Artificial intelligence has quickly become the cornerstone of digital transformation. In industries like health care, the pressure to improve efficiency, cut costs, and maintain quality has never been greater. AI and Health Care, according to Muhammad Haroon Ashfaq, represent one of the strongest partnerships for achieving operational excellence (OpEx). But what do professionals working on the ground really think?
A recent study surveyed 355 professionals across industries to explore the AI-OpEx connection. The findings reveal both opportunities and challenges, giving us a clearer picture of how AI can deliver real value in daily operations.
Why the AI-OpEx Conversation Matters
Operational excellence is about building smarter, leaner systems that consistently deliver value. In health care, this translates to shorter wait times, better patient outcomes, fewer errors, and efficient resource use.
AI is positioned as a game-changer because it can analyze massive amounts of data, spot inefficiencies, and automate repetitive tasks. Yet, as Muhammad Haroon Ashfaq points out, success depends on how organizations implement AI—whether it’s aligned with OpEx principles or simply adopted as a trend.
The survey of 355 professionals helps us move beyond theory. It gives us practical evidence of how AI is impacting operations today.
What the Data Says: Key Insights from 355 Professionals
The survey reveals several important patterns about AI adoption and operational excellence.
1. Efficiency Gains Are Real, But Uneven
72% of respondents reported that AI tools improved efficiency in their workflows.
However, only 45% said those improvements were consistent across departments.
This highlights a common problem: AI works well in pockets but struggles to scale across entire organizations. In health care, for example, AI might speed up radiology scans but face integration issues with patient record systems.
2. Data Quality Remains the Biggest Barrier
68% of professionals agreed that poor data quality limits AI effectiveness.
Many noted that inconsistent data sources or missing records caused unreliable results.
This is particularly critical in health care, where inaccurate data could compromise patient safety. Muhammad Haroon Ashfaq stresses that clean, well-governed data is the foundation of both AI and OpEx success.
3. Training Drives Adoption
61% of respondents said their organizations lacked proper training to use AI tools effectively.
Those who received structured training were twice as likely to trust AI-driven recommendations.
This reinforces the point that people—not just technology—drive operational excellence. In health care, clinicians and administrators must understand and trust AI before it can deliver value.
4. Cost Savings Are Noticeable but Require Patience
54% of professionals reported cost reductions after implementing AI.
Yet, most said savings only appeared after at least 12 months of use.
Quick wins are possible, but long-term commitment is necessary for sustained impact.
5. Ethics and Trust Are Top Concerns
57% of respondents worried about bias in AI models.
49% were unsure if their AI systems complied with ethical standards.
In health care, trust is everything. Patients and professionals must feel confident that AI systems are transparent, fair, and safe.
Connecting the Dots: AI and Health Care
So, what does this data mean for the health care sector? The connection between AI and Health Care, as Muhammad Haroon Ashfaq explains, is strongest when organizations pair technology with operational discipline.
Efficiency gains in hospitals can reduce wait times for patients.
Data quality investments can ensure diagnostic accuracy.
Training programs can help doctors and nurses see AI as a partner, not a threat.
Cost savings can make health care more affordable for both providers and patients.
Ethical frameworks can maintain trust between patients and technology.
By aligning these elements, health care organizations can turn AI into a tool for true operational excellence.
Practical Strategies Inspired by the Survey
The responses from 355 professionals point to actionable strategies:
1. Start Small, Scale Gradually
Organizations should begin with pilot projects in areas where AI can deliver clear results. For example, automating billing or appointment scheduling provides early efficiency gains. Once successful, expand into clinical areas.
2. Invest in Data Infrastructure
Clean, reliable data is the foundation. Hospitals should strengthen their electronic health records (EHR) systems and ensure interoperability between platforms.
3. Prioritize Training and Upskilling
Make training a continuous process. Staff should learn not only how to use AI tools but also how to question and interpret results responsibly.
4. Build Ethical Safeguards
Implement transparent AI systems with clear explanations of how results are generated. Include audits to detect bias and ensure compliance with regulations.
5. Measure Results Against OpEx Goals
Success must be tied to clear KPIs like reduced wait times, lower costs, and improved patient satisfaction. AI projects that do not connect to operational excellence should be reevaluated.
Lessons from Muhammad Haroon Ashfaq
Drawing from both the survey data and his own research, Muhammad Haroon Ashfaq highlights three critical lessons for aligning AI and Health Care with operational excellence:
Technology alone is not enough. Infrastructure, governance, and culture must align with AI capabilities.
Trust drives adoption. When professionals trust AI, they use it consistently and responsibly.
Operational excellence is a journey. It requires continuous improvement, not one-time deployments.
These lessons mirror the findings from 355 professionals and reinforce the importance of a balanced, disciplined approach.
The Future Outlook: AI + OpEx in Health Care
As more organizations embrace AI, the gap between hype and practice will continue to narrow. The survey data suggests that the next stage will focus on:
Scalable AI adoption across entire health care networks.
Real-time analytics that give leaders instant insights into operations.
Patient-centered AI systems that personalize care while respecting privacy.
Collaborative ecosystems where hospitals, startups, and regulators work together to improve outcomes.
Muhammad Haroon Ashfaq predicts that organizations that master both AI and OpEx principles will set the standard for the future of health care.
Conclusion
The survey of 355 professionals offers a clear message: AI can drive operational excellence, but only when implemented with discipline and care. Efficiency gains, cost savings, and better outcomes are possible, but data quality, training, and trust remain critical.
AI and Health Care, as highlighted by Muhammad Haroon Ashfaq, form a powerful combination when paired with operational excellence principles. The connection is real, but it requires strategy, not hype.
Health care leaders should see this moment as an opportunity to bridge technology with practice. By learning from the data and applying practical strategies, they can build systems that are not just smarter—but also safer, leaner, and more human-centered.
