Evolution of Automation platforms<\/h2>\n
Automation is a constant human endeavor. Every knowledge worker is familiar with the humble \u201cmacro\u201d \u2014 a shortcut for a repetitive set of commands to add an extra boost of productivity.<\/p>\n
Early automation efforts focused on workflows like Quote-to-Cash, Payroll with engineers writing bespoke code, stitching together these workflows governed by static rules and de\ufb01nitions. These brittle early approaches drove development of the first generation of automation platforms, such as:<\/p>\n
- \n
- Robotic Process Automation (RPA) platforms<\/strong> deliver maximum value in automating repetitive manual tasks. They combine a library of predefined workflows and a low-code\/no-code platform to help users build their own playbooks. RPA platforms have progressively incorporated AI\/ML models to expand their capabilities.<\/li>\n
- iPaaS\u00a0platforms<\/strong> like\u00a0Workato<\/a> started by creating a middleware layer to integrate data, application sources, and APIs to connect different resources. This data layer is a critical input for Automation engines and creating a clean interface is the first step in the Automation journey.<\/li>\n
- Low-Code Task Automation\u00a0platforms<\/strong> offer a predefined set of integrations with a simple UI to automate repetitive tasks for knowledge workers and SMBs.<\/li>\n
- Various\u00a0Vertical Automation<\/strong>\u00a0approaches focused on specific workflows in domains such as supply chain, IT operations, and developer ecosystem as well as chatbots for customer-facing use cases like help desks and customer service teams.<\/li>\n<\/ul>\n
While these platforms significantly reduced repetitive work, complexity remained in the bootstrapping needed with predefined workflows or consultative rollouts to realize the value of automation. Implementations have also been brittle to operating environment changes in the enterprise.<\/p>\n
GenAI has the potential to accelerate this automation journey as incumbents incorporate compelling capabilities into their platforms today, builders experiment with new architectures, and researchers push towards the ultimate goal of an autonomous Arti\ufb01cial General Intelligence (AGI).<\/p>\n
![\"Evolution](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/05\/Evolution-of-AI-automation-architectures.jpg\")
<\/p>\n![\"predictive](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/05\/predictive-generative-general-purpose-AI.jpg\")
<\/p>\nAI in Automation: Different players, distinct approaches<\/h2>\n
Automation in the enterprise is often a complex task, some practitioners even refer to it as complex orchestration of multiple elements to execute workflows. With the advent of genAI, incumbents and startups\/ScaleUps are approaching this opportunity from different angles.<\/p>\n
- \n
- RPA and Task Automation platforms bring a significant incumbency advantage, with a deep library of automated workflows and experience working with enterprises on complex workflows. GenAI offers an opportunity to address the brittleness and bootstrapping problems with a simplified user experience.<\/li>\n
- Application platforms like Microsoft 365 and Notion are embedding AI directly into the platform and user workflow to help complete tasks, o\ufb00er suggestions, and generate content to assist in the user\u2019s workflow.<\/li>\n<\/ul>\n
![\"AI-native](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/04\/Chart03-Opt-2-e1714404254593.png\")
<\/p>\nAI-native approaches start with an application or workflow and reimagine it from a \ufb01rst-principles perspective. On the application front<\/a>, a new generation of productivity tools like Swimm and Writer<\/a> have provided a compelling showcase for genAI transformation of work. Similarly, many vertical applications from sales, marketing, legal, and \ufb01nance use genAI capabilities to simplify complex workflows.<\/p>\n
LLM providers and startups\/ScaleUps are taking a fresh approach to automation leveraging Agents to harness genAI capabilities to execute simple workflows. Other approaches connect LLMs with the required \u201cscaffolding\u201d to address complex workflows and applications. Agentic automation<\/a> today is an area of constant innovation<\/a> and research<\/a> as builders experiment with models, architectures,<\/a> and tools.<\/p>\n
RPA and Task automation platforms<\/h2>\n
