In this episode, we explore how AI is reshaping retail security with real business impact. Using the blog “Best 10 AI-Powered Security Solutions Recognized for Boosting Retail ROI” (link: https://alphavision.ai/blogs/axis-integration/best-10-ai-powered-security-solutions-boosting-retail-roi) as our guide, we’ll walk through ten leading solutions, what makes them stand out, and how retailers can use them not just to reduce loss, but to optimize operations and drive value.

• The retail shrink challenge: in North America alone, retail shrink (losses from theft, error, fraud) topped $120 billion in 2024, squeezing margins.

• What defines a “good” AI security solution in retail: real‐time alerting, search, integrations with POS/operations, scalability, compliance

• Ten standout AI security systems (and what differentiates them):
    • Alpha Vision + Axis Integration: marries high-end Axis cameras with cloud AI Magic Search, intrusion alerts, live deterrence, license plate recognition.
    • Genetec Security Center: unified video + access + POS, open architecture for analytics and reuse of existing cameras.
    • Avigilon (Motorola Solutions): advanced imaging, “Appearance Search,” self-learning analytics, strong for investigations.
    • Avigilon Alta: cloud-native VMS, plug-and-play deployment, centralized control, operational insights.
    • Verkada: integrated hardware + cloud, smart search, two-way audio and deterrence, POS correlation.
    • Rhombus Systems: unified camera + sensor + control, decent for multi-site retail, AI search, operational analytics.
    • Eagle Eye Networks: open cloud VMS supporting many camera brands; natural language search, LPR, integrations.
    • Hanwha Vision (Wisenet): edge AI on the cameras (object detection, queue monitoring), built-in deterrents, hybrid storage.
    • Milestone Systems (XProtect with AI plugins): open VMS, wide AI marketplace, hybrid storage, rule engines.
    • Spot AI: retrofit AI to existing cameras via a plug-and-play appliance, hybrid storage, quick deployment.

• Strengths vs tradeoff considerations:
    • Cloud vs edge vs hybrid bandwidth, latency, resilience
    • Subscription & licensing cost scaling with store count
    • Vendor lock-in vs openness and integrability
    • False positives / tuning AI models must be calibrated per environment
    • Privacy & compliance (face recognition, LPR, personal data handling)

• Practical approaches for adoption:
    • Start with pilot locations (flagship stores, high-loss zones)
    • Monitor alerts and iterate AI settings (reduce noise)
    • Build fallback / offline modes where connectivity is weak
    • Track metrics (shrinkage, incident response time, labor saved) to build business case

• Retail security directors and loss prevention leaders

• CIOs / CTOs in retail chains or omnichannel operations

• Systems integrators focusing on retail technology

• Operations / store management leads aiming to optimize store performance

• Investors or tech scouts evaluating AI in physical retail