To AI · Not to AI?
Why? · If yes, how? · How much?
Claude, OpenAI, Gemini — each is strong at something, weak at others. Sometimes the right answer is not AI at all — AI is costly, in both dollars and the change it brings. We discern whether AI is right for you and run a cost-benefit analysis — before you spend or scale.
This is not a philosophical question — it is one we test in the water, head to head:
AI SETA vs. Non-AI SETA
Our research compared mining employees’ phishing behavior under non-AI versus AI-enabled SETA — AI lifted detection to 58% vs. 47%. Our IEEE study (132 users) showed why: explainable AI is the mediator that drives the gain.
Robotics Surgery vs. Non-robotics Surgery
With Symani robotic assistance, surgical outcomes improved while surgeon performance stayed comparable — our paper at Plastic Surgery The Meeting, co-authored with surgeons at the Department of Plastic Surgery, Cedars-Sinai Medical Center.
To Quantum · Not to Quantum
Our systematic review of quantum AI found that translating classical thinking into quantum falls short — it is a different paradigm that reshapes both the problem and the data. Discernment before the next frontier’s hype.
Catch up with fast AI, safely
Caught between missing out and getting it wrong — like a deer in headlights, where the light is real, the danger is real, and standing still is not safe either. Move too fast and AI overruns judgment; freeze and the moment passes.
- — Competitors are already deploying AI.
- — The board is asking for an AI strategy.
- — Domain experts expect AI but with fear and caution.
- — The window to lead feels like it is closing.
- — Which AI tool should we use?
- — What do we feed it — and what do we protect?
- — How do we know the output is right?
- — How much does it cost — short-term and long-term?
- — What happens to our people?
This could happen to YOU — falling apart, two different ways
There is a sweet spot — we find it for you
Readiness, not resistance
Our longitudinal study across an enterprise-wide system rollout found that building readiness for change — not fighting resistance — drives adoption success.
Pacing disruptive AI
Our Routledge chapter, Mindful Change Management for Disruptive AI, maps how a slow-moving organization keeps pace with fast-moving AI — mindful at every level, from executive vision to employees adopting AI for higher-value work.
Adoption discipline, long before AI
Technology adoption is not new to AI. Our peer-reviewed research spans waves — including a cloud-success study that updated DeLone & McLean’s IS Success theory — mapping what drives adoption and what stalls it.
How to keep and scale your best people?
Often the real reason is people, not technology — we help you scale your best.
The tension is real — and it is dividing us. At a recent graduation, the speaker mentioned AI. Half the audience cheered. Half booed. Not a technical disagreement — a human one. Companies feel the same split: keep their people, but fear losing the scale and speed of the AI era.
Use AI for the coding, design, and documentation — but how much? What roles would your best people play? Where is the fulcrum point for harmonious human–AI integration? Let us help you locate it.
This is the human–AI interface: knowing where AI reaches its limits, and where a qualified human must step in — especially when the stakes are life and death. The roles keep trading — sometimes the human leads and AI supports, sometimes AI leads and the human supervises; the skill is the fluid exchange, like jazz.
Ethical decision-making in autonomous systems
Our research modeled what makes a driver intervene when an autonomous vehicle faces an ethical dilemma — and found personal morality, not the machine’s logic, is the dominant factor.
Human
AI Co-adapt, Human-in-the-loop
Our research on fully automated (Level 5) vehicles argued for shared control — a human in the loop — and found that as the AI takes on harder cases, human competencies must advance alongside it, not disappear.
Hold the balance — secure, private, accountable
We build governance in from the start — not bolted on after.
The sweet spot moves as you scale and the technology shifts; governance is how you hold it — and how you stay safe. We train your people on privacy and security, and bring depth in AI security, privacy, GenAI governance, and explainability — backed by published research.
AI-privacy literacy in Generation Z
Our IEEE study built a framework (DCPS) to measure Gen Z’s AI privacy literacy — and found a stark gap: real awareness of risk, but very low ability to act on it.Hua & Wang. IEEE TPS-ISA, 2025 (invited paper).
Clinical AI under data scarcity
Examines two industry answers to scarce clinical data — MONAI’s federated learning (training across hospitals without moving raw patient data) and MAISI’s synthetic medical imaging — and maps their risks (inference attacks, HIPAA, fairness, generalizability) with concrete guidance for safe deployment.Wang, Kalla & Shadowen. Issues in Information Systems, forthcoming.
Explainable AI reduces phishing risk
When AI flags a threat, does explaining why help people resist it? Our IEEE study tested explainable AI (XAI) as the mediator between AI security training and phishing susceptibility — empirically, with 132 email users. Both AI-SETA and XAI significantly cut susceptibility.Masialeti & Wang. IEEE TPS-ISA, 2025 (invited paper).
We “hold your hands” throughout — to ensure measurable success
For a consultancy rooted in rigorous research, measurement is a given — not a feature.
We tailor the right off-the-shelf AI to your needs. The results are measured as a matter of course — our applications are diverse, but the rigor underneath is the same.
Ready to find your sweet spot?
Adopt the right AI, at the right pace — wisely, and measured.
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