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HIGHLIGHTS
- In business process services (BPS), the potential of technology is yet to be properly tapped. The advent of LLMs that power generative artificial intelligence (GenAI) and agentic AI is set to change that.
- The integration of agentic AI into BPS workflows is not easy. It requires a deep understanding of business, industry, and process contexts for success. General-purpose LLMs, however, lack this ability.
- ‘Business knowledge fabrics’ (BKFs)—models fine-tuned on enterprise data, domain-specific, and industry-specific knowledge and context—can help overcome this challenge.
- When combined with agentic AI frameworks, BKFs enable the creation of intelligent digital agents that can perform complex tasks autonomously.
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Technology supercharging business process services
Innovation in technology has long been a driving force for industries, reshaping the way work gets done.
In business process services (BPS), however, the true potential of technology, especially artificial intelligence (AI), remains largely untapped, with most progress limited to task-level automation rather than intelligent transformation.
However, with the rapid emergence of agentic AI and generative AI (GenAI) powered by large language models (LLMs), there is a unique opportunity to transform the BPS landscape. These technologies promise systems that don’t just automate tasks but act autonomously as they make decisions, learn from interactions, and dynamically orchestrate complex workflows.
However, the successful integration of agentic AI into BPS workflows is easier said than done. Success depends on a deep understanding of business, industry, and process contexts—something general-purpose LLMs lack. For example, the context behind recruiting workflows, procurement lifecycles, customer conversations, or invoice processing nuances in BPS is highly specialized and industry dependent.
Enter ‘business knowledge fabric’ (BKF)
This is where ’business knowledge fabric’ (BKF) comes in.
Unlike generic models, BKFs are models fine-tuned on enterprise data, and domain- and industry-specific knowledge and context. They are trained to understand specific terminologies, nuances, priorities, and workflows of various business functions. When combined with agentic AI frameworks, BKFs enable the creation of intelligent digital agents that can:
- Interpret unstructured business data inputs
- Engage in multi-turn, context-rich conversations
- Navigate across diverse document and data types
- Adapt to feedback and evolving business goals
Together, agentic AI and BKF pave the way for a new operating model in BPS—one that is context-aware, self-learning, and built for enterprise-grade decision-making at scale.
A BKF consists of multiple contextual layers:
- Industry context: This layer represents the broader industry environment in which the enterprise operates. It includes factors such as customer personas, employee personas, supplier types, regulations, systems landscape, process terminology, and workflows.
- Enterprise context: This layer focuses on the specific enterprise or organization. It encompasses the company's strategies, organizational structure, geographies supported, operating model, people composition, technology investments, and internal policies that guide its operations and decision-making processes.
- System context: This layer pertains to the systems and technology in use within the enterprise. It includes the hardware, software, networks, and other technological infrastructure, such as ERPs, procurement software, SaaS platforms, contact center technology, that support the organization's operations and enable it to achieve its objectives,. This context is continually enriched with AI agents being developed as part of technology platforms such as ERP, SaaS.
- Process context: This layer deals with the data and information that the enterprise collects, processes, and utilizes, such as purchase requisition forms, product catalogues, employee data, supplier onboarding guidelines, contracting guidelines, customer experience delivery and measures, and process KPIs. It involves data sources, data management practices, data quality, and the insights derived from data analysis that inform business decisions.
Advantages of BKFs
The question is which specific areas will benefit the most from BKF-powered Agentic AI systems to supercharge BPS.
Let’s explore:
- End-to-end automation of complex processes: Traditional automation tools often operate in silos, processing structured data within narrow task boundaries. However, unstructured data is inherently complex and varied, making comprehensive automation challenging. Agentic AI systems, powered by business language models, break this limitation by functioning as autonomous digital agents capable of interpreting diverse data types such as PDFs, emails, scanned images, annual reports, human queries, and voice transcripts. In BPS, these AI agents extract relevant information like dates, amounts, terms, or customer details, and then validate the data. After verification, they can trigger downstream workflows like approvals, payments, or compliance checks without human intervention. This complete automation pipeline reduces manual data entry and reconciliation, accelerating cycle times from days to minutes, enabling BPS firms to scale operations seamlessly.