Current generations of automation platforms have aggressively embraced newer ML and AI models as part of their platforms. A brief overview of the current state of these platforms is discussed below:<\/p>\n
![\"advanced](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/04\/Chart04-e1714404152845.png\")
<\/p>\nUser Interface<\/strong> connects to a low-code studio where users can build, deploy, and verify automations. This interface is also used to monitor performance, track usage per policy, and even measure ROI for the automations they create.<\/p>\n
iPaaS<\/strong> plays a critical role as the middleware, bringing together data from applications, data stores, and event streams to create an efficient interface to the automation layer.<\/p>\n
Automation layer<\/strong> uses the template from the studio to either select from pre-built playbooks, a library of predictive ML models and tools, or execute new workflows. Some common use cases include:<\/p>\n
- \n
- Extracting data from unstructured sources like images or emails and fill out a form.<\/li>\n
- Observing humans (e.g. read screen, track keystrokes) to produce a repeatable workflow or suggest potential new automations.<\/li>\n
- Extracting data from an inventory system and use ML models to create a forecast.<\/li>\n<\/ul>\n
Incumbents are using genAI to both simplify user engagement and offer new workflows, such as:<\/p>\n
- \n
- Type in a task such as \u201csales prospecting,\u201d and the copilot translates the intent and searches the library of automations to offer users a starting point for their task.<\/li>\n
- Create a form and update it with the appropriate fields based on pre-trained templates. Fill in the data extracted from various unstructured sources.<\/li>\n
- Generate the \u201clow\u201d code to create automations based on NL description along with test cases to validate the output and a description of the workflow.<\/li>\n<\/ul>\n
AI tools help these platforms build on their incumbent advantage (customers and playbooks) by helping accelerate time-to-value for users. Better UI\/UX helps by reducing the consultative bootstrapping users typically need to get started in complex deployments. As LLM capabilities evolve, we can expect RPA and Task Automation to also grow in capability.<\/p>\n
\u201cIn the future all human interaction with the digital world will be through AI agents.”
\n<\/em>\u2013 Yann LeCun<\/a>\u00a0<\/em><\/p>\nAgentic automation frameworks: Copilots\/GPTs and Agents<\/h2>\n
It might be helpful to define terms commonly used to refer to genAI use cases in the market.<\/p>\n
- \n
- Copilots <\/strong>are genAI-based interfaces to existing applications and platforms, which offer users simplified ways to discover and augment their existing features.<\/li>\n
- Agents<\/strong> combine LLM capabilities with code, data sources, and user interfaces to execute workflows. There are a couple of approaches builders are working on:\n
- \n
- Simple wrappers around an LLM or an LLM trained for a specific task (code gen).<\/li>\n
- A Mixture of Experts architecture with \u201cscaffolding\u201d to combine task-specific agents, pre-defined code\/workflows, and external tools to reimagine applications or automate complex tasks.<\/li>\n<\/ul>\n<\/li>\n
- General Purpose Agents\u00a0<\/strong>aim to automate any task by simply describing it. This remains a long-term goal for researchers requiring ongoing AI advancements \u2014 learn more in “References and further reading,” below.<\/li>\n<\/ul>\n
No-code agents\/GPTs<\/h3>\n
AI Agents started as experiments, with builders such as Yohei Nakajima releasing projects like Baby AGI, which built on native LLM functionality to run simple automations. LLM providers now offer compelling no-code platforms with a library of plugins to external resources to build custom versions of the LLM. For a lot of simple tasks or one-off automations, this might be a fast way to get going.<\/p>\n
In this approach, a no-code console allows users to provide a detailed description of the task or use few-shot prompting to guide the LLM in building a task agent. LLM providers now offer integrations with data sources and applications, enabling the Agent to leverage external data as part of its workflow. The agent can also use proprietary data, using techniques like Retrieval Augmented Generation (RAG) for accuracy. APIs bring in external tools like search.<\/p>\n
