HP will cut 4,000–6,000 jobs by 2028 as it leans into AI; targets $1B savings and flags memory-cost headwinds

## HP's AI-driven restructuring HP announced a multiyear plan to reduce its global workforce by 4,000–6,000 jobs by fiscal 2028. The company is redesigning processes around artificial intelligence to speed development and improve customer satisfaction. Management also targets productivity gains through platform consolidation and program simplification. Shares fell about 5.5 percent in after-hours trading after the news. ## Where cuts will land and why Teams in product development, internal operations, and customer support will be affected. HP aims to streamline overlapping tools and simplify customer-facing programs. The company expects roughly 1 billion dollars in gross run-rate savings within three years. HP also disclosed additional layoffs of 1,000–2,000 people in February under an earlier restructuring plan. ## Costs and savings profile HP outlined about 650 million dollars in total restructuring charges tied to the new plan. Roughly 250 million dollars will be recognized in fiscal 2026. Management reiterated the 1 billion dollar savings target by 2028. The financial roadmap pairs near-term charges with mid-term operating leverage from AI-enabled process redesign. ## AI PCs and memory headwinds AI-enabled PCs made up more than 30 percent of HP shipments in the quarter ended October 31. That shows early traction for on-device AI features in mainstream hardware. However, HP warned of rising DRAM and NAND prices driven by data-center buildouts for AI. The margin impact will be bigger in the second half of fiscal 2026. HP plans several mitigations. It will qualify lower-cost suppliers and reduce memory configurations in select systems. The company also flagged selective price actions. The goal is to protect profitability while sustaining AI PC adoption. ## Guidance and recent results HP guided to adjusted EPS of 2.90–3.20 dollars for fiscal 2026. That sits below the 3.33 dollar analyst consensus cited by LSEG data. Q1 adjusted EPS is projected at 0.73–0.81 dollars. The latest quarterly revenue came in at 14.64 billion dollars, modestly above expectations. ## The bigger picture HP's plan places it among large incumbents using AI to drive process redesign and cost takeout. The approach combines workforce reductions with heavier AI integration across development and support. The company faces near-term profitability headwinds from memory inflation. It also guides cautiously for 2026 as it navigates upgrade cycles and component volatility. ## Why this matters for Morocco Moroccan buyers and channel partners are sensitive to component price swings. DRAM and NAND inflation can raise PC costs, especially in higher-memory configurations. AI PCs are gaining momentum globally, and Moroccan demand will follow as use cases mature. Businesses will weigh features against budgets while targeting clear productivity wins. Morocco's economy blends industrial exports, services, and technology-enabled operations. AI promises measurable gains in call centers, financial services, logistics, and manufacturing. The HP story underscores a broader shift toward AI-first processes. Moroccan firms should prepare for workflow redesign and component cost volatility in 2026. ## Policy and governance context Morocco's Digital Development Agency, known as ADD, leads national digital transformation programs. Agencies use ADD frameworks to modernize services and technology operations. The CNDP oversees data protection under Law 09-08. That framework shapes responsible AI deployment, including consent, transparency, and data minimization. Public bodies and private firms need strong data governance to deploy AI responsibly. Policies should cover retention, access controls, model risk, and audit trails. Clear governance reduces compliance risk and builds user trust. It also enables faster approvals for pilots and production systems. ## Talent and universities Universities play a central role in Morocco's AI capacity. UM6P in Benguerir and ENSIAS in Rabat run programs in data science, AI, and software engineering. Research centers and incubators expand applied work and industry partnerships. These institutions help build bilingual AI skills in Arabic and French, plus English for global toolchains. Local bootcamps and meetups strengthen the talent pipeline. Companies can co-design capstone projects on real datasets. That accelerates hiring and improves model relevance. It also creates a feedback loop between academia and production teams. ## Startups and applied AI Morocco's startup scene is building useful AI products for local needs. Atlan Space develops autonomous drone systems that use AI for environmental monitoring and maritime missions. It illustrates how AI can deliver high-impact services with limited ground infrastructure. The model suits large coastal and rural areas. Precision agriculture is another active area. Ventures such as AgriEdge apply satellite imagery and machine learning to farm analytics. They support irrigation planning, yield optimization, and input decisions. These products respond to climate and resource constraints that farmers face. ## Practical uses gaining ground Several AI use cases are mature enough for Moroccan firms to adopt now. Common examples include: - Contact center automation for intent detection and call routing. - Fraud detection and risk scoring in banking and fintech. - Demand forecasting and route optimization in retail and logistics. - Predictive maintenance and quality inspection in manufacturing. - Document classification and summarization for legal and administrative workflows. Healthcare shows growing interest in triage, appointment optimization, and imaging support. Private clinics and health networks can pilot AI with clear guardrails. Bilingual support matters in patient communications. Models should handle Arabic, French, and English terms reliably. ## Procurement and cost strategies Memory inflation affects total device cost and margin. Buyers can manage exposure through careful configuration planning. Practical steps include: - Choose right-sized memory and storage for target workloads. - Stage upgrades over time as component prices normalize. - Prefer devices that support efficient local inference. - Use model quantization to reduce memory footprint. - Negotiate supplier diversification and delivery windows. Consider AI workload placement as well. On-device inference lowers latency and data movement. Cloud inference reduces device memory needs but adds network costs. Hybrid designs can balance privacy, cost, and performance. ## Process redesign playbook for Moroccan firms AI value emerges from workflow changes, not just new devices. Start with high-friction tasks and clear KPIs. Map inputs, outputs, decision rules, and escalation paths. Replace repetitive steps with automation and smart assistants. Build modular services around search, classification, generation, and prediction. Use retrieval augmented generation for internal knowledge bases. Layer policy controls for sensitive actions. Align system behavior with compliance and risk appetite. Track business outcomes in small increments. Measure cycle times, accuracy, cost per task, and customer satisfaction. Iterate through monthly sprints. Expand to adjacent processes once the first wins are stable. ## Managing risk, privacy, and compliance Data protection under Law 09-08 requires disciplined governance. Firms should implement role-based access controls, encryption, and audit logging. Separate personal data from training corpora when possible. Review cross-border processing and third-party contracts. Model risk management matters as AI scales. Validate fairness, accuracy, and stability. Run adversarial tests and red teaming for high-impact systems. Document model lineage and decision rationale for audits. ## Building skills for durable AI AI programs benefit from multidisciplinary teams. Combine product managers, data scientists, MLOps engineers, and domain experts. Equip teams with testing, monitoring, and performance tooling. Build bilingual evaluation sets for Moroccan contexts. Invest in micro-credentials and hands-on labs. Partner with UM6P, ENSIAS, and incubators for live datasets and applied projects. Train staff on privacy and responsible AI. Establish internal guidelines for safe and effective model use. ## Outlook for 2026 in Morocco Expect AI PC adoption to grow as use cases mature. Memory prices may remain volatile through the second half of 2026. Moroccan SMEs and enterprises should plan budgets accordingly. Balance device upgrades with targeted workflow automation. The HP announcement signals a broader market shift. AI-first operations will shape product roadmaps and support models. Firms that act now can capture early gains. Those gains compound through better data, faster decisions, and higher customer trust. ## Key takeaways - HP will cut 4,000–6,000 jobs by 2028 while redesigning processes around AI. - Management targets about 1 billion dollars in savings and faces memory cost headwinds. - AI PCs show traction, but DRAM and NAND inflation will pressure margins in 2026. - Moroccan firms should prioritize workflow redesign, governance, and disciplined procurement. - Talent, universities, and startups provide a strong base for practical AI adoption.