- Enhanced data accuracy and compliance: Unstructured data often includes complex, domain-specific terminology and regulatory language, making accurate interpretation a challenge. Business knowledge fabric trained on industry- and organization-specific texts become adept at understanding nuanced meanings. For example, in financial or healthcare BPS processes, a contract clause or invoice term might carry legal or compliance implications. Agentic AI can detect these subtleties, flagging potential risks or deviations from standards automatically. This leads to higher data accuracy—critical in heavily governed sectors like finance, insurance, or pharmaceuticals.
- Dynamic decision-making and exception handling: A major bottleneck in automation is exceptions and ambiguity handling. Traditional rule-based systems falter when data inputs deviate from expected formats or contain contradictory information, requiring human review that slows processes. Agentic AI systems powered by BKFs can dynamically reason through these uncertainties. They analyze context, consult knowledge bases, or raise exceptions with users to clarify unclear data. If necessary, they escalate complex exceptions to human operators with comprehensive context, speeding resolution. This ability to manage exceptions intelligently reduces downtime and keeps business processes flowing smoothly even under unpredictable conditions.
- Scalable knowledge management: BPS operations generate vast amounts of tacit knowledge locked in unstructured documents and communications. Agentic AI systems continuously ingest new data, extract insights, and update internal knowledge bases autonomously. Over time, this evolving knowledge repository improves the AI’s ability to understand new queries, anticipate issues, and suggest solutions proactively. Unlike static manuals or FAQs, the AI adapts to changes in policies, regulations, or customer preferences, ensuring that the organization’s institutional knowledge grows and remains relevant without extensive manual upkeep.
- Elevated customer experiences: Customer interactions often generate unstructured data like support emails, chat logs, or social media comments. Processing these inputs quickly and contextually is vital to delivering personalized, timely responses and even proactive care. Agentic AI powered by BKFs can analyze the sentiment, intent, and specific details of customer messages to provide accurate, empathetic replies or route queries to appropriate experts. Furthermore, by continuously learning from interactions, AI can anticipate customer needs, recommend tailored products or services, and flag recurring issues for proactive resolution, thereby elevating overall customer satisfaction and loyalty.
- Full stack capability leverage: Since BKF embeds ‘system context’, too, it can embed AI agents that are being made available as part of ERPs and SaaS platform to orchestrate the autonomous process. This ensures faster time to autonomy with an underlying common data context from various systems.
Some real-world use cases of BKF in BPS are:
- Invoice and claims processing: Automatically extract and verify data from diverse invoice formats and insurance claims, reducing processing time from days to just minutes.
- Contract lifecycle management: Review and flag key clauses, track compliance deadlines, and support negotiation workflows through intelligent summarization and alerts.
- Customer support automation: Analyze unstructured support tickets and chat transcripts to route queries appropriately and suggest resolutions.
Potential challenges in implementing BKF
While the promise is immense, adopting agentic AI operating models with BLMs requires:
- High-quality, domain- and industry-specific training data
- Robust data privacy and security frameworks
- Change management to integrate AI agents smoothly into human workflows
- Continuous monitoring of BKF to deliver desired business impact
Early adopters in the BPS industry stand to gain unprecedented efficiency, agility, and value from their unstructured data assets and existing investments in tools and systems by scaling BKF in production and going beyond pilots.
Innovation is imperative
Agentic AI operating models powered by business language models represent a paradigm shift for the BPS industry’s handling of unstructured data.
By enabling intelligent, autonomous, and context-aware processing, these technologies can transform bottlenecks into strategic advantages—unlocking new levels of operational excellence and customer satisfaction.
For BPS providers ready to innovate, embracing this AI-driven future is necessary. It is time to move to BKF specific to business such as finance knowledge fabric (FKF), supply chain knowledge fabric (SCKF), human resources (HR) knowledge fabric (HRKF), and customer experience knowledge fabric (CXKF).