![\"Advanced](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/04\/Chart05-e1714404681350.png\")
<\/p>\nAdvanced Agents, as shown above, can be built around an LLM\u2019s capabilities with glue code to bring together these various elements into a unified Agent. LLM providers are expected to continue launching new capabilities such as Agent modeling, collaboration, more tool access and prebuilt functions, reflection, safety guardrails, etc., making them powerful platforms for building Agents.<\/p>\n
\u00a0Mixture of Experts agent architectures<\/h3>\n
Builders like Bardeen, Imbue, and MultiOn are attacking the problem of delivering deterministic outcomes for complex workflows with a Mixture of Experts (MoE) Agent framework. The idea is to segment workflow into tasks assigned to specific Agents or functions and provide the Agent with a required \u201cscaffolding,\u201d including data, rich sets of tools, and interfaces. An approximation of the architecture is discussed below:<\/p>\n
![\"Mixture](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/04\/Chart06-e1714405013741.png\")
\n<\/strong><\/p>\nUser Interface<\/h4>\n
The user-facing LLM enables a user to describe the task and leverage the context windows to provide relevant context such as few-shot examples. Newer UI approaches incorporate user context and interactivity<\/a>, allowing the user to steer the Agent and refine its approach. This design enables \u201cHuman-in-the-loop\u201d to certify the final output. This design enables a more fluent \u201cHuman-in-the-loop\u201d interface to provide input and certify the final output.<\/p>\n
Task Agents<\/h4>\n
The workflow can be decomposed into distinct tasks executed by LLM Agents, some of which are detailed below. This optimizes current LLM capabilities and enables flexibility in using Task agents for specific functions with clean abstractions and options to upgrade or reconstitute them in the future.<\/p>\n
- \n
- Planning Agents today might propose a plan which breaks user intent into Task lists, which are approved by humans before routing for execution. This remains an active research area<\/a>.<\/li>\n
- Routing tasks map the task to the appropriate AI\/ML Agent or predefined workflow.<\/li>\n
- Functional agents are trained on specific tasks (genAI or predictive ML models).<\/li>\n
- Codegen Agent translates tasks into code, such as SQL query, for specific tasks.<\/li>\n
- Reflection LLM iterates on output to evaluate quality and refine final output. Platforms like Devin have demonstrated the effectiveness of this technique to improve output accuracy.<\/li>\n<\/ul>\n
Deterministic runtime<\/h4>\n
To deliver a final output that is consistently right, composing the different outputs from various tasks on a deterministic runtime has proven to be good practice. For example, for finance use cases, the code gen LLM generates SQL queries executed in the runtime for precise data extraction.<\/p>\n
The core design principle of the MoE architecture is to use AI\/ML models only where needed and leverage predefined workflows\/playbooks. LLMs are being used at design time with synthesis in a deterministic run time.<\/p>\n
Agent-to-Human AI interfaces<\/h4>\n
As discussed, a human-in-the-loop interface is a key aspect of the architecture today. Builders are Grounding Agents in User Context with numerous approaches from inputs in the context window to designing Agents as browser extensions, allowing them to observe user behavior and capture context. LLM Plugins bring in external data or tools, a key aspect of giving Agents more skills. Finally, the Agent can use APIs to communicate with user platforms such as email, productivity, and communication tools, emulating typical human workflow.<\/p>\n
Agent-to-Agent interfaces<\/strong> are an area of active research and development. In an MoE model, Task agents with different abilities will need to interact as discussed above. In time, we could envision Agents interacting with other Agents to accomplish tasks \u2014 extrapolating to an AI-enabled version of APIs connecting applications today.<\/p>\n
Considerations for enterprises deploying automation<\/h2>\n
- \n
- Most enterprises already use a range of automation platforms, from classic RPA and Task automation platforms for specific tasks to home-grown solutions. Productivity from AI is still more hype than reality. Candidates for genAI-based automation need to undergo a clear-eyed cost\/benefit analysis, as they will follow a similar maturation curve to previous approaches.<\/li>\n
