Lessons from Executive Concerns
AI technology is advancing rapidly, but most organizations aren’t fully prepared for the risks associated with it. MIT Sloan recently surveyed executives across industries and found a common theme: data readiness is the foundation for safe and successful AI adoption.
Why Data Readiness Matters
Executives view data as both the greatest opportunity and the most significant risk in AI projects. Without clean, well-organized, and secure data, AI can:
- Leak sensitive information (like personal data or intellectual property)
- Make bad decisions due to poor-quality inputs (“garbage in, garbage out”)
- Create compliance and regulatory headaches
- Damage trust with customers and employees
In short: AI is only as good as the data behind it.
The Top Risks Leaders Worry About
Surveyed executives highlighted these as the biggest risks to AI adoption:
- Data Security & Privacy – preventing leaks, breaches, and unauthorized use.
- Intellectual Property (IP) Protection – keeping “secret sauce” data out of third-party tools.
- Model Reliability – avoiding hallucinations, misinformation, or unsafe outputs.
- Governance & Oversight – making sure accountability and monitoring are in place.
- Adoption & Scaling – balancing speed with responsible risk management.
- Malicious Attacks – defending against adversarial threats unique to AI systems.
One common thread: all of these risks connect back to data readiness.
What Data-Ready Organizations Do Differently
Executives in the study pointed to three big steps every organization can take:
- Strengthening Data Governance
- Audit where sensitive data enters AI systems.
- Apply strict access controls and validation checks.
- Govern external/vendor AI usage with clear policies.
- Testing for Adversarial Risks
- Run “red team” exercises to simulate data leaks and attacks.
- Monitor for prompt injection, data poisoning, or misuse.
- Regularly audit outputs for errors and unsafe recommendations.
- Balancing Adoption with Capability Building
- Start with low-risk AI use cases (automation, reporting, copilots).
- Build internal skills and governance frameworks before scaling.
- Treat AI as both a business enabler and a risk to be managed.
Key Takeaway
AI adoption isn’t just about buying tools—it’s about preparing your data. Without strong data availability, quality, governance, and security practices, AI projects expose organizations to real business and security risks.
Executives agree: the winners in the AI era will be those who get their data in order first.
Why New Era for AI Data Readiness?
AI success starts with data readiness—and that’s where New Era stands apart. We combine deep expertise in data governance, security, and compliance with a practical, business-first approach to AI adoption. Our assessment helps you:
- Identify Risks Before They Become Problems
We uncover gaps in data quality, privacy, and governance that could derail AI initiatives.
- Build a Secure, Compliant Foundation
Our frameworks align with industry regulations and best practices to protect sensitive data and intellectual property.
- Accelerate AI Adoption Responsibly
We guide you from low-risk use cases to scalable AI solutions, ensuring governance and trust at every step.
With New Era Technology as your partner, you won’t just assess readiness — you’ll future-proof your AI strategy.
Survey Source:
Executive Concerns with AI Adoption: Identifying Business and Security Risks by Keri Pearlson, Rajiv Dattani :: SSRN