Trigent recently hosted an exclusive premier leadership forum, Trigent Tech Forum, gathering a diverse group of stakeholders for a 360-degree perspective on “Bringing AI into the Modern Enterprise“. This included leading AI practitioners, market analysts, business, service providers, investors, and even welfare organizations.
Rohit Adlakha, the moderator, began with a fascinating overview of how Generative AI Services (Gen AI) is transforming industries. He traced its evolution, highlighting the shift from narrow AI to more general AI applications. He then focused on real-world applications currently being delivered by Trigent, including anomaly detection, Know Your Customer (KYC) verification using Large Multimodal Models (LMMs), and personalized property recommendations, all driven by extensive data analysis.
The Ensuing Discussion Centered Around Key Challenges and Opportunities
Challenges
- Inaccuracies and Hallucinations: Many technology and AI leaders cited hallucinations and inaccuracies in AI-generated output as significant challenges. This, coupled with the immense computational resources required and unclear return on investment (ROI), presents a significant hurdle. Todd Smith, co-founder and CEO of Opnbook—an emerging leader on the path to becoming the Google Analytics of AI—emphasized the importance of context and clarity in AI responses, drawing a parallel to the rapid evolution of AI technologies and the need for a scientific approach. Another leader stressed the crucial need to carefully evaluate the cost-benefit ratio of AI adoption. He aptly compared AI tools to employees, highlighting the importance of thorough testing in controlled environments to ensure security and performance before full-scale implementation.
- Enterprise-Wide Adoption: The practice leaders emphasized that AI adoption requires more than just technological implementation. It demands a fundamental shift in organizational mindset. This necessitates a re-evaluation of AI’s role across the entire ecosystem, including partners and customers. The forum stressed the importance of “What if Not” analysis alongside traditional “What if” scenario simulations. This approach acknowledges the competitive disadvantage that can arise from not adopting AI, regardless of the industry.
- Socio-Economic Impact: A unique perspective emerged from the non-profit sector, which highlighted the challenges of using AI to combat issues like slavery. Data quality emerged as a major concern, particularly with sensitive information involving survivors and government data. This organization currently prioritizes controlling AI usage over widespread implementation.
- Build vs Buy Decisions: The CTO of a leading innovator in North America’s REIT sector shared his team’s journey with the “build vs. buy” dilemma in AI adoption. While initial experiments focused on building internal AI solutions, they later shifted towards partnering with vendors for specific use cases, such as automating lease abstraction. The increasing accessibility of tools like Microsoft Copilot Studio is driving a growing trend towards AI in application development. The key takeaway: foster an “AI-first” mindset within teams to effectively leverage these capabilities.
Opportunities and Change Levers
- Domain-Specific Models: Ashok Jakati, a renowned insurance technology leader at Quility recognized as the InsurTech of the Year 2024, highlighted the critical role of domain specificity in AI and Gen AI. He emphasized that current pretrained models, Retrieval-Augmented Generation (RAG) models, and Sequence-to-Sequence Learning (S2S) models are built upon this foundation. He discussed how AI is being used to enhance Customer Relationship Management (CRM) systems and improve operational efficiency within the insurance industry. He also acknowledged the challenges of data accuracy and the ethical considerations associated with AI adoption, particularly in supporting sales agents.
- Manufacturing Success: On the other hand, Rajdeep Mahida, Vice President of IT, MKS Instruments, a Massachusetts-based leader in semiconductor manufacturing, shared how AI is driving strategic marketing and competitor analysis. He emphasized the importance of tailoring AI development to specific business needs and highlighted the positive impact on operations.
- Communicating the Value of AI: A practice expert suggested innovative and holistic AI storyboarding as a key strategy for driving organizational culture change. By humanizing AI, presenting it as a “smart and dedicated coworker” with shared goals, organizations can effectively drive individual and team productivity. This approach can shift the perception of AI within the organization, fostering a more positive and productive work environment.
- The “Bot-Man” Workforce: The future workplace will likely feature a hybrid model where humans and AI work together. This “bot-man” workforce will share common enterprise goals and Key Performance Indicators (KPIs), collaborating on Mixture-of-Experts (MoE) models and autonomous enterprises with intelligent workflows. While these concepts may seem like science fiction, they are already being implemented in practice. Successful adoption will depend on effective human execution and a cultural shift that embraces this new reality.
Trigent’s AI Journey: Finally, Chella Palaniappan, President – Client Services at Trigent, shared how the leading IT services provider is leveraging AI tools like ChatGPT, Copilot, and Amazon Q Developer to streamline software development processes. These tools are being used for code scaffolding, AI-assisted testing, and navigating complex codebases. Trigent has developed internal guides to support developers and aims to increase AI usage from 30% to 85% by next summer.
The Future of Enterprise AI Solutions
In 2025, enterprise AI solutions will expand its use cases across strategic value spectrums. Knowledge-automated smart machines will increasingly function as customers, coworkers, employees, and key stakeholders. Machine-first digital engineering will evolve from initial concept to final product, encompassing design, build, test, and operation. Machines will act as hybrid agents and digital avatars, assisting humans in decision-making and actions. AI-powered executive summaries are already becoming commonplace.
The emergence of “shadow AI” – the unregulated use of AI within organizations – presents a growing challenge. However, solutions are evolving rapidly, both technologically and organizationally, drawing upon established principles of disruptive technology adoption.
Conclusion
The first session of Trigent Tech Forum provided valuable insights into the challenges and opportunities presented by AI within the modern enterprise. As we look forward to the upcoming sessions, I am personally excited to delve deeper into these discussions and continue exploring the possibilities. By fostering a culture of innovation and embracing a human-centered approach to AI adoption, organizations can unlock its full potential and drive significant business value.
