How to Build Trust in AI Implementation

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Lately, I’ve been asked an interesting question about AI adoption. Specifically, how should an organization implement AI tools without instilling fear, but instead align with the organization’s mission and vision? Whenever I discuss AI adoption, two thoughts always come to my mind immediately: 1️⃣ Transparency in AI Usage: As executives, we must be transparent about our future use of AI within our business. Transparency aligns with our commitment to ethical practices and strengthens employees’ trust. It’s important to demystify the use of AI, ensuring everyone understands its applications and benefits. Together, we can build a foundation of trust that forms the bedrock of our AI journey. 2️⃣ Employee Inclusion in Decision-Making: Our greatest asset is our people. In the AI adoption era, including employees in decision-making is critical to building trust. They bring invaluable insights, diverse perspectives, and a deep understanding of our operations. By involving our teams, we tap into a wellspring of collective intelligence that propels us ahead. Let’s foster a culture where everyone feels empowered to contribute to the decisions that shape our AI strategy. By combining transparency in AI usage with inclusive decision-making, we are not just adopting technology; we are building a culture of innovation, trust, and shared success. The convergence of transparency and employee inclusion isn’t just a strategy; it’s an organizational mindset.

AI Trust & Safety reminds me a lot of cybersecurity a decade ago. Previously, when I was a cybersecurity engineer, we could see the major players adopting CS best practices early. As the awareness of bad actors became more prevalent, the adoption of a CS posture snowballed and almost every industry connected to the web now has some cybersecurity measures in place (fingers crossed). As AI applications proliferate, the demand for Trust & Safety roles increases. Just how significant is this growth? We see the rise in global conferences like the Credo AI's Summit, led by CEO & founder Navrina Singh, ACM, Association for Computing Machinery's FAccT, and TrustCon, put on by the Trust & Safety Professional Association. Trust & Safety teams ensure tech platforms are ethical and fair, crucial for standards and user protection. Embedded in Legal, Product, or Operations, they address harmful risks and align with responsible AI practices. As AI's influence grows, these teams are key. Companies like Facebook, Google, and Amazon have boosted their Trust & Safety efforts and with AI integration deepening, prioritizing Trust & Safety is vital for ethical tech navigation. We also see a rising trend in Trust & Safety teams at well-funded AI startups, mainly because their end customers want to know that the potential harms are being addressed in a meaningful way. Here are some ways to collaborate with Trust & Safety:  — Engage Early: Include Trust & Safety from the start in product development for built-in ethics. — Regular Training: Educate all teams on Trust & Safety's role and how to contribute.  — Open Communication: Promote a culture where Trust & Safety feedback is encouraged and acted upon. #TrustAndSafety #AI #TechEthics #AIStartups