This article is based on the Future of Work Podcast episode “Why AI and ESG Must Evolve Together to Protect the Future of Work” with Kate O’Neill. Click here to listen to the entire episode.

In the rush to innovate, are today’s leaders forgetting why they started? 

Businesses chasing AI without aligning to human-centered metrics risk building beautiful systems that fail spectacularly.

In a recent episode of The Future of Work® Podcast, Kate O’Neill, CEO of KO Insights and a seasoned digital transformation strategist, delivered a critical message to today’s business leaders: you must stop chasing metrics in isolation and start thinking in terms of ecosystems. 

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As AI becomes an increasingly central part of how organizations operate, leaders face a choice: retrofit outdated success models to new technologies, or reimagine the system altogether through the lens of purpose, resilience, and human flourishing.

With a career advising clients as varied as Google, McDonald’s, and the United Nations, O’Neill isn’t a futurist just making vague predictions. She’s a strategist with a clear framework and a call to action to solve AI integration problems: align artificial intelligence initiatives with Environmental, Social, and Governance (ESG) principles — not in name only, but in measurable, mission-driven ways that track real-world outcomes.

“I think ESG as a concept is valid. It’s not the principles that are wrong. It’s that we’ve been measuring the wrong things,” she said during the podcast conversation. 

This insight forms the cornerstone of O’Neill’s approach. In a world captivated by AI’s predictive capabilities and automation potential, organizations often overlook the encompassing impact of their decisions. 

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Are these technologies improving lives? Are they regenerating ecosystems — social or environmental — rather than extracting from them? Too often, she explains, companies confuse compliance with progress, chasing ESG as a branding exercise instead of a structural transformation.

This critique is not about abandoning ESG or digital transformation. Quite the opposite. It’s about evolving both.

From Checklists to Systems Thinking

The past decade has seen ESG reporting become a staple of corporate responsibility efforts. But O’Neill points out a flaw: ESG frameworks often push businesses to focus on standardized inputs and outputs rather than actual impact. 

These rubrics, while helpful for consistency, can fail to reflect the lived experience of people and communities affected by a company’s operations.

Instead, she argues for aligning with the United Nations Sustainable Development Goals (SDGs), a framework of 17 interrelated goals with actionable metrics designed to improve life for all — not just shareholders.

To her, that’s a better approach, as most businesses are doing something that could be furthering the SDGs, but they just don’t necessarily realize it.

From water access and infrastructure to gender equality and education, the SDGs provide a nuanced, flexible way for companies to identify where their operations already intersect with meaningful societal progress. 

More importantly, they allow companies to evolve those operations in a direction that’s measurable, values-aligned, and resilient.

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Making ESG Real in the Age of AI

AI technologies are tools that mirror the systems they’re built within. When integrated blindly, AI can amplify inequities and environmental damage. But when aligned with well-defined social goals, it can act as a force multiplier for good.

Consider how companies often rush to replace human labor with AI in the name of efficiency. O’Neill challenges this logic, not just from a social justice perspective but from a business strategy standpoint. In many cases, this kind of substitution overlooks deeper ESG implications — regional job displacement, lost organizational knowledge, reduced resilience in the face of uncertainty.

“Additive” use of AI, she argues, is far more effective than “replacing” strategies. Enhancing human capability, rather than removing it, yields more sustainable organizations.

This philosophy stems from a fundamental distinction O’Neill highlights: the difference between sense-making and prediction.

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Humans interpret, synthesize, and apply judgment. Machines, even the most advanced AI, rely on data and probability. One of her favorite analogies comes from healthcare: a doctor can hear the emotional nuance in a teenager’s “I’m fine” — something no large language model can reliably decode today. 

In complex systems — like health, education, or public infrastructure — nuance matters.

A Fast-Changing Landscape Needs Slow, Strategic Thinking

Much of the anxiety among today’s executives comes from the pace of change. Technology is moving faster than ever, and leaders are under pressure to act quickly or risk irrelevance. But as O’Neill notes, movement alone isn’t enough. Strategic motion — guided by values and grounded in measurable, ecosystem-wide outcomes — is what will separate resilient organizations from fragile ones.

The goal is progress, not perfection, and that progress requires recognizing the trade-offs embedded in every transformation decision.

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We are already seeing early-stage consequences: water-intensive AI data centers straining local ecosystems; workers displaced without meaningful re-skilling pathways; energy use surging in areas already vulnerable to climate stress. 

What Companies Can Do Now

The path forward, according to O’Neill, is rooted in clarity, alignment, and iteration. Businesses don’t need to pivot overnight or rebuild their operations from scratch. They need to take stock of what they already do well, identify the SDG most aligned with their mission, and begin tracking meaningful, relevant metrics that reflect their contribution to a better future.

