The recent recognition of Newgen Software as a Notable Vendor in Forrester’s Digital Process Automation Software Landscape for Q3 2026 signals a meaningful shift in how enterprises view end‑to‑end process intelligence. Forrester’s analysis, which evaluated 39 vendors across the DPA spectrum, highlights the growing importance of platforms that combine modeling, orchestration, dynamic case management, and AI‑led support into a cohesive operating model. This acknowledgment places Newgen alongside other innovators that are helping organizations move beyond isolated task automation toward truly integrated, intelligent enterprises. For decision‑makers navigating a crowded vendor landscape, such third‑party validation offers a data‑driven reference point when assessing strategic fit, technological depth, and market relevance. The timing of this recognition is particularly pertinent as businesses accelerate AI adoption and seek platforms that can scale intelligent automation without creating new silos or compliance risks. Understanding the criteria Forrester used—geographic reach, industry focus, deployment flexibility, and extended use‑case vision—provides a lens through which prospective buyers can align Newgen’s strengths with their own transformation goals.
Digital Process Automation, as defined by Forrester, encompasses platforms that enable the creation of process applications grounded in advanced programming principles, modeling, orchestration, dynamic case management, and AI‑led support. This definition moves beyond traditional workflow tools by emphasizing the ability to adapt processes in real time based on contextual data, embed intelligence directly into process logic, and manage complex, long‑running cases that span multiple systems and stakeholders. In practice, a DPA platform serves as the connective tissue between legacy systems, modern cloud services, and human workers, allowing organizations to design, execute, and monitor processes that are both resilient and responsive. The emphasis on AI‑led support means that these platforms are not merely passing data through static rules; they are capable of learning from execution patterns, suggesting optimizations, and even autonomously handling routine decisions. For enterprises operating in highly regulated sectors, this capability translates into faster audit trails, reduced manual intervention, and the ability to enforce policy consistently across heterogeneous environments.
Forrester’s report underscores an “automation imperative” driven by AI‑based optimism, revealing that 54% of automation decision‑makers believe their organizations must prioritize end‑to‑end automation over piecemeal workflow or task‑level initiatives. This statistic reflects a growing realization that isolated automation projects often create fragmented efficiencies that fail to deliver measurable business outcomes at scale. When automation is limited to individual tasks, handoffs between systems become points of friction, data loses context, and the overall process remains brittle. End‑to‑end automation, by contrast, seeks to create a seamless flow where information, decisions, and actions move cohesively from initiation to resolution, enabling faster cycle times, improved compliance, and richer customer experiences. The shift toward end‑to‑end thinking also demands platforms that can orchestrate not just human work but also machine‑generated actions, IoT signals, and AI‑derived insights, thereby creating a true intelligent operating model that adapts as business conditions evolve.
At the heart of the current DPA evolution lies the concept of agentic execution—a trend Forrester identifies as the market’s main driver. Agentic execution refers to AI agents that dynamically construct work patterns based on real‑time context, evolving conditions, and organizational goals, thereby augmenting or even replacing rigid, predefined workflows. Unlike traditional BPMN‑style models that require explicit mapping of every decision point, agentic systems infer the next best action from available data, policies, and historical outcomes, allowing processes to self‑optimize in response to changing circumstances. This approach is especially valuable in environments where exceptions are the norm rather than the exception, such as claims processing in insurance, patient intake in healthcare, or permit approvals in government. By embedding AI agents directly into the orchestration layer, enterprises can achieve higher straight‑through processing rates, reduce reliance on manual triage, and maintain agility when regulatory requirements shift.
Despite the promise of agentic execution, Forrester warns that scaling AI‑embedded automation from pilot to production remains a significant hurdle for many enterprises. Common pitfalls include multisystem integration gaps, where AI models cannot access necessary data across legacy ERP, CRM, and custom applications; human‑interaction breakdowns, where workers struggle to trust or effectively collaborate with autonomous agents; and regulatory compliance issues, particularly when AI‑driven decisions lack explainability or auditability. These challenges are amplified in highly regulated industries such as finance, healthcare, and public sector, where every action must be traceable, justifiable, and aligned with strict governance frameworks. Successful scaling therefore requires not only robust AI capabilities but also strong data governance, clear change‑management practices, and platforms that provide built‑in controls for model monitoring, bias detection, and policy enforcement. Vendors that address these concerns holistically are better positioned to help customers transition from experimental AI projects to reliable, production‑grade automation.
Newgen’s profile in the Landscape reflects a strategic focus on the geographic regions of North America, EMEA, and APAC, and on three core industries: financial services and insurance, government and public sector, and healthcare. This alignment is no accident; these sectors share common characteristics that make them ideal beneficiaries of sophisticated DPA solutions. They typically face intense regulatory scrutiny, manage long‑running case‑type processes that involve multiple stakeholders and document‑heavy workflows, and require high levels of auditability and security. For instance, a loan origination process in banking must satisfy KYC/AML rules, coordinate credit checks, integrate with external rating agencies, and maintain a tamper‑proof audit trail—all while delivering a seamless customer experience. Similarly, healthcare patient journeys involve consent management, insurance verification, clinical documentation, and billing, each step subject to strict privacy regulations. Newgen’s emphasis on these verticals suggests that its platform is engineered to handle the complexity, variability, and compliance demands inherent in such environments.
