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AI Powered Work—Built on Trust

Research Article

AI-Trust

AI Powered Work—Built on Trust is the force multiplier that helps your people surpass goals through the synergy of human insight and AI. Artificial Intelligence has moved from the lab to the workplace. It writes reports, forecasts demand, screens resumes, detects fraud, and even drafts strategies. The potential is staggering, McKinsey (2023) estimates AI could add up to $4.4 trillion annually to the global economy. And yet, the paradox remains. Despite billions invested and adoption at record levels, 80% of companies using generative AI report no meaningful profit gains.

Introduction

Introduction: Why 80% of AI Efforts Fail to Deliver

The problem is not capability. AI often performs impressively in pilots. The problem is trust.

  • Managers hesitate to act on AI recommendations.
  • Employees resist tools they don’t understand.
  • Regulators question systems they cannot audit.

Without trust, AI becomes just another experiment. With trust, AI elevates how we work — unlocking efficiency, enhancing problem-solving, and driving strategic insight.

Unlocking Workflow Efficiency

Efficiency is the first promise of AI: reducing cycle times, automating repetitive work, and streamlining operations. But efficiency is only realized when people trust the system enough to stop second-guessing it.

Case 1: Insurance Claims

One insurer adopted AI to process claims and detect fraud. Initially, every AI flag was manually reviewed. The result: slower processing and frustrated customers.

Once the company established thresholds — with human review required only for high-risk cases — employees began to trust the system. The payoff:

  • 70% reduction in turnaround times
  • 30% cost savings
  • Higher fraud detection accuracy

Efficiency came not from the technology alone, but from the trust that allowed people to use it.

Case 2: Retail Forecasting Failure

A global retailer’s AI predicted demand spikes before holidays. Managers, worried about reputational risk if the system was wrong, ignored the recommendations. Shelves went empty, and the company lost millions. A post-mortem showed: the AI was right.

Here, efficiency wasn’t lost because the technology failed but because trust wasn’t there.

Key Insight: Workflow efficiency depends less on algorithmic sophistication and more on managerial confidence. Trusted AI doesn’t just speed up work, it elevates it.

Enhancing Problem-Solving with AI

AI isn’t only about automation. Its real power lies in helping humans solve complex problems: identifying hidden patterns, generating new options, and providing insights at scale. But people must view AI as a partner, not a rival.

Case 1: Amazon’s Hiring Algorithm

Amazon built an AI to screen résumés. Trained on historical data dominated by men, the model began penalizing women’s résumés. Employees quickly lost confidence. The project was abandoned.

The lesson: AI cannot enhance problem-solving without governance. Without trust in the fairness of its data, solutions create more problems than they solve.

Case 2: McKinsey’s “Lilli” Chatbot

By contrast, McKinsey introduced Lilli, an AI chatbot that synthesizes firm knowledge. Consultants feared it might replace junior analysts until leaders framed it as a co-pilot. After just one hour of training, consultants used it 17 times a week on average.

Instead of replacing problem-solvers, Lilli amplified their capabilities. Consultants trusted it because it was positioned as an enhancer, not a threat.

Case 3: Healthcare Diagnostics

In healthcare, AI systems that explain why they flagged anomalies, for example, using heatmaps on scans are much more trusted than black-box outputs. Doctors use them not because they’re perfect, but because they can justify their use to patients.

Key Insight: Problem-solving improves when trust is designed into data, process, and culture. When employees trust AI, they stop fighting it and start leveraging it.

Driving Strategy & Insight

The greatest value of AI lies not in automating the present but in shaping the future. Predictive analytics, scenario modeling, and generative tools give leaders foresight. But strategic adoption requires trust.

Case 1: Apple Card Credit Limits

When women reported receiving lower credit limits than men with equivalent credit profiles, regulators stepped in. Even if statistically defensible, the lack of transparency eroded trust. A potentially strategic AI tool for financial services turned into a reputational liability.

Case 2: Procter & Gamble Supply Chain

P&G integrates AI into supply chain forecasting to anticipate disruptions. Managers trust the system because governance protocols validate the data. The result: proactive strategic decisions, not reactive firefighting.

Case 3: Global Trust Gaps

A 2025 KPMG–University of Melbourne survey of 48,000 people across 47 countries found emerging economies trust AI more than advanced ones. In India and Brazil, consumers embraced AI banking because it delivered faster loans and better fraud protection. Strategic adoption followed.

Key Insight: Strategy requires foresight, and foresight requires trust. Trusted AI moves from tool to co-strategist.

Why Trust Is the Operating System

Across these dimensions, efficiency, problem-solving, and strategy, the same truth applies, trust is the operating system for AI adoption.

Without trust:

  • Efficiency is lost to second-guessing.
  • Problem-solving stalls in fear of bias or misuse.
  • Strategy falters when leaders won’t stake reputations on machine-driven insights.

With trust:

  • Processes accelerate.
  • Human expertise is enhanced.
  • Organizations gain foresight and competitive advantage.

How Organizations Can Build Trust

Trust doesn’t happen by accident. It must be deliberately engineered through five pillars:

  1. Transparency & Explainability — Managers need dashboards and reports that show why a system made its recommendation.
  2. Data Integrity & Governance — Rigorous standards for cleansing, auditing, and monitoring data build structural trust.
  3. Shared Accountability — AI recommends; humans decide. Responsibility stays clear.
  4. Human AI Collaboration — Position AI as a co-pilot, not a replacement.
  5. Ethics & Compliance Integration — Align adoption with values and regulations from the start.

These principles transform AI from an experiment into a trusted partner.

The Trust Dividend

When organizations get this right, benefits multiply:

  • Speed & Efficiency: Faster cycles, fewer bottlenecks.
  • Problem-Solving: Hidden patterns revealed, better decisions made.
  • Strategy: Leaders move from reacting to anticipating.
  • Culture: Employees feel empowered, not displaced.
  • Competitive Advantage: AI becomes a differentiator, not a stranded investment.

Accenture (2023) found that “AI-mature” firms, those with robust governance and high adoption, achieved 50% higher revenue growth than peers. That is the Trust Dividend in action.

Conclusion: Elevating Work Through Trust

The paradox of AI is clear: capability without trust delivers no value. For Blackstone and organizations worldwide, the challenge is not whether AI works; it does. The challenge is whether people trust it enough to take action.

Trust isn’t a soft issue. It’s a hard requirement for adoption. It determines whether AI remains in pilot purgatory or becomes a strategic co-pilot.

The opportunity is here: Unlock efficiency. Enhance problem-solving. Drive strategy and insight.

AI will elevate your work, but only if trust is built in from the start.

References (Printable URLs)

Written by Joseph Raynus

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