle, adoption obstacles, and caution on agentic claims. (Gartner Says Generative AI for Procurement Has Hit Peak of Inflated Expectations)
• Reuters on agentic AI project attrition (context for bounded autonomy). (Over 40% of agentic AI projects will be scrapped by 2027, Gartner says | Reuters)
• Deloitte, 2025 Global CPO Survey (human-centered digital + performance), and 2024 blog on GenAI in procurement. (2025 Global Chief Procurement Officer Survey | Deloitte US)

In July 2025, MIT’s NANDA initiative released The GenAI Divide: State of AI in Business 2025 and its findings should stop every business leader in their tracks.
Despite $30–40 billion invested globally in GenAI, the study found that ≈ 95% of enterprise pilots deliver no measurable ROI, and only about 5% reach scalable, integrated success.

Enterprises are experimenting faster than they’re operationalizing.

What the Data Reveal

1. High adoption, low transformation
Over 80% of companies have piloted AI tools, but only a fraction moved beyond proof-of-concept. Success comes not from “trying AI,” but from embedding it into core business systems—ERP, CRM, MES, or compliance workflows.

2. The real barrier is integration, not technology
MIT’s research calls this the “learning gap”: most GenAI systems don’t adapt, retain feedback, or plug into decision loops.
Without domain-specific learning, AI remains surface-level, producing flashy outputs, not measurable gains.

3. External partnerships double the odds of success
One of the study’s most practical findings: organizations that partner with specialized vendors see 2× higher success rates than those building internally.
Why? Vendors bring cross-industry experience, tested frameworks, and governance infrastructure that’s hard to replicate in-house.

For industry leaders, the MIT study reinforces a truth we’ve long understood in engineering and manufacturing: architecture determines performance. The organizations seeing real ROI are building the systems that allow intelligence to flow safely, consistently, and transparently.

Ask not “What model should we use?” but “What structure makes the model trustworthy?”
The winners will be the ones who design AI like infrastructure that is reliable, auditable, and aligned with the business it serves.

Here’s what you can do now:

1.  Start small, but start with purpose

Define 2–3 workflows where AI can remove friction or cost—pricing variance, audit trails, policy modeling, or data reconciliation. Measure before and after.

• Embed, don’t bolt on

AI must live inside your workflow. If it can’t interact with your ERP, approval chains, or data lake, it’s a demo, not a solution.

•  Design for governance and auditability

The MIT study shows that explainability and traceability predict ROI.
In regulated industries, trust is not a feature—it’s a requirement.

•  Choose partners, not providers

External partnerships outperform internal builds when vendors:

• Understand your industry and compliance needs
• Integrate deeply into your operational stack
• Commit to measurable business outcomes
• Provide auditable, policy-aware AI guardrails

The GenAI Divide provides a roadmap for enterprises.  MIT’s research proves that AI success isn’t about model size or spend; it’s about architecture, governance, and human alignment.  The future belongs to organizations that can integrate AI into their everyday decisions with transparency, discipline, and trust.

Every AI pilot teaches something but not every experiment should become a product.
The lesson from MIT’s 2025 report is clear:
Build systems that learn responsibly, operate transparently, and deliver real business value.

Because the 95% isn’t your destiny, it’s the beginning of a larger story of AI success.

Jeana Bolanos is the Founder & CEO of SalesE, a Virginia-based SaaS company combining deterministic decision architectures with AI to automate and govern complex sales and operational workflows for enterprise distributors and manufacturers.

Our founder, Jeana Feely, was recently featured on the Enterprise Podcast Network, where she shared her insights on how AI is transforming the manufacturing marketplace.

In the interview, Jeana discussed the growing impact of AI across industries, future trends shaping manufacturing, and how SalesE is helping companies streamline customer service and sales through automation. Her perspective highlights both the challenges and opportunities that come with rapid technological change.

Listen to the full conversation here.

We’re proud to have a founder who is leading the conversation on innovation and the future of business!