Today, we’re introducing the newest addition to Ema’s Sales & Marketing suite: 

Designed to generate qualified leads on autopilot, Ema’s AI SDR helps businesses book 5x more sales meetings per Account Executive (AE), at just 1/10th the cost.

Some of the world's leading enterprises use Ema to transform their deal lifecycle, from automating sales intelligence and proposal creation to even contracting and deal closure. Today, with the launch of Ema’s AI SDR, we’re excited to extend these benefits to sales prospecting, which directly drives business growth.

Here’s how AI SDR helps sales teams grow their pipeline multi-fold with quality leads while keeping costs low:

AI SDR comes with advanced people- and company-enrichment capabilities, analyzing trillions of data points across multiple sources to identify high-intent leads.

Ema’s AI SDR creates a comprehensive buyer profile that helps it write highly personalized email sequences which resonate with the prospect, ensuring higher conversion rates and more meetings at scale.

AI SDR monitors key metrics, learns from past performance, and runs automated experiments to identify messaging that works. This ensures you achieve maximum ROI with every email sent. 

Ema’s AI SDR uses best practices to ensure your emails have high delivery and low unsubscribe rates, while also keeping your domain rating healthy.

Here’s more on each capability and how it works.

Ema’s advanced data enrichment capabilities go far beyond traditional LinkedIn searches or contact databases used traditionally by SDR teams. Ema uses the following sources to generate better outcomes for your campaigns:

 Ema utilizes a multi-provider data enrichment model to provide you the highest data quality and maximum coverage on contact and company related data, at a fraction of the cost.

 Ema tracks important events like job changes, new hires, promotions, and more across news and social media to help you pinpoint your ideal buyers at the right moments throughout their buying journey.

With a one-click CRM integration, or a simple CSV-upload, Ema allows you to import your leads into our intent classification models. Ema then enriches your data with additional insights tailored to each lead, helping you optimize outreach further.

Ema makes prospecting effortless. With a high-level description of your Ideal Customer Profile (ICP), Ema identifies relevant sample leads in real-time. (You can also tweak your criteria later, if required.) Once you finalize the ICP, Ema analyzes and scores all available leads, generating a refined list of the best-fit prospects based on our in-house intent classification models.

Ema then creates a detailed, well-researched profile for each prospect, with details on the prospect’s background, role, and their pain points. This allows your team to drive meaningful conversations with prospects and engage them more effectively.

Ema deeply understands your buyers, products, and business to craft hyper-personalized email sequences that resonate with prospects.

Ema builds deep knowledge of your company and products in just minutes, using relevant information from the web, product release notes, supporting documentation, blog posts, FAQs, etc. By consuming your sales playbooks, Ema adapts to your team’s best practices for targeting, messaging, and follow-up strategies.

Refine Ema’s output with simple instructions

Ema starts by presenting an example email template, giving you a clear idea of the structure and tone it will follow. Refining this template is effortless—just use natural language instructions.

For instance, if you prefer using bullet points to focus on product benefits, simply tell Ema “use bullet list”, and she’ll adapt the email to fit your preferences seamlessly.

Whether you’re targeting English-speaking prospects or otherwise, Ema’s multilingual capabilities enable you to connect with all your prospects. When it's hard to explain what you want, Ema’s AI SDR can be instructed to look at your past emails, so she can learn to incorporate your style effortlessly.

Smart Follow-Ups, Tailored to Your Strategy

Ema’s AI SDR creates a suitable strategy to follow-up with prospects, generating sample emails for each stage of the sequence. These follow-ups will also link to your case studies and blogs, and include relevant CTAs that are likely to convert.

Once you launch your campaign, Ema generates personalized email sequences for each lead as shown below.

You can view the email sequence generated for each lead, and edit further if required.

Ema tracks its performance against key metrics like emails sent, open rate and response rate and continuously improves by understanding what works and what doesn’t.

For every campaign, Ema generates multiple variations of instructions and runs automated A/B experimentation on them. By comparing performance across these versions, Ema identifies what resonates best with your audience and target market. These insights are then applied to future campaigns, allowing Ema to continuously refine its strategies and improve outcomes through real-time learning and feedback.

