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Techday NZ
17-07-2025
- Business
- Techday NZ
AI shifts from adviser to architect in enterprise decision-making
A new study by TCS and MIT Sloan Management Review has identified a significant change in how enterprises are deploying artificial intelligence, marking a transition from AI functioning as an advisory tool to acting as a foundational architect for decision-making structures. The research, titled "Winning with Intelligent Choice Architectures," examines the role of intelligent choice architectures (ICAs) - dynamic AI systems that collaborate closely with humans - to shape, refine, and optimise decision-making environments across six sectors: retail, manufacturing, banking and finance, life sciences, energy, and communications. The report highlights the strategies of companies such as Walmart, Mastercard, Meta, and Pernod Ricard as they adopt these advanced AI capabilities. AI's evolving role The core finding of the study is that the value of AI within organisations is shifting away from simply improving existing business processes to elevating the quality of available options, thereby enabling better, faster, and more accountable decisions. This change is particularly evident in large enterprises looking to differentiate themselves in complex, highly competitive markets. ICAs flip the script. They do not just learn from decisions - they learn how to improve the environment in which decisions are made. That's not analytics, that's architecture. The research was conducted jointly over a year by academic and industry experts, drawing on experiences from a diverse range of global businesses. Michael Schrage, Research Fellow at MIT Sloan's Initiative on the Digital Economy and one of the coauthors of the report, emphasised the significance of this shift in approach. Sector insights In the retail sector, AI helps address challenges in staff turnover, customer personalisation, and supply chain logistics. Pernod Ricard, for instance, applies ICAs to test and personalise campaign content early in the development process, expediting refinement and adaptation. Similarly, Walmart's HR department leverages an ICA to pinpoint local store talent, widening the pool for internal development. Hybrid decision-making supported by AI is also being implemented in manufacturing, improving product design and supply chain management. Cummins, for example, is exploring generative AI to simulate extreme scenarios in powertrain design, aimed at bolstering resilience and reducing time-to-market. In the banking, financial services, and insurance sector, ICAs tackle areas such as risk management, regulatory compliance, personalised service, fraud prevention, and adaptation to market changes. Mastercard is integrating ICAs across departments to harness insights from onboarding, customer care, and sales, in order to improve operational efficiency. LibertyGPT, an AI tool at Liberty Mutual, reportedly saved employees more than 200,000 hours in 2024 by quickly answering queries and summarising large volumes of information. Communications and technology companies are using ICAs to identify and act on valuable business opportunities. BT, the British telecommunications company, has developed Aimee, an AI assistant involved in 60,000 customer interactions each week, autonomously resolving around half of all product and billing enquiries while supporting advisers with the rest. Meta applies ICA frameworks to enable internal teams to make more informed product decisions, experiment with business models, and fine-tune user engagement strategies. The healthcare sector is also experiencing transformation through ICAs, especially in areas like drug discovery and patient care. The study found that using ICAs with scientific teams can prioritise promising drug candidates, potentially reducing drug discovery times by up to 30% and associated costs by as much as 40%. Defining accountability Companies implementing ICAs report outcomes that are not only more efficient but also more transparent and accountable. The design of decision environments - where rights are allocated and options presented - is central to the increased effectiveness of both human and machine collaboration. Ashok Krish, Head of AI Practise at TCS, outlined the impact of this new paradigm. "By augmenting human judgment with machine intelligence, ICAs shift AI from task automation to building superior decision environments for complex multi-factorial situations, enabling more trackable, traceable outcomes that ensure accountability. They help align talent development strategies with organisational goals, making it easier to identify and nurture high-potential employees in the AI-era. Ultimately, ICAs foster environments where human judgment and AI work together seamlessly to create connected organisation intelligence, where smarter and more informed decisions are made." David Kiron, Editorial Director at MIT Sloan Management Review, stressed the collaborative nature of these advances. "This isn't AI as co-pilot. This is AI and humans working together as architects to redesign how people perceive, weigh, and act on choices." The study also examines the importance of transparency in decision-making structures. Sankaranarayanan Viswanathan, Vice President and Head of Business Innovation Corporate Technology Office at TCS, stated, "The real challenge for enterprises isn't just making better decisions - it is recognising that decisions are merely the outcome of the choices they privilege or overlook. What we need are systems that foster intelligent choice architectures - enabling the organisation to see, understand, and act with awareness. Accountable AI demands clarity not only in outcomes, but in the choices considered, the priorities weighed, and the trade-offs accepted. Without this, intelligent systems will silently assume decision-making authority - often without oversight or recourse." Broader implications The joint research by TCS and MIT Sloan Management Review continues a longstanding relationship focused on understanding how enterprises can integrate and leverage new digital technologies effectively. The report provides sector-specific examples illustrating how organisations across diverse industries are configuring ICAs to optimise workflows, reassign decision rights, and enhance overall business performance.
