Top Companies' AI Moves

Today's Stories

1. 1

   NTT DATA, Palo Alto Networks form $1B AI security alliance

   NTT DATA and Palo Alto Networks announced a multi-year strategic alliance on August 20, 2026, targeting $1 billion in joint business by the end of 2029. The partnership combines Palo Alto Networks' AI-powered cybersecurity platforms and Unit 42 threat intelligence with NTT DATA's consulting, engineering, and managed services across six focus areas: Autonomous Security Operations Center, AI governance, identity security, Zero Trust & SASE, resilient cloud, and firewall modernization. Organizations face dual pressure—rapid AI adoption and accelerating cyber threats—yet lack integrated solutions. Palo Alto Networks CEO Nikesh Arora stated the partnership allows operationalization "at true global scale," while NTT DATA CEO Abhijit Dubey emphasized the need for "a new approach to cyber resilience that combines AI-driven security, deep industry expertise and global scale." The alliance targets highly regulated industries: financial services, healthcare, manufacturing, and the public sector.

   The alliance deploys more than 2,000 Palo Alto Networks-certified professionals and NTT DATA's 7,500+ cybersecurity experts across 70+ delivery centers and 20+ Autonomous Cyber Defense Centers. NTT DATA gains early access to new Palo Alto Networks platform features through direct engineering collaboration, accelerating AI security service delivery to clients.

2. 2

   Target Names Chandhu Nair First Chief AI Officer

   Target appointed Chandhu Nair as its first Chief AI Officer and Senior Vice President, effective August 21, 2026. Nair will coordinate AI initiatives across the company's merchandising, guest experience, technology, and team and community priorities. Nair brings a track record in retail AI transformation from his role at Lowe's, where he led the AI transformation office, data and analytics practice, and AI products and platforms team. He is also a co-founder of Cognitive Retail, a computer vision AI company for brick-and-mortar retailers, giving Target direct expertise in applying AI to in-store operations.

   Nair has stated that AI's impact in retail should be measured not by technical benchmarks but by real store outcomes—a signal that Target plans to embed AI into everyday customer interactions rather than pursue technology for its own sake.

3. 3

   NTT DATA and Palo Alto Networks partner on AI security risks

   NTT DATA and Palo Alto Networks have announced a partnership focused on addressing security risks related to artificial intelligence systems. As organizations increasingly deploy AI, the security vulnerabilities and risks that accompany these systems require specialized attention; a collaboration between a major IT services provider and a leading cybersecurity firm signals that tackling AI-specific threats is becoming a core business priority.

   The partnership's specific offerings, timeline for deployment, and which industries or customer segments will be targeted first have not yet been detailed in available information.

4. 4

   61% of car shoppers plan to use AI—but dealers lag behind

   Cox Automotive released findings from its AI in Auto Retail Tracker showing that 61% of vehicle shoppers say they plan to use AI during their next purchase journey. Many already trust AI-powered tools to guide their decisions, yet relatively few dealers are actively adjusting their business strategies for AI search and discovery. The gap between shopper expectations and dealer readiness mirrors the early days of digital retailing, when consumer behavior shifted faster than dealership operations. Dealers feel overwhelmed by the pace of change despite rising AI adoption across the industry, creating a potential competitive disadvantage for those without a clear AI strategy.

   Cox Automotive highlights that successful dealerships stand apart through a clear strategy, trusted partners, and practical ways to use AI across their operations—suggesting these are the tools dealers need now to align with consumers who are already relying on AI to discover and evaluate vehicles.

5. 5

   Google Cloud tool classifies child pneumonia from X-rays without coding

   Researchers used Google Cloud Vertex AI AutoML, a no-code machine learning platform, to build and validate a system that automatically classifies pneumonia in pediatric chest radiographs (X-ray images of children's chests). The study was a proof-of-concept internal validation, published in the journal Cureus. Pneumonia diagnosis typically requires radiologist expertise and time. An automated classification system could reduce diagnostic delays and potentially expand access to reliable pneumonia detection in settings with limited specialist availability, particularly for pediatric cases where misdiagnosis carries clinical risk.

   The study represents a proof-of-concept validation; the body does not specify availability, deployment timeline, accuracy metrics, or rollout plans. Further external validation and clinical integration steps would be needed before real-world hospital use.

6. 6

   AI, Materials Shortages Shift Supply Chain Risks Beyond Shipping

   S&P Global's third-quarter 2026 outlook says supply chain bottlenecks are moving from transportation to shortages of critical materials and components. AI-driven memory demand, petrochemical constraints, and tariff uncertainty are pressuring product availability and costs through 2027. Distributors face three concurrent pressures. Technology distributors compete for memory chips as AI infrastructure consumes more capacity; South Korea's semiconductor producer prices reached 715% of 2023 average in May. Chemical and plastics distributors deal with tighter petrochemical supplies—naphtha imports into mainland China, Japan, Singapore, and Taiwan fell to 73% of pre-conflict levels in April. Tariff uncertainty is pushing companies to order earlier and rebuild safety stocks, tying up cash.

   S&P Global forecasts computer producer prices will rise 16% in the U.S. and 10.9% in mainland China by Q2 2027 versus Q4 2025. Memory-chip producers will spend $181.1 billion on capital expansion in 2027, up 141% from 2024, but new capacity will not eliminate shortages immediately because components must be built and qualified before reaching the market.