How Generative AI Transforms Enterprise Strategies to Further Fuel Innovation Across Organizations
By Lane F. Cooper, Editorial Director of BizTechReports and CIO.com Executive Roundtable Moderator
The convergence of Generative AI (Gen AI) with enterprise-wide strategies is reshaping how businesses modernize operations, innovate services, and stay competitive, according to a recent BizTechReports vidcast featuring Aparna Sharma, Managing Partner of IBM Consulting, and Sunil Joshi, Distinguished Engineer at IBM Consulting.
AI integration into enterprise systems has evolved rapidly since the debut of Gen AI two years ago. Sharma highlighted this transformation, emphasizing how Gen AI democratizes AI and enhances business strategies across industries.
"Technology is no longer an enabler but a central force in every business," she noted. "Gen AI has captured the world's imagination, bringing AI capabilities to supply chains, IT operations, customer service, and HR. It's a massive leap forward in how enterprises think about differentiation and customer experience."
To illustrate, she described how IBM worked with the United States Tennis Association (USTA) to bring innovation and accessibility together to transform experiences for fans. With the help of IBM's WatsonX AI models, the USTA integrated AI-generated match commentary, real-time win predictions, and match reports into the U.S. Open app,
"These tools didn't just enhance the tournament experience—they brought it to life for millions of fans around the world, whether they were courtside or watching from their living rooms," said Sharma. "The AI-generated match commentary gave fans real-time updates and detailed insights about the game as it unfolded, while win prediction models kept audiences on the edge of their seats by showing how each player's performance affected their chances of victory. For those who couldn't watch live, AI-crafted match reports provided a quick and comprehensive recap of key moments."
By integrating generative AI, the USTA was able to turn data into world-class digital experiences.
"This kind of innovation is about reducing the divide between the haves and have-nots," Sharma added. "We're at a point where technology like this is leveling the playing field, allowing more people to enjoy experiences they might not have had access to before."
Joshi expanded on the internal implications of Gen AI's democratization within enterprises.
By making complex technologies accessible to non-specialist users, Gen AI empowers diverse personas across organizations. For instance, product managers can use AI to analyze ideation sessions, automatically categorize insights, and translate them into actionable user stories—accelerating productivity and enabling better strategic planning.
"Generative AI allows us to automate complex workflows and augment decision-making, reducing the barriers of entry for those without deep technical expertise," Joshi said. He emphasized that targeted use cases, when implemented thoughtfully, yield tangible outcomes that enhance overall organizational efficiency.
Managing Risks
While the benefits of Gen AI are vast, both Sharma and Joshi acknowledged the need to address inherent risks. Sharma stressed the importance of selecting the proper use cases.
"Targeted problem-solving is key," she advised. She shared examples of legacy applications with millions of lines of code written in outdated languages like COBOL. Previously insurmountable challenges, such as rewriting these applications, are now manageable with Gen AI, which automates code generation and modernization while maintaining security within the enterprise's four walls.
Joshi echoed the sentiment, pointing out that risk management begins with careful use case selection. "We need to encourage experimentation while balancing risk," he said. "Start with low-hanging fruit—use cases that are non-controversial yet impactful, such as generating documentation for legacy codebases or improving IT operations ticket resolution. Success in these areas builds confidence and lays the groundwork for more ambitious implementations."
A successful Gen AI strategy requires more than technological investment—it demands a leadership commitment to people and culture.
"Generative AI is about people and how work gets done," Sharma stated. She urged leaders to prioritize employees in their Gen AI strategies by providing tools, training, and support that empower workers to embrace change.
Sharma also emphasized the importance of fostering continuous learning and adaptability. "Roles are evolving, and we have an opportunity to redefine them in a way that breaks stereotypes and focuses on high-value work," she said.
"Generative AI won't replace people, but those who use it will replace those who don't."
Joshi concluded the discussion with a call to action for organizations to embrace experimentation and adopt strategic frameworks for scaling Gen AI. "Be the disruptor or get disrupted," he advised. He outlined a three-fold approach for managing complexity while scaling:
Experimentation and Piloting: Start with use cases that demonstrate technical and economic viability. Maintain a human-in-the-loop approach to validate results and build trust in AI outputs.
Enterprise Integration: Leverage techniques like multi-shot prompting and enterprise-specific model fine-tuning to infuse AI with organizational compliance, design standards, and business context.
Value Measurement: Employ metrics such as A/B testing to quantify ROI and refine strategies based on real-world performance.
As organizations explore the transformative potential of Gen AI, the discussion underscored a critical truth: the future of work lies in the seamless integration of AI and human ingenuity.
"The current phase of Gen AI adoption represents both a learning curve and an opportunity for enterprises to rethink their strategies. With careful planning, experimentation, and a focus on measurable outcomes, organizations can position themselves to thrive in an AI-driven future," concluded Joshi.