Insights

AI Adoption: Solving What Matters

In a world where AI adoption is no longer optional but imperative, businesses face a complex landscape of opportunities and pitfalls. Candy Zhang, Client Solution Consultant at the AI Innovation Center, brings a unique perspective to this challenge, blending over a decade of financial industry expertise with cutting-edge AI implementation strategies.

Bridging Business Needs and Technical Solutions

Candy’s journey from chartered financial analyst to AI consultant wasn’t accidental. Her experience designing asset management systems provided the perfect foundation for her current role translating business challenges into AI-powered solutions.

“The most valuable skill in AI consulting isn’t technical knowledge, it’s the ability to translate complex concepts into tangible business value,” Candy explains. Her work involves connecting business leaders with development teams, ensuring AI implementations address real needs rather than chasing technological novelty.

The Hallucination Challenge: When AI Gets Creative with Facts

Perhaps the most significant barrier to enterprise AI adoption is the phenomenon known as “hallucination” when AI produces convincing but factually incorrect information. This presents challenges in industries where accuracy is non-negotiable.

Candy recommends a three-pronged approach to address this challenge:

External Knowledge Integration: Implementing retrieval-augmented generation (RAG) systems that verify AI-generated content against trusted sources in real-time.

Strategic Prompt Engineering: Designing inputs that guide AI models to express uncertainty rather than fabricating answers, particularly for domain-specific questions.

Domain-Specific Fine-Tuning: Training models on highly-focused datasets relevant to specific business contexts to reduce the likelihood of factual errors.

“Hallucination isn’t just a technical problem, it’s a trust problem,” Candy notes. “Every incorrect output erodes user confidence, which is why addressing it requires both technical solutions and expectation management.”

Beyond Chatbots: Three AI Implementation Pathways

While generative AI and chatbots dominate headlines, Candy identifies three strategic use cases delivering measurable business impact across industries:

1. Knowledge Acceleration

AI’s ability to synthesize and contextualize information is transforming knowledge management. Healthcare providers are using AI to compile clinical summaries from patient encounters, dramatically reducing documentation time. Similarly, customer service teams deploy AI to surface relevant knowledge base articles, improving both response times and accuracy.

“The most successful knowledge applications don’t just answer questions, they anticipate them,” Candy observes.

2. Operational Excellence Through Automation

In finance, Robotic Process Automation (RPA) enhanced with AI capabilities is eliminating manual workflows while maintaining regulatory compliance. These systems can now extract unstructured data from invoices, validate it against multiple systems, and initiate payment processes without human intervention.

“The ROI calculation is straightforward,” says Candy. “When you eliminate manual data entry and validation, you reduce both costs and errors while freeing employees for higher-value work.”

3. Predictive Analytics for Market Advantage

Retailers leveraging AI-driven analytics are gaining competitive advantages through more accurate demand forecasting and customer preference mapping. These systems identify patterns that would be impossible for human analysts to spot, enabling proactive rather than reactive business decisions.

“The differentiator isn’t having data, everyone has data,” Candy emphasizes. “The advantage comes from extracting actionable insights faster than your competition.”

Starting Your AI Journey: A Pragmatic Approach

For businesses just beginning their AI implementation, Candy recommends a methodical approach:

Secure Executive Buy-In Through Industry-Specific Use Cases

Rather than presenting abstract AI capabilities, research successful implementations within your industry. “Executives respond to competitive advantage, not technological novelty,” Zhang advises. “Show them how peers are using AI to solve similar problems.”

This approach requires developing a comprehensive strategy that addresses not just benefits but also risks, particularly around data privacy, ethics, and integration challenges.

Target Customer Pain Points with Pilot Projects

“The best AI implementations start with specific customer frustrations, not technology capabilities,” Candy notes. By identifying areas of customer dissatisfaction, businesses can design focused pilot projects that demonstrate value while revealing unexpected dependencies.

These pilots should be designed with scalability in mind, ensuring that successful experiments can expand into enterprise-wide solutions without requiring complete redesign.

Prioritize Data Quality

Even the most sophisticated AI models can’t compensate for poor data quality. Candy recommends establishing robust data governance standards before implementation begins.

“Quality data produces quality insights; compromised data produces compromised decisions,” she explains. This requires not just technical solutions but cultural shifts, training staff to understand data quality as a shared responsibility rather than an IT function.

The Future of AI Consulting

As AI becomes increasingly integrated into business operations, Candy sees the role of consultants evolving. “We’re moving from explaining what AI can do to helping businesses navigate what AI should do,” she reflects.

This evolution requires consultants who understand both technological capabilities and business contexts, professionals who can think critically about ethical implications, opportunity costs, and long-term strategic impact.

If you’re exploring using AI for your workflows, let’s connect.