Simultaneously, there’s a growing need for platforms that can orchestrate and manage not only traditional APIs and RPA endpoints but also a diverse range of AI models. And key vendors focused on RPA, DPA, iPaaS, and emerging AI agent building platforms are offering traditional process automation capabilities with market positioning that’s difficult to distinguish. They have: A unified set of similar capabilities.

Currently, we are observing three patterns of how agentic automation is used in productive environments: 1) AI agents used in isolation or an AI agent replacing existing automation, mainly an RPA bot; 2) RPA bots calling AI agents, and vice versa, along an automated process; and 3) several AI agents orchestrated along a process.

Bei näherer Betrachtung gleicht die Entwicklung eines KI-Agents zur Lösung eines Problems jedoch eher einem Pflaster als einer nachhaltigen, langfristigen Lösung – ähnlich wie bei einigen RPA-Bots in der Vergangenheit. Unsere Empfehlung lautet daher, zunächst das Problem zu identifizieren und zu verstehen, bevor Sie entscheiden, ob die beste Lösung ein KI-Agent, ein RPA-Bot, eine API oder ein besserer Prozess ist.

RPA Service Providers Guide RPA Programs Toward Stable Scale And Value Even as the sun sets on the golden age of robotic process automation, many organizations still look to use it to improve operational cost structures or support digital transformation initiatives. Companies eager to realize rapid outcomes and sustained value from their RPA initiatives turn to specialist RPA service firms and boutique consultancies for resources, tools, and best practices.

RPA vendors have invested in process intelligence to provide “dynamic work management,” and by 2026 we think data mined from the running process can manage work between humans and AI agents. Thousands of companies are well established with RPA platforms and will be open to using them for LLM-infused agents to leverage their investment. RPA has grown in part because of its ability to integrate easily with existing work patterns through UI integration.

The transformation in product propositions and shifts in client demand have put significant pressure on RPA service providers. This has evolved into an established market in which (see Figure 1): There remains a palpable client need for RPA specialists. Nuts-and-bolts RPA services remain vital to clients even as RPA itself becomes last season’s flavor. Experienced and mature clients, who expect more from their automation portfolios, are evolving beyond RPA and toward automation fabrics.

Bring Scale And Value To Your RPA Programs In 2023

The old pitfalls of RPA continue to plague deployments. Use Forrester’s Ten Golden Rules For RPA Success report as a guide to deliver on the promise of RPA and enjoy scale, value, and resilience in your RPA program in 2023. Acquisitions In The RPA Industry In Recent Years A table showing companies that acquired or got acquired by an RPA vendor over the past few years. This graphic has an associated spreadsheet that includes all data presented. Please access the spreadsheet for details. Figure 3

Automation skills, whether RPA, intelligent document extraction, conversational intelligence, or ML, remain in short supply. Hiring these skills or transferring them from systems integrator partners is equally hard. One of the biggest benefits of the COE is to grow RPA and adjacent automation skills in the business enterprise. Establishing a set of KPIs helps; they document and track the skills and efficiency you’re building.

The design environment unites technologies, such as applications, RPA bots, API calls, AI agents, and identity provider (IDP) workloads; it also identifies and addresses automation and execution gaps and overlaps. Use embedded AI to enhance this phase by recommending optimal process configurations and improvements.