Yahoo
23-06-2025
- Business
- Yahoo
MIT researcher shares key lessons from over 100 AI prompt-a-thons
Good morning. AI is accelerating a rethink of the finance function. Earlier this year, I spoke with Michael Schrage, a research fellow at MIT Sloan's Initiative on the Digital Economy, about his prediction that AI will eventually transform the CFO role into that of an AI-powered chief capital officer. Now, as generative AI and AI agent use become more prolific, Schrage and I reconnected to discuss a tool that's fast gaining traction in the enterprise: prompt-a-thons. These are structured, sprint-based sessions for developing prompts for large language models (LLMs) like ChatGPT and Gemini. Schrage has led more than 100 of these since 2023, including in executive education, MBA classes, and business settings. 'Prompt-a-thons aren't just workshops; they're mirrors,' he said. 'They reflect not only what people want AI to do—but how they think, what they value, and what they overlook.' In 60–90-minute sprints, small cross-functional teams design, test, and iterate prompts to improve KPIs, clarify workflows, and challenge assumptions. According to Schrage, most participants discover their initial thinking is 'flawed, shallow, or stuck in spreadsheet autopilot.' The prompt-a-thon process reframes prompting as a high-impact diagnostic and design discipline—engineered for fast, actionable insight. 'It's not just about using AI more effectively—it's about thinking and collaborating more intelligently with it,' he said. For many finance leaders, the instinct is to upskill people on AI. Schrage suggests flipping the frame: 'Let's prompt your cost centers and forecast failures until something breaks—and gets better.' He points to financial planning and analysis (FP&A) as a particularly powerful starting point. Prompt-a-thons here often surface hidden data, unchallenged assumptions, and areas of organizational ambiguity or resistance. 'Prompt-a-thons aren't about rainbows and unicorns,' he added. 'But every so often, one shows up—usually disguised as a counterintuitive insight.' Why emphasize small teams or collaborative prompting? 'A prompt is a hypothesis about how the world works—and the world pushes back,' Schrage explained. 'Solo prompting explores. But team prompting evolves and that's where real learning happens.' Schrage compares the approach to sports analytics: 'You're not just trying to win once. You're trying to build the kind of team that keeps winning.' Though he would never position himself as a finance expert, Schrage offers three recurring lessons learned from finance-driven prompt-a-thons: —Prompts are scaffolds, not shortcuts. Great prompts don't replace critical thinking—they sharpen and amplify it. One-shot prompts are useful; iterative ones are transformative. —Avoid trying to automate what you don't understand. The danger isn't that LLMs get things wrong—it's that they confidently reinforce flawed assumptions baked into broken processes. —Look beyond cost-cutting. Most finance prompts chase efficiencies. The best ones expose strategic blind spots and generate new hypotheses worth testing. Schrage's key takeaway is that the quality of a team's prompts reveals the quality of its decision culture. 'People don't just learn how to prompt; they learn what their organization won't let them ask,' he said. 'That's when everything changes.' Prompt-a-thons expose what firms know, want to know, and avoid, he said. By closing these gaps, teams not only boost AI fluency—they get better at asking and answering the questions that matter, Schrage said. Sheryl This story was originally featured on Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