This can be as simple as adding one SDG-aligned KPI to a leadership dashboard or as complex as redesigning hiring practices to retain knowledge and community ties. What matters most is the intentionality behind the action.

For leaders struggling with how to begin, O’Neill offers practical guidance: don’t wait for perfect information. Move. Learn. Adapt. Align technology strategy with purpose — not in a silo, but as part of a larger ecosystem of human and planetary thriving. Because in the future of work, success will be defined by how wisely we integrated AI into the human systems that sustain us.

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Hangzhou, China (ANTARA/PRNewswire)- AliBtrip, a designated platform specializing in business travel management under Fliggy of Alibaba Group (NYSE: BABA and HKEX: 9988), has introduced an integrated AI business travel solution. The innovative solution features two key modules: an employee travel agent for personalized planning, and a corporate management agent to streamline financial administration and compliance through data-driven decision-making support.

AliBtrip distinguishes itself from other Travel Management Companies (TMCs) with its unique AI applications and digital transformation management, leveraging Alibaba’s extensive ecosystem. Recent data indicates that AliBtrip serves over 20,000 industry-leading clients and more than one million growth companies, with over 20 million corporate employees booking business trips through the platform.

“The foundation of business travel services lies in trust between employees and companies,” said Zhuoran Zhuang, Vice President of Alibaba Group and CEO of Fliggy. “AliBtrip’s solution aims to transform AI capabilities into tangible benefits, reducing both visible and hidden costs while enhancing value for our clients.”

Addressing the challenges of corporate travel

Unlike consumer-focused solutions, the AI implementation for business travel emphasizes efficiency and compliance at every stage. AliBtrip’s AI solution, powered by multiple intelligent agents, tackles the complexities of corporate travel through a sophisticated division of labor among agents. It integrates long and short-term memory management and real-time deployment of the Model Context Protocol (MCP).

Powered by AliBtrip and Fliggy’s extensive data from the hotel and travel sectors, this AI solution draws from a real-time price database for flights, accommodations, transportation, and dining, ensuring practical and effective travel planning.

Optimizing management with intelligent analysis

For businesses, AliBtrip’s AI acts as an expert in administrative and financial management, offering real-time strategic analytics and action support. This capability significantly reduces the transactional workload related to analysis, communication, and compliance.

In addition to its pricing database, AliBtrip’s AI solution can customize exclusive datasets that reflect each corporate client’s travel standards and employee preferences, aligning with the company’s policies and values.

Features such as a strategy center and natural language interaction streamline corporate management, with intelligent cost control options presented in clear, quantitative indicators and intuitive examples for decision-makers, allowing them to make adjustments with a single click. The AI can also analyze historical travel data to identify potential issues proactively.

Enhancing employee satisfaction through streamlined booking

For employees, AliBtrip’s AI simplifies the booking process, alleviating the burden of comparing travel policies and booking transportation, accommodation, and car services separately. The employee travel agent generates comprehensive itineraries based on three key inputs: purpose, time, and destination, linking seamlessly to the travel request in the system. After verifying departure and arrival locations, it autonomously creates the itineraries that include tickets, hotels, and transportation, all while considering factors such as weather and check-in times.

Real-time travel assistance enhances the overall experience, with automatic reminders and recommendations integrated into the travel itineraries that comply with corporate standards. This significantly reduces risks associated with budget overruns or non-compliance.

The AI solution also uncovers cost-saving opportunities often-overlooked; for example, suggesting business class tickets with early departures that could avoid overnight stays or prioritizing hotels that align with employee preferences within budget constraints.

“Employees should be served, not restricted,” said Shenyang Shi, General Manager of AliBtrip, highlighting the philosophical shift underlying the innovative solution. “By leveraging advanced travel planning algorithms and combining intent recognition capabilities with comprehensive datasets and route optimization, the platform demonstrates how AI can reconcile cost management with employee satisfaction, creating value for both businesses and their traveling workforce.”

About Fliggy

Fliggy is a wholly-owned subsidiary of Alibaba Group (NYSE: BABA and HKEX: 9988 (HKD Counter) and 89988 (RMB Counter), and is one of the leading online travel platforms in China. Fliggy places a strong emphasis on innovation in its products and services, catering to the increasingly personalized and diversified needs of consumers in both China and overseas markets.

Leveraging Fliggy’s advantage as part of the Alibaba ecosystem, merchants can benefit from the vast user base within the Group. Fliggy also collaborates with partners through a full-service management format, helping more merchants, especially small and medium-sized ones, easily and efficiently share opportunities enabled by digitalization.

