Rapid advancements in AI technology offer unprecedented opportunities to enhance business operations, customer and employee engagement, and decision-making. Executives are eager to see the potential of AI realized. Among 100 c-suite respondents polled in WNS Analytics’ “The Future of Enterprise Data & AI” report, 76% say they are already implementing or planning to implement generative AI solutions. However, 67% report struggling with data migration, while others cite challenges with data quality, talent shortages, and data democratization issues.
MIT Technology Review Insights recently spoke with industry leaders to discuss how enterprises can navigate the burgeoning era of AI.
AI across industries
There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings, but generative AI enhances these offerings by incorporating tailored communication that considers the customer’s persona, behavior, and past interactions. In insurance, generative AI helps identify subrogation recovery opportunities that manual handlers might overlook, enhancing efficiency and maximizing recovery potential. Banking institutions leverage AI for customer due diligence and anti-money laundering efforts, while AI technologies improve diagnostic accuracy in radiology through sophisticated image recognition.
The core of successful AI implementation lies in understanding its business value, building a robust data foundation, aligning with strategic goals, and infusing skilled expertise across every level of an enterprise.
- “If we do succeed, what are we going to stop doing? When we empower colleagues through AI, we give them new capabilities and faster ways of doing things.” — Shan Lodh, director of data platforms, Shawbrook Bank
Whether automating routine tasks or enhancing customer experiences, it’s essential to define what AI can do for an enterprise in specific terms. AI’s popularity is not a good enough reason to jump into enterprise-wide adoption.
“AI projects should come from a value-led position rather than being led by technology,” says Sidgreaves. “Always ensure you know what value you’re bringing to the business or customer with AI. Ask yourself, do we even need AI to solve that problem?”
Having a good technology partner is crucial for realizing value. Gautam Singh emphasizes, “At WNS Analytics, we keep clients’ organizational goals at the center and focus on generating value through our unique AI and human interaction approach.”
The foundation of any advanced technology adoption is data. “Advanced technologies like AI may not always be the right choice, so we work with clients to develop the right solution for each situation,” explains Singh.
Maximizing AI’s impact involves regular communication and collaboration across departments, ensuring infrastructure supports AI initiatives.
- “At Animal Friends, we see generative AI potential with chatbots and voice bots that can serve our customers 24/7.” — Bogdan Szostek, chief data officer, Animal Friends
Investing in domain experts is essential for deploying AI systems successfully. Continuous training and upskilling are necessary to keep pace with evolving AI technologies.
Ensuring AI trust and transparency
Creating trust in generative AI requires accountability, security, and ethical standards. Transparency about how AI systems are used can forge trust among stakeholders. The Future of Enterprise Data & AI report cites that 55% of organizations identify “building trust in AI systems” as the biggest challenge when scaling AI initiatives.
“We need talent, communication, and ethical frameworks,” says Lodh. “These elements become even more necessary for generative AI.”
AI should augment human decision-making. Guardrails with human oversight ensure enterprise teams control high-risk decisions.
“Bias in AI can creep in from anywhere unless you’re careful. Challenges include privacy, data quality, and training AI systems on biased data,” warns Sidgreaves. High-quality data enhances AI model reliability, and regular audits help maintain data integrity.
An agile approach to AI implementation
ROI is top of mind for leaders looking to capitalize on AI’s potential. Starting small, creating measurable benchmarks, and adopting an agile approach can ensure success. By beginning with pilot projects, companies can manage risks and optimize resources.
In insurance, AI significantly impacts risk and operational efficiency. Sidgreaves highlights that reducing manual processes is essential, with generative AI revolutionizing this aspect.
“Consider reviewing policy wording. Traditionally, this takes weeks, but with LLMs, it can be done in seconds,” she notes.
Lodh stresses the importance of establishing ROI at the project’s onset and implementing cross-functional metrics for comprehensive project impact assessment.
“It’s hard because technology changes quickly. We need to apply an agile approach, test, learn, iterate, and fail fast if needed,” says Szostek.
Navigating the future of the AI era
The rapid evolution of the digital age brings immense opportunities for enterprises globally. With numerous use cases promising efficiencies and innovation, few leaders dismiss AI as hype. However, responsible implementation requires a balance of strategy, transparency, and robust data privacy measures.
- “We must ensure we know what value we’re bringing to the business with AI. Do we even need AI to solve that problem?” — Alex Sidgreaves, chief data officer, Zurich Insurance
To harness AI’s power while maintaining trust, enterprises must define clear business values, ensure accountability, manage data privacy, and balance innovation with ethical use.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