Deployment flexibility emerges as a critical differentiator for Newgen, as the vendor reports offering all three primary deployment models tracked in the Forrester study: hosted/private SaaS, multitenant SaaS, and on‑premises. This versatility is especially valuable for prospects in regulated industries where data residency, latency requirements, or existing infrastructure constraints may prohibit a pure‑cloud, multitenant approach. Hosted/private SaaS offers a dedicated, single‑tenant cloud environment that can satisfy data‑sovereignty mandates while still delivering the operational benefits of cloud‑based updates and scaling. On‑premises deployment remains essential for organizations that must keep certain data or processes behind firewalls due to legacy contracts, security policies, or performance considerations. By supporting all three models, Newgen enables customers to choose a deployment architecture that aligns with their risk tolerance, IT strategy, and regulatory obligations, without being forced into a one‑size‑fits‑all solution that could hinder adoption or create future migration challenges.
Beyond the foundational capabilities every DPA vendor must deliver—such as business workflow automation, department‑ and end‑user‑focused automation, employee support agents, endpoint orchestration, and incident and service request case management—Forrester asked participating vendors to highlight up to three extended use cases that represent areas of strategic focus. Newgen selected AI agent industry outcomes, customer self‑service, and document automation as its priority areas. This selection underscores a belief that the next wave of value will come from embedding AI agents that can drive measurable industry‑specific results (e.g., faster claims settlement, reduced underwriting time), empowering customers to serve themselves through intelligent, context‑aware portals, and automating the ingestion, classification, extraction, and routing of the vast volumes of unstructured documents that still dominate many enterprise processes. Together, these extended use cases address the pain points of manual document handling, fragmented customer touchpoints, and the difficulty of translating AI insights into tangible operational improvements.
The commentary from Runki Goswami, CMO of Newgen Software, reinforces the company’s vision of orchestrating intelligent enterprises where AI is not an add‑on but a core ingredient of the operating model. She notes that AI is redefining the enterprise by shifting focus from automating isolated tasks to orchestrating intelligence across the entire business, enabling faster decisions, operational resilience, and superior customer experiences. This perspective aligns with the broader market movement toward unified platforms that break down silos between processes, content, communications, and AI, creating a single source of truth for decision‑making. Goswami’s emphasis on trust, governance, and control built into the orchestration layer highlights the importance of ensuring that AI‑driven automation remains transparent, auditable, and compliant—a critical requirement for the industries Newgen targets. Her statement also signals confidence that the Forrester recognition validates Newgen’s long‑term investment in a holistic, intelligence‑first approach to enterprise automation.
Forrester’s report also flags adaptive process orchestration (APO) as the “top disruptor” to traditional DPA, noting that APO seeks to replace conventional visual workflow design with plain‑language agent instructions that describe desired outcomes rather than step‑by‑step sequences. This shift reflects a broader trend toward intent‑based automation, where users declare what they want to achieve (e.g., “approve a loan application if risk score is below threshold and documents are complete”) and the platform figures out the optimal execution path using AI agents, rule engines, and dynamic case management. As APO matures, we can expect increasing feature overlap between DPA and APO platforms, blurring the lines between what was once considered a separate category. For enterprises making long‑term platform decisions, this convergence means that investing in a DPA solution with strong agentic capabilities, flexible orchestration semantics, and a roadmap toward intent‑based design can future‑proof their automation investments and reduce the risk of needing to replatform as APO gains traction.
For organizations evaluating DPA vendors in light of these insights, several actionable steps can help ensure a successful selection and implementation. First, clearly define the end‑to‑end processes you intend to automate, mapping out data sources, decision points, regulatory touchpoints, and desired customer or employee outcomes. Second, prioritize vendors that demonstrate proven agentic execution capabilities, transparent AI governance, and support for the deployment models that match your IT strategy—whether that be private cloud, multitenant SaaS, or on‑premises. Third, request concrete references or case studies from industries similar to yours, focusing on metrics such as straight‑through processing rates, reduction in manual handling, audit readiness, and customer satisfaction improvements. Fourth, evaluate the vendor’s roadmap for adaptive process orchestration and intent‑based features to ensure the platform can evolve alongside emerging trends. Finally, invest in change‑management and user‑training programs early, as the success of intelligent automation hinges not only on technology but also on people’s trust in and ability to work alongside AI‑driven agents. By following these steps, enterprises can move beyond pilot projects to achieve scalable, resilient, and compliance‑friendly automation that delivers real business value.