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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Press Release – Calabrio

Workforce Intelligence, launched this week by Calabrio at its C3 event, is designed to bridge the gap between modern customer expectations and outdated workforce systems.

Customer experience (CX) is now a competitive battleground. In sectors from banking to healthcare, customers expect fast, seamless, and personalised support—often across multiple channels at once. But for the agents tasked with delivering this service, the job has never been harder. Staffing shortages, unpredictable demand, and the complexity of digital interactions mean burnout and turnover are at an all-time high. A new category of workforce technology aims to shift the balance.

Workforce Intelligence, launched this week by Calabrio at its C3 event, is designed to bridge the gap between modern customer expectations and outdated workforce systems. By embedding artificial intelligence at the core, the solution delivers real-time insights and automated support that make life easier for both managers and frontline staff.

Unlike traditional WFM systems, which were built for an earlier era of call-centre operations, Workforce Intelligence continuously adapts to changing conditions. Forecasting, scheduling, and intraday management become smarter and more accurate, reducing the manual tasks that typically bog down teams. The outcome: faster decisions, fewer errors, and more satisfied customers.

For agents, the benefits are tangible. Agent Assist, the platform’s Gen-AI scheduling tool, allows employees to use plain language to check rosters, swap shifts, or request time off. This empowerment fosters greater engagement and flexibility, both of which are critical in an industry where attrition remains a pressing challenge. By humanising the technology, the platform helps organisations not just retain staff but also elevate the quality of service.

The strategic value is equally clear for business leaders. In an era of economic pressure, the ability to improve forecasting accuracy and reduce operational costs is a game changer. As CTO Joel Martins noted: “We pioneered self-scheduling, multi-skill forecasting, and cloud-native WFM—now we’re leading again. Workforce Intelligence gives leaders the agility, cost savings, and real-time visibility they need to outpace change.”

This shift also aligns with broader trends in enterprise technology. Across industries, AI is moving from experimental pilots to mission-critical deployments. By positioning workforce management as a proactive intelligence hub rather than a back-office function, solutions like Workforce Intelligence demonstrate how AI can generate measurable business outcomes—from higher customer satisfaction to improved operational efficiency.

As companies continue to face pressure from both customers and shareholders, the ability to turn every interaction into a strategic advantage will become central. Workforce Intelligence is more than just another tech upgrade—it signals a new chapter in how businesses think about customer service: not as a cost centre, but as a driver of growth and loyalty.

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The Rise of Agentic AI and Emerging Threats

In the rapidly evolving world of artificial intelligence, a new breed of technology known as agentic AI is poised to transform how businesses operate, but it also introduces profound security challenges that chief information security officers (CISOs) are scrambling to address. These autonomous systems, capable of making decisions and executing tasks without constant human oversight, are being integrated into enterprise environments at an unprecedented pace. However, as highlighted in a recent article from Fast Company, many CISOs are ill-prepared for the risks, including potential misuse by malicious actors who could turn these agents into tools for cybercrime.

The allure of agentic AI lies in its ability to handle complex workflows, from automating supply chain management to enhancing customer service interactions. Yet, this autonomy comes with vulnerabilities. Security experts warn that without robust safeguards, these agents could be hijacked, leading to data breaches or even coordinated attacks on critical infrastructure. For instance, if an AI agent with access to sensitive financial data is compromised, the fallout could be catastrophic, echoing concerns raised in broader industry discussions.

CISOs’ Readiness Gaps Exposed

Recent surveys and reports underscore a troubling disconnect between AI adoption and security preparedness. According to the Unisys Cloud Insights Report 2025 published by Help Net Security, many organizations are rushing into AI without aligning their innovation strategies with strong defensive measures, leaving significant gaps in cloud AI security. CISOs are urged to prioritize risk assessments before deployment, but the pressure to innovate often overshadows these precautions.

This readiness shortfall is further compounded by human factors, such as burnout and skill shortages among security teams. The Proofpoint 2025 CISO Report from Intelligent CISO reveals that 58% of UK CISOs have experienced burnout in the past year, while 60% identify people as their greatest risk despite beliefs that employees understand best practices. This human element exacerbates vulnerabilities, as overworked teams struggle to monitor AI agents effectively.

Autonomous Systems as Risk Multipliers

Agentic AI’s interconnected nature amplifies these dangers, turning what might be isolated incidents into widespread threats. As detailed in an analysis by CSO Online, these systems are adaptable and autonomous, making traditional security models insufficient. They can interact with multiple APIs and data sources, creating new attack vectors that cybercriminals exploit through techniques like prompt injection or data poisoning.

Moreover, the potential for AI agents to “break bad” – as termed in the Fast Company piece – involves scenarios where agents are manipulated to perform unauthorized actions, such as leaking proprietary information or disrupting operations. Posts on X from cybersecurity influencers like Dr. Khulood Almani highlight predictions for 2025, including AI-powered attacks and quantum threats that could further complicate agent security, emphasizing the need for proactive measures.

Strategies for Mitigation and Future Preparedness

To counter these risks, industry leaders are advocating for a multi-layered approach. The Help Net Security article on AI agents suggests that CISOs focus on securing AI-driven systems through enhanced monitoring and ethical AI frameworks, potentially yielding a strong return on investment by preventing costly breaches. This includes implementing zero-trust architectures tailored to AI environments and investing in AI-specific threat detection tools.

Collaboration between security teams and AI developers is also crucial. Insights from SC Media indicate that by 2025, agentic AI will lead in cybersecurity operations, automating threat response and reducing human error. However, this shift demands upskilling programs to address burnout, as noted in the Proofpoint report, ensuring teams can harness AI’s benefits without falling victim to its pitfalls.

The Broader Implications for Enterprise Security

The integration of agentic AI is not just a technological upgrade but a paradigm shift that requires rethinking organizational structures. A Medium post by Shailendra Kumar on Agentic AI in Cybersecurity 2025 describes how these agents revolutionize threat detection, enabling real-time responses that outpace traditional methods. Yet, the dual-use nature of AI – as both defender and potential adversary – means CISOs must balance innovation with vigilance.

Economic pressures add another layer of complexity. With ransomware and AI-driven attacks expected to escalate, as per a Help Net Security piece on 2025 cyber risk trends, organizations face higher costs from disruptions. CISOs in regions like the UAE, according to another Intelligent CISO report, are prioritizing AI governance amid a 77% rate of material data loss incidents, highlighting the global urgency.

Navigating the Agentic AI Frontier

As we move deeper into 2025, the conversation around agentic AI’s security risks is gaining momentum on platforms like X, where users such as Konstantine Buhler discuss the need for hundreds of security agents to protect against exponential AI interactions. This sentiment aligns with warnings from Signal President Meredith Whittaker about the dangers of granting AI root access for advanced functionalities.

Ultimately, for CISOs to stay ahead, fostering a culture of continuous learning and cross-functional collaboration will be key. By drawing on insights from reports like the CyberArk blog on unexpected challenges, leaders can anticipate issues such as identity management in AI ecosystems. The path forward demands not just technological solutions but a holistic strategy that prepares enterprises for an AI-dominated future, ensuring that the promise of agentic systems doesn’t unravel into a security nightmare.