By Akshay Chhabra, Chairman and Managing Director, 1Point1 Solutions
For decades, the global and domestic Business Process Management (BPM) sectors were primarily governed by the principles of cost efficiency and the consistent provision of high-quality services. During this period, clients transitioned from a mindset of “let’s do everything in-house” to one of “let’s focus only on what matters most in-house.”
Generally, BPM was driven by rules and meticulous standards to optimise predictability and service standards. In essence, this was a deterministic engine built for stable environments but brittle in the face of change. In the new era, AI is rewriting the very rules of BPM engagement and redefining subtleties such as efficiency and long-term business value.
What’s New?
For years, the BPM industry excelled at perfection by automating repetitive tasks. The industry took generally agreed-upon low-complexity, high-volume tasks and executed them at swifter speeds. India’s famed BPO and BPM industry had a subtle advantage – two, in fact – reliability and competitive pricing.
In the modern era, two significant changes have occurred in the BPM industry. First, the traditional contracting model may no longer be suitable, and second, AI has empowered clients to generate meaningful results across various processes.
Newer AI models and capabilities offer intelligence to navigate complex situations. In fact, AI systems can be trained to navigate process trees, maps, and scope data, and to recognise real-time patterns, thereby preparing for the process. With this, the organisation can prepare for shifts in customer preferences in real time and align supply chains in a highly structured manner.
Adaptive Workflows
While AI is a great superpower, it does not diminish the role of BPM. On the contrary, the immediate reality is augmentation. That is, AI could handle high-frequency, low-judgment tasks such as routing, categorisation, and data extraction, while humans are freed to operate at a higher cognitive register, such as managing exceptions, navigating emotional complexity, and making judgment calls that the context demands.
The emergence of real-time, decision-centric workflows is another tangible shift driven by AI. In a traditional BPM environment, an exception — such as a unique customer complaint or a delayed shipment — would halt the entire process, requiring human intervention to resolve the deviation. AI changes this dynamic entirely.
Modern systems continuously scope the context and dynamically re-route work. An AI engine can instantly perform sentiment analysis, cross-reference the customer’s lifetime value, prioritise escalation based on risk, and even initiate a personalised remediation workflow, all without a single manual review.
Intelligent BPM does not emerge from algorithms alone but from the consistency and accessibility of data. A sophisticated AI engine or algorithm fed with poor or siloed data may be arguably more dangerous than “no-automation” at all.
Decision Scalability vs. Labour Scalability?
In the past, scaling BPM meant increasing headcount, as the global outsourcing model was built on labour arbitrage (more full-time equivalents (FTEs) to handle growing volume). But AI has changed this, replacing labour scalability with decision scalability.
AI systems are adept at managing millions of routine operational decisions and are therefore better at managing smaller, skilled teams in a diverse operational environment. This shift could also influence how “value” is priced.
With AI, the BPM industry is already evolving towards outcome-based models, where “value” is measured not by how many FTEs are added or how many calls are handled, but by consensus on business impact, prediction accuracy, or operational resilience.
New Pillars of BPM
With great intelligence (just like powers) comes great responsibility, and as we embed AI more deeply into decision-making, concerns about transparency and accountability have come at the forefront. AI errors have been reported as technical glitches, but in an operational environment, a glitch or even a “hallucination” could be a governance nightmare.
In sectors such as banking, healthcare, and insurance, where automated decisions carry regulatory weight, the future of BPM rightly depends on how responsibly the governance layer is designed, rather than on vanilla automation. Enterprises require AI observability, audit trails, and human-in-the-loop controls, all integrated directly into their BPM architectures.
Perhaps the most significant shift is philosophical. BPM was traditionally seen as a cost centre — a function to keep the lights on as cheaply as possible. AI is transforming it into a strategic business capability. Intelligent BPM enables enterprises to anticipate disruptions, hyper-personalise customer journeys, and dynamically reallocate resources to seize market opportunities.
The organisations that win will not be those with the largest AI budgets. They will be those capable of meaningfully integrating AI into their workflow DNA and operational culture. The future of BPM is no longer about automating tasks.
Artificial Intelligence isn’t just enhancing BPM and redefining its purpose as the driver of enterprise resilience and growth. As this model matures, we will witness more transitions – including metrics to evaluate and decision accuracy rates, as well as cultural recalibration for a better human-AI collaborative model.