- \u201cIt\u2019s the data, stupid.” AI agent performance is directly correlated with the quality and relevance of its training data. For a lot of enterprises, the journey begins with creating clean and focused data sets and pipelines that can ground the models.<\/li>\n
- The LLM landscape is evolving rapidly with the impending release of GPT5\/Llama3, which will reset the SOTA bar. At the same time, multiple models at GPT4-level performance are now available at attractive cost points. Enterprises now have models from different sources, at different cost-performance levels, to choose from based on use-case and functionality needs.<\/li>\n
- At a platform level, the market has several choices. Incumbents are embedding AI or offering copilots to accelerate time-to-value for users. Startups\/ScaleUps and LLM providers are taking an AI-native approach to reinvent vertical use cases or create new platforms to transform the cost, performance, and UX. The workflow and performance benchmarks should drive choices.<\/li>\n
- LLMs today are very sensitive to prompting and slight variations could cause drift in the model output. Establishing clear measures of performance at the use cases level (versus model level) is key. The same goes for governance and data security. Human-in-the-loop is a fundamental feature of all AI deployments today.<\/li>\n<\/ol>\n
Considerations for builders in automation<\/h2>\n
- \n
- Builders can adopt a \u201ccrawl, walk, run\u201d approach with genAI in automation platforms. A deep understanding of the user, the use case, its performance benchmarks, and leveraging LLM as a tool matching its capabilities to the task is critical to building a differentiated solution.<\/li>\n
- LLMs are primarily System 1 thinkers (reflexive responses based on trained data). Builders are using LLMs where needed for differentiated capabilities and pre-defined functions\/playbooks, ML models where possible. Focused and targeted data sets are critical to grounding the models.<\/li>\n
- For complex use cases \u2014 constant experimentation and the right \u201cscaffolding” to incorporate user context, access to external tools and datasets, Reflection mechanisms, etc., are foundational aspects of a \u201cMix of Experts\u201d Agent architecture.<\/li>\n
- Simple text-based UI is a good first step. Builders are innovating by adding\u00a0real-time interactivity<\/a>\u00a0and\u00a0Multimodal UI<\/a>\u00a0<\/sup>to create more active engagement for users to track LLMs task lists, evaluate outputs, and offer active feedback to steer output.<\/li>\n
- Bringing differentiated data sets with the right governance, as well as thinking through\u00a0safety tradeoffs, security guardrails, and performance<\/a> are important to avoid regulatory and compliance issues at deployment time in end user environments.<\/li>\n<\/ol>\n
Generative AI Agentic use cases<\/h2>\n
In our conversations[10]\u00a0<\/sup>with enterprises, there are various efforts underway in Agentic Automation. A few use cases are detailed below:<\/p>\n
- \n
- Chief Data Officer at an F100 telecom: \u201cWe are building autonomous agent workflows to link tables and databases together, joining multiple data sources and then taking actions or making recommendations from the data.\u201d<\/li>\n
- VP of Data and AI at a global consulting firm is building agentic workflows to enable data analysts to capture insights from disparate spreadsheets.<\/li>\n
- SVP Data and AI at an F500 construction and real estate firm is building an agentic application linking Palantir, OpenAI, and an internal copilot to select winning RFP bids from thousands of submissions.<\/li>\n
- SVP at a large bank, “I see two active use cases for GenAI. One, coding copilots which are rolled out to all our engineers, we are seeing around 20%+ productivity improvement for senior engineers. We are looking forward to newer capabilities here. Two, chatting with documents with LLMs, RAG has significantly improved the way to preserve privacy while grounding the models. Chatbots have been an experimental roll out, and we are still perfecting this use case to account for safety and compliance.”<\/li>\n
- Chief Digital Officer at a large bank, “Automation will take many forms in our estate. We have a large footprint of RPA, vertical automation platforms for ITSM, etc., and have built Agents on LLMs. We are actively experimenting with Agentic Automation architectures and learning as we go. Some incumbent vendors have shown great agility incorporating AI.”<\/li>\n<\/ul>\n
AI Automation market map<\/h2>\n
Click the market map to expand and explore.<\/p>\n
![\"\"](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/05\/AI-automation_market-map_0702.png\")
<\/p>\nThe pace of innovation in AI is only accelerating. It is important to caveat that a lot of the approaches discussed here are in the experimental and early production stages.<\/p>\n
As the world of Agents and Automation evolves, we are committed to actively tracking and updating the state of play in this space. Meanwhile, we welcome the opportunity to work with founders building in Agentic Automation, reimagining vertical applications, and differentiated infrastructure platforms and look forward to the feedback and dialogue with the community.<\/p>\n
\nNote: Insight Partners has invested in Workato, Jasper, Writer, Bardeen, Big Panda, Torq.<\/em><\/p>\n
References and further reading<\/h2>\n
[1] Yann LeCun, Objective Driven AI, Ding-Shum Lecture, Harvard University, 3-28-2024<\/a>
\n[2] Andrew Ng, AI Agentic Workflows The Batch, Issue 241<\/a>-245
\n[3] Evan Armstrong, What are AI Agents \u2013 And Who Profits From Them?<\/a>
\n[4] Bardeen.ai, https:\/\/www.bardeen.ai\/posts\/are-ai-web-agents-a-gimmick<\/a>
\n[5] Evaluating AI systems – Anthropic<\/a>
\n[6] Unraveling Gemini: Multimodal LLMs on Vertex AI<\/a>
\n[7] Devin: The AI software engineer with a game changing UI interface<\/a>
\n[8] 101 real world Gen AI Use Cases : Google Cloud<\/a>
\n[9] How people are really using Gen AI – HBR<\/a>
\n[10] Insight Partners IGNITE Enterprise customer briefings
\n[11] The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey<\/a> – Tula Masterman<\/a>,\u00a0Sandi Besen<\/a>,\u00a0Mason Sawtell<\/a>,\u00a0Alex Chao<\/a>
\n[12] PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change<\/a>–\u00a0 Karthik Valmeekam<\/a>,\u00a0Matthew Marquez<\/a>,\u00a0Alberto Olmo<\/a>,\u00a0Sarath Sreedharan<\/a>,\u00a0Subbarao Kambhampati<\/a>
\n[13] Market Map: Awesome AI agents: A list of autonomous agents<\/u><\/a>,\u00a0Agent Database, Staf.ai<\/a>, Generative AI\u2019s Act Two<\/a>, Gen AI Infra Stack<\/u><\/a>
\n[14] AIOS: LLM Agent Operating System – Kai Mei,\u00a0 <\/a>Zelong Li<\/a>,<\/span><\/a> Shuyuan Xu, <\/a>Ruosong Ye, <\/a>Yingqiang Ge, <\/a>Yongfeng Zhang<\/a>
\n<\/a>[15] Generative\u00a0Agents: Interactive Simulacra of Human Behavior, Park etal.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"The mainstreaming of AI tools has ignited hope for dramatic productivity improvements for knowledge workers and consumers alike. Transformer-based Large Language Models (LLMs) have demonstrated AI capabilities that are transforming workflows with new automation approaches. In the article below, we trace the automation journey in the age of AI and dig into some of the […]<\/p>\n","protected":false},"author":18,"featured_media":20827,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[131],"tags":[],"class_list":["post-19895","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-investor-pov","topic-industry-insights","domain-ai-ml"],"acf":[],"yoast_head":"\n
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- Bringing differentiated data sets with the right governance, as well as thinking through\u00a0safety tradeoffs, security guardrails, and performance<\/a> are important to avoid regulatory and compliance issues at deployment time in end user environments.<\/li>\n<\/ol>\n
- Planning Agents today might propose a plan which breaks user intent into Task lists, which are approved by humans before routing for execution. This remains an active research area<\/a>.<\/li>\n
- Agents<\/strong> combine LLM capabilities with code, data sources, and user interfaces to execute workflows. There are a couple of approaches builders are working on:\n
- Copilots <\/strong>are genAI-based interfaces to existing applications and platforms, which offer users simplified ways to discover and augment their existing features.<\/li>\n
- iPaaS\u00a0platforms<\/strong> like\u00a0Workato<\/a> started by creating a middleware layer to integrate data, application sources, and APIs to connect different resources. This data layer is a critical input for Automation engines and creating a clean interface is the first step in the Automation journey.<\/li>\n