Fliggy’s long-term strategy is to promote the digital transformation of the tourism industry, using an open platform and mechanisms to help the industry make better use of digital business infrastructure for their operations.

Source: Fliggy

Reporter: PR Wire
Editor: PR Wire
Copyright © ANTARA 2025

AI isn’t just upgrading business process management (BPM). It’s demolishing the old playbook and writing a new one. It’s about unleashing the true potential of both your workforce and your operations.

The demise of rule-based BPM

For decades, traditional BPM had one job: follow the rules. The core mission was to document, optimize, and automate processes. When exceptions arose, escalate to humans. Rinse and repeat. This approach was useful, but inherently limited. Processes ran on predefined rules, unable to adapt to changing contexts without human intervention. But today, businesses can’t afford to move at the speed of paper memos.

Enter agentic AI – intelligent systems that don’t just follow instructions but make decisions, adapt strategies, and optimize results in real time. These aren’t chatbots or simple automation tools. They’re digital agents that understand context, learn from experience, and act with purpose.

Machine learning (ML) algorithms can help spot complex patterns. Natural language processing turns customer complaints into actionable insights before they become crisis points. AI-powered process automation doesn’t just execute tasks – it improves how those tasks get done. These advanced technologies allow systems to anticipate bottlenecks and even make recommendations for improvement – transforming operational workflows into a source of continuous intelligence.

AI is an opportunity

Often, AI adoption is seen through the lens of fear – job displacement, loss of control, complex ethical dilemmas. While such concerns must be addressed through effective change management strategies and data governance, you cannot deny AI’s potential.

It improves operational quality. ML algorithms can detect anomalies before they become defects. In manufacturing, service delivery, or digital operations, this capability enables better output consistency for teams and customers.

Faster value. AI reduces cycle time, accelerating the overall speed of operations. Automated systems can execute tasks in seconds that once took hours. For example, invoice processing for suppliers that would get stalled for months, can now happen in days. This isn’t just faster processing – it’s faster decision-making, faster problem-solving, and faster response to market changes.

AI drives productivity and efficiency gains. Organizations have already started to see measurable productivity increase in software code generation, customer experience management, and complex document processing. These gains not only reduce operational costs but also free human talent to focus on higher-value, creative, and relationship-driven work.

Overcoming challenges

Adopting AI in BPM is not without its hurdles.

Investment anxiety holds businesses back. Integrating AI isn’t just about purchasing software licenses or services. It’s a complete rethink of how businesses operate. You need the right infrastructure along with quality data and governance. The upfront costs can feel overwhelming. So, start small. Pilot projects in non-critical areas. Prove value before scaling. But start.

AI thrives on collaboration across functions – especially, for service providers who need access to sensitive data to train and optimize AI models. Establishing secure, privacy-compliant mechanisms for data sharing is essential to building trust and delivering measurable outcomes.

AI tools are only as effective as the people using them. Your team needs to upgrade its skillset to communicate efficiently with AI systems. Without proper skills, AI investments deliver minimal returns. The good news? These skills are learnable. So, invest in training. Create practice environments. Celebrate early adopters who become internal champions.

Roll out solid adoption strategies. The c-suite needs to lead AI adoption for a coherent strategy and to understand the impact across the business. Show teams how AI can solve their daily frustrations. Let them experience the benefits firsthand. Train champions who spread enthusiasm organically. AI works best as an assistant. Not as a replacement.

From process automation to business intelligence

The real magic happens when AI transforms your operations into an intelligence engine.

Process mining tools can now map and analyze workflows in real time, uncovering inefficiencies that might otherwise go unnoticed. Predictive analytics can forecast demand, optimize staffing, and anticipate customer needs. Hyperautomation – the convergence of AI, robotic process automation, process mining, and other technologies, promises a future where end-to-end processes can continuously improve in performance based on live data.

Consider a global business services (GBS) provider managing accounts payable for multiple clients. Traditional BPM would process invoices according to predefined rules. AI-enhanced BPM learns from payment patterns, identifies potential fraud, negotiates payment terms based on cash flow predictions, and flags opportunities for early payment discounts.

The same process that once required human oversight can now generate strategic insights for businesses.

The future belongs to the bold

AI adoption isn’t a luxury anymore – it’s survival.

The moment demands bold leadership. Not the kind that waits for certainty. But the kind that acts when opportunity presents itself. Build collaborative frameworks with clients and partners. Master secure data sharing. Upskill your workforce. The teams that embrace AI assistance will outperform those clinging to old ways of working. Foster a culture where AI complements human judgment.

Most importantly, good strategies create better momentum. Experiment within a controlled scope. And then scale tested AI initiatives across functions. So, move forward with confidence.

The author is Diwakar Singhal, Global Business Leader, Genpact.

Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.

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