While Neural Networks and Machine Learning have been in commercial use for a couple of decades, AI overall has become dramatically more accessible today. Firms today don’t need armies of AI scientists to realize the benefits of AI in their business operations. Reasoning AI has reduced hallucination, making LLMs more ready for commercial use. Additionally, the popularity of Agentic AI has now made the monetization of AI much easier.

While the opportunity is immense and well understood, there are a few obstacles that need to be overcome:

• Data – A lot of data, while immensely valuable, may not be captured today, or be captured in a fragmented, unreliable way. e.g., you may have several salesforce instances, the sales processes may not be ensuring diligent data entry, data taxonomies may be inconsistent, other relevant data (e.g. product, financials, service) may not be readily accessible.
• Models – Any AI models used need to be vetted for regulatory compliance, cyber security, biases, data privacy, hallucinations, etc.
• Operating model – Should you own and develop? Which models have the potential to be your secret sauce (think TikTok). These should be built, tuned, enhanced inhouse. What are the external data sources that can provide you an edge especially in serving your customers and prospects?
• Costs – While general purpose LLMs have gotten cheaper the overall investments in cloud infrastructure, data management and model management can escalate quickly. Several firms have unintentionally ended up in a log jam, while their AI centers-of-excellence build aspirational enterprise-wide data and AI infrastructure.
• Monetization – Most firms have already benefited from small, tactical deployments of AI. Now is the time to monetize AI in a way that helps you leapfrog the market. What are the most impactful use cases? How do you ensure that the results are revolutionary, rather than evolutionary?

• Inject AI intelligence into traditional business decisions (e.g. prospecting, dispatching, matching leads to sales personnel)
• Gather, enrich, cleanse, integrate underlying data to extract intelligence for day-to-day operations
• Adopt, adapt and develop fit-for-purpose models for higher efficacy
• Realize benefits by transforming business processes with access to AI intelligence
• Develop & deliver transformational AI monetization strategies

Client Successes:

• Gen-AI based prospecting tool that built a targeted 16,000+ asset manager lead database
• AI-enabled workforce uberification tool for 35,000+ staff across 60+ locations processing 500+ skill tags and levels
• 2.8x increase in client conversion through feature engineering
• Inhouse analytics platform to democratize data & models for 4,000+ users