Introduction
Healthcare and mobility are changing fast because products are getting smarter. By “intelligent products” we mean devices and software that sense the world, learn from data, and act to help people. In hospitals, that includes wearables that track heart rhythm, smart pumps that adjust drug dosing, and imaging systems that spot early signs of disease. In mobility, it includes advanced driver‑assistance systems, connected vehicles, and city platforms that manage traffic in real time. These changes do not happen in isolation: cloud platforms, 5G, edge computing, and new rules on safety and privacy shape how products are made and used (Topol, 2019; Litman, 2022).
Why healthcare needs intelligent products
Most healthcare today is reactive. A patient gets unwell, goes to the clinic, and the clinician acts based on limited data. Intelligent products help move care to a proactive model. A smartwatch can flag irregular heartbeats before symptoms are obvious. A remote monitoring kit can spot a fall risk or a slow change in oxygen levels. Hospital devices can share data with electronic records so a full picture is visible during ward rounds. This can reduce emergency admissions and improve patient experience when it is done with strong governance and consent (Topol, 2019; NHS England, 2019).
Design principles that matter
Simple design rules make these products effective:

• Human‑in‑the‑loop: clinicians stay in charge; devices support their judgement, not replace it.
• Explainability: alerts should show why the system raised a risk, in plain language and with simple visuals.
• Safety by design: fail‑safe defaults, audit trails, and clear clinical ownership are essential when actions can change care.
• Interoperability: data must flow through standards like HL7 FHIR so devices talk to each other across vendors and settings (HL7, 2019).
• Privacy by design: encryption, access control, and purpose limitation are built in from day one (WHO, 2021).
  Examples in healthcare
  Wearables now capture heart rate, rhythm, sleep, and activity. When combined with simple risk models, these streams can help detect atrial fibrillation or early deterioration after hospital discharge. Imaging products use AI to triage scans, helping radiologists focus on urgent cases. Smart infusion pumps cross‑check orders against patient weight and kidney function to cut dosing errors. Hospital asset tags track equipment location, reducing lost time. In all cases, the product works best when it fits into daily workflow and shows value without extra clicks (Topol, 2019).
  Mobility: safer, cleaner, and more accessible
  The mobility sector is seeing a similar shift. Driver‑assistance features such as lane keeping and automatic emergency braking reduce collisions. Fleet telematics help companies plan routes, cut fuel use, and schedule maintenance before breakdowns occur. City platforms combine camera feeds, sensors, and public transport data to manage traffic lights dynamically. This improves travel time and air quality when deployed with fairness and transparency. Full self‑driving is still a research challenge, but step‑by‑step automation supported by clear rules and driver monitoring is adding value today (Litman, 2022).
  Business value and operating models
  Intelligent products change how companies make money. Instead of one‑off sales, firms can offer “product‑as‑a‑service” with subscriptions that include analytics and updates. Hospitals may pay for “uptime” of a device plus quality outcomes, not just the hardware. Car makers sell connected services that update features after purchase. These models require strong service design, cyber security, and clear contracts on data ownership. They also need robust monitoring (MLOps) to track model drift and performance over time (Davenport and Ronanki, 2018).
  Risks and how to manage them
  Risks include biased models, over‑reliance on automation, poor fit with workflows, and privacy breaches. Mitigation starts with representative data, documented model limits, and regular bias audits. User testing with clinicians, drivers, and citizens reduces the risk of adoption failure. Privacy risks are managed with data minimisation, pseudonymisation, and strong consent flows. Safety cases and post‑market surveillance are needed for regulated products such as medical devices and advanced vehicle functions (WHO, 2021; MHRA, 2021).
  What success looks like
  A successful intelligent product is trustworthy, useful, and maintainable. Trust comes from safety evidence, clear explanations, and strong governance. Usefulness comes from solving a real problem in the daily routine with fewer clicks and faster feedback. Maintainability comes from modular design, standard interfaces, and a lifecycle plan for updates and monitoring. Organisations that follow these simple ideas see better outcomes for patients and citizens, lower costs, and new revenue (Westerman, Bonnet and McAfee, 2014).
  Conclusion
  Intelligent products in healthcare and mobility are not just new gadgets. They are a practical way to improve safety, experience, and efficiency when built with the right standards and safeguards. The path forward is stepwise: start with the most valuable workflows, integrate with existing systems, gather evidence, and scale carefully. With human oversight and clear rules, these products can deliver real impact today while building towards more advanced automation in the future (Topol, 2019; Litman, 2022).

References (Harvard style)
Topol, E. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books.
Litman, T. (2022) Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute.
NHS England (2019) The NHS Long Term Plan. London: NHS England.
HL7 (2019) Fast Healthcare Interoperability Resources (FHIR) — Release 4. Ann Arbor: HL7 International.
World Health Organization (2021) Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO.
Davenport, T. and Ronanki, R. (2018) ‘Artificial Intelligence for the Real World’, Harvard Business Review, 96(1), pp. 108–116.
MHRA (2021) Software and AI as a Medical Device: Change Programme. London: Medicines and Healthcare products Regulatory Agency.
Westerman, G., Bonnet, D. and McAfee, A. (2014) Leading Digital: Turning Technology into Business Transformation. Boston: Harvard Business Review Press.