Building Trust to Drive Revenue in the AI Ecosystem

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

Need to build trust as an AI-powered company? There is a lot of hype - and FUD. But just as managing your own supply chain to ensure it is secure and compliant is vital, companies using LLMs as a core part of their business proposition will need to reassure their own customers about their governance program. Taking a proactive approach is important not just from a security perspective, but projecting an image of confidence can help you to close deals more effectively. Some key steps you can take involve: 1/ Documenting an internal AI security policy. 2/ Launching a coordinated vulnerability disclosure or even bug bounty program to incentivize security researchers to inspect your LLMs for flaws. 3/ Building and populating a Trust Vault to allow for customer self-service of security-related inquiries. 4/ Proactively sharing methods through which you implement the best practices like NIST’s AI Risk Management Framework specifically for your company and its products. Customers are going to be asking a lot of hard questions about AI security considerations, so preparation is key. Having an effective trust and security program - tailored to incorporate AI considerations - can strengthen both these relationships and your underlying security posture.

Did you know that 80% of AI projects fail due to a lack of trust?    As organizations incorporate AI into their operations and offerings, establishing trust and effectively managing the associated risks needs to be a priority. My partner in leading Deloitte’s Enterprise Trust work, Clifford Goss, CPA, Ph.D., was recently featured in a great The Wall Street Journal article discussing how essential risk management is for successful AI adoption: https://deloi.tt/3TNckVQ. Cliff, along with our colleague Gina Primeaux, are focused on helping organizations manage the risk, regulatory, and compliance aspects of AI.    Cliff shares two ways organizations can strengthen AI trust: 1. Top-down risk management: Establishing strong governance policies and controls empowers organizations to leverage AI confidently while maintaining compliance. 2. Bottom-up risk management: Conducting thorough cyber assessments helps address concerns like unethical data use, data leakage, and misuse, reducing financial and reputational risks.    To keep pace with rapid AI advancements—from generative to agentic AI—risk management programs must remain flexible and responsive to new challenges and regulations. In doing so, organizations can build the trust necessary to fully realize AI’s benefits.