AI SDR’s self-learning capabilities are powered by Ema’s fusion model which enables it to generate the highest quality output at the least cost. Fusion is a mixture of expert models that utilizes most of the current foundational models — GPT 4, GPT 3.5, Claude, Gemini, Mistral, Llama2 (plus many more), and private models based on internal enterprise data — to maximize relevance and accuracy.

Ema provides a proprietary mailbox management and deliverability engine designed to minimize the chances of your emails landing in spam. It automatically monitors your DNS settings, assists with cleaning your email list, and offers email warmup capabilities to improve inbox placement.

The emails crafted by AI SDR adhere to best practices, ensuring they avoid spam filters. Additionally, AI SDR includes built-in features like daily sending limits and opt-out options, keeping your campaigns fully compliant with email sending regulations and maintaining your sender reputation.

Ema adheres to industry-leading compliance standards, including the GDPR, SOC 2 (Type I and II), ISO 27001:2022, HIPAA, and NIST. Our data governance also redacts sensitive information before passing it on to public LLMs. Along with top-tier encryption and customizable private models, this well-rounded approach to security underscores our dedication to handling sensitive data with utmost care.

Automate more than sales prospecting with Ema’s Sales & Marketing suite

Unlike other vertical solutions, Ema is a horizontal Agentic AI platform whose suite of AI employees automate every aspect of 

 — from sales intelligence and sales data analysis to proposal generation and prospecting.

Ema seamlessly integrates with the tools you already use, ensuring a smooth and unified workflow. Further, with Ema’s Generative Workflow Engine™, you can create custom AI employees that can tackle the most complex workflows in any business function, whether lead scoring, meeting scheduling, CRM updates, sales forecasting, or more. Ema’s one-stop solution for 

 provides a holistic tech stack that amplifies the work of all your teams, so they can be more efficient and creative in their work.

Hire Ema today to transform your sales and marketing

Our vision for Ema’s Sales and Marketing suite is to eliminate the repetitive, time-consuming tasks, so that human sales teams can focus on what they do best—building relationships.

With Ema’s AI SDR, you can save 90% of your time, otherwise spent on researching leads and crafting personalized emails—enabling your team to focus on meaningful outreach.

 today. We’re excited to see your calendar filled with high-quality sales meetings.

Introducing Ema’s AI SDR: 5x more sales meetings without adding headcount

Enterprises have a variety of tasks that can be solved via intelligent automation, but no single public LLM can achieve the accuracy needed in specific and dynamic enterprise contexts.

While using multiple models and applying model selection is the right way forward, traditional and ML-based methods of model selection do not reliably deliver high accuracy at low costs and latency.

intelligently combines the best public and private models to achieve the highest accuracy at the lowest costs and latency, while also preserving the security of sensitive enterprise data.

Enterprises face a daunting challenge when it comes to using AI, given how rapidly it is evolving. The key question is: 

How best can you leverage Large Language Models (LLMs) for various enterprise tasks, while achieving high accuracy at the lowest possible costs and latency?

With over 100 LLMs available today, each offering unique strengths in accuracy, latency, and cost and for different tasks, the natural inclination might be to hand-pick the best single model for each job—but this approach is fundamentally flawed and inefficient.

Agentic tasks in an enterprise range from highly simple to highly complex. For example, a straightforward task might involve generating routine email responses, or summarizing meeting notes. In contrast, complex tasks could be providing nuanced customer support, synthesizing vast amounts of enterprise knowledge, or conducting predictive analyses based on intricate data sets. The diversity and complexity of these tasks make it impossible for any single LLM to meet enterprise-grade requirements efficiently and securely.

The idea of selecting a "best" model assumes that one LLM can sufficiently balance the trade-offs between accuracy, latency, and cost across all types of tasks. But this is rarely the case. A model optimized for accuracy may be too slow for real-time applications, while a model that excels in speed may lack the depth needed for more sophisticated queries. Moreover, models that are cost-effective at scale might compromise on performance, leading to inadequate results for critical tasks.

An approach that combines multiple models for each task is thus essential. Enterprises need to move beyond the idea of a one-size-fits-all LLM and embrace using multiple models, each tailored to specific aspects of their agentic workflows.

Consider this: a lightweight model handles initial customer interactions or routine tasks, while a more powerful, resource-intensive model is used for in-depth analysis and complex decision-making processes. Such a strategic combination of models would ensure that each task is handled by the most appropriate tool, optimizing for accuracy, latency, and cost in a way that no single model can achieve.

But choosing the right combination of models in this manner, for a wide range of enterprise tasks, is no simple feat either. We explain why common approaches to model selection are inadequate for the enterprise, and how that shaped

 our vision to build EmaFusion™—our proprietary model that achieves the highest accuracy at the lowest costs and latency, in a manner that is secure, flexible, and efficient across all enterprise tasks

Why Model Selection or Model Routing is Inadequate

In their quest to leverage large language models (LLMs) for agentic tasks, enterprises have turned towards various model selection strategies hoping to identify the "best" model for each task. But both traditional and machine learning-based approaches to model selection have proven inadequate for the complex and dynamic nature of enterprise environments.

ML Expert Manual Configuration-Based Model Selection

One common approach involves having a machine learning expert painstakingly analyze each agentic task to identify the most suitable LLM. Through extensive trial and error, they configure a routing framework that hardcodes which task should be directed to which model.

While this method may seem thorough, it has significant drawbacks:

: Once a configuration is established, incorporating new models into the framework becomes challenging. The entire system may need to be re-evaluated and reconfigured, which is not only time-consuming but also limits the ability to quickly take advantage of advancements in LLM technology.

 This approach requires the expertise of seasoned machine learning professionals who must continuously monitor and adjust the system. The trial-and-error nature of the process is labor-intensive, costly, and prone to human error.

Static Solutions in a Dynamic Environment:

 Enterprises are dynamic, with constantly evolving tasks and requirements. A rigid configuration may not adapt well to changing needs, leading to sub-optimal performance as tasks evolve or new tasks emerge.

 As the number of tasks or models increases, the complexity of managing the routing framework grows exponentially, making it difficult to maintain efficiency across the system.

In these solutions, a machine learning model analyzes the prompt body of the agent's task and attempts to route it to a single LLM. While this method appears to solve for the limitations of the above approach, it also suffers from a few issues in enterprise environments.

 These models are typically trained on benchmark datasets available on the internet, which do not reflect the specific and nuanced tasks of an enterprise setting. The model selection process is thus not well-tuned to unique enterprise environments, leading to poor performance.

 In enterprises, agentic tasks are often complex, with prompts that may encapsulate multiple atomic tasks. For example, a single prompt might require generating a detailed customer report while simultaneously updating internal records and providing recommendations for the next steps. Existing machine learning-based selection models struggle with such complexity, often routing these prompts to high-capacity models that, while powerful, may be unnecessarily expensive and slow.

 Due to their inability to decompose complex prompts into manageable sub-tasks, these systems tend to over-rely on the most capable—and typically most expensive—models, leading to inflated costs and increased latency.

 The training data for these selection models often lacks the diversity and specificity required to accurately route enterprise-level tasks. This results in models that are not only biased towards general-purpose tasks but also fail to optimize for the diverse needs of an enterprise, where specialized knowledge and context are critical.

Both these model selection strategies, while valuable in certain contexts, fall short when applied to enterprise agentic tasks. They either lack the flexibility to adapt to new models and tasks or are constrained by training data and complexity, leading to inefficiencies and higher costs. There is a need for a more sophisticated approach, one that can effectively combine multiple models to meet the diverse and evolving demands of modern enterprises.

Model merging, the process of combining parameters or knowledge from multiple pre-trained models into a single unified model, has been explored in academic research as a method to leverage diverse model capabilities. Techniques such as 

parameter averaging, knowledge distillation, and weight interpolation 

have been proposed to merge models trained on different datasets or tasks.

These approaches remain largely theoretical, however, impractical for real-world use cases, particularly when attempting to merge closed-source and open-source models. Closed-source models often have proprietary architectures, undisclosed training methodologies, and inaccessible weights, making direct parameter alignment or knowledge integration infeasible. In addition, 

 further complicate the practical application of such merging efforts.

The fundamental incompatibilities between open and closed-source models—ranging from architecture differences to training data biases—highlight that model merging is primarily an academic exercise with limited utility in real-world scenarios. Practical approaches such as ensembling, Retrieval-Augmented Generation (RAG), or fine-tuning individual models for specific tasks are far more effective and implementable in production environments.

EmaFusion™: Inspired by how humans solve complex problems

 lies in the analogy of assembling a team of human experts to tackle a complex problem, where each expert brings unique strengths, perspectives, and domain-specific knowledge. Instead of relying on a single generalist model, 

EmaFusion™ integrates outputs from a diverse set of foundation models, smaller models, and domain-specific models, enabling a collective intelligence that surpasses the capabilities of any individual component.

This approach is inherently collaborative—each model contributes its specialized understanding to produce a well-rounded, nuanced solution. 

Unlike model routing, which dynamically selects a single model based on the input, EmaFusion™ leverages the collective output of all models, allowing it to synthesize diverse insights and subtleties that routing to any single model would miss.

mitigates biases inherent to any one model

, making it especially valuable for complex and multidisciplinary challenges.

Unlike traditional model selection strategies that fall short in the enterprise context, EmaFusion™ introduces a dynamic model fusion architecture that optimizes the use of all available large language models (LLMs), as well as many domain specific and private models, to deliver superior results.

Dynamic Model Fusion for Optimal Performance

EmaFusion™ is the first architecture designed to combine multiple LLMs dynamically, ensuring that each agentic task is executed with minimal trade-offs. By leveraging the strengths of various models,

 EmaFusion™ intelligently balances accuracy, cost, and latency, providing a tailored solution for every task

. This approach is especially critical in enterprise settings, where tasks can range from simple query processing to complex, multi-faceted operations.

One of the core innovations of EmaFusion™ is its ability to automatically synthesize training data. Starting with a small number of seed prompt templates—representative examples of high-level enterprise tasks—EmaFusion™ generates a comprehensive dataset that captures the nuances and diversity of real-world scenarios. This data serves as the foundation for training its novel two-tier Fusion network architecture, enabling EmaFusion™ to understand and predict the best model combinations for any given task.

The first tier of the Fusion network focuses on model prediction. It learns to anticipate which models are best suited for a specific agentic task without needing to run the task through the models themselves. This predictive capability allows EmaFusion™ to quickly and efficiently identify the optimal models, reducing the need for resource-intensive trial and error.

The second tier is where EmaFusion™ truly shines. EmaFusion™ learns how to combine the selected models in the most effective way. For example, consider a complex task like generating a detailed customer report that requires extracting information from multiple sources, summarizing key points, and providing actionable insights. EmaFusion™ might combine a model known for its data extraction accuracy with another model that excels in summarization, and yet another that specializes in generating actionable insights. 

The result is a cohesive output that is not only accurate but also delivered with optimal efficiency.

EmaFusion™  is designed to be highly flexible, with an architecture that can incorporate any model, whether it’s an open-source LLM, an API-only LLM, or an LLM fine-tuned with custom training data, ensuring that enterprises are not locked into any specific set of models. Instead, with EmaFusion™, they can continuously integrate the latest advancements in LLM technology into their workflows without disrupting operations. EmaFusion™ adapts to these changes seamlessly, so that enterprises always have access to the most effective tools for their needs.

The range and dynamism of enterprise environments demand technology and automation that goes beyond what any single LLM or model can provide. Further, traditional model routing methods between LLMs also fall short of enterprise needs, where tasks can be both simple or complicated, and are best addressed by a strategic combination of public and private models that optimizes for accuracy, costs, as well as latency.

EmaFusion™ transcends the limitations of traditional model selection by offering a 

 that can meet the evolving demands of enterprise agentic tasks. Its innovative architecture provides a powerful, flexible, efficient and secure solution for enterprises to harness the full potential of LLMs without being locked into any single one of them, or constrained by the typical trade-offs in accuracy, cost, and latency.

Why Does Model Routing Not Work? How EmaFusion is Revolutionizing the Art and Science of Combining Public and Private Models

To succeed with Agentic Business Automation (ABA), enterprises must transition from siloed, static tech stacks to horizontal, adaptive systems that enable data sharing and intelligent, real-time, secure decision-making by AI.

Enterprises will go from paying for access to software to the usage and outcomes generated by it, working with vendors that are accountable and aligned with strategic enterprise objectives.

As humans go from using software as an assistive tool to working with intelligent and autonomous systems, organizations will need to equip their new superteams with robust change management practices and continuous learning and upskilling.

When electricity was first introduced into American factories in the early 1900s, 

 for more than two decades. It was only after managers reorganized their production lines to use more distributed machinery, which was possible now because of electricity, that they could start reaping the benefits of this groundbreaking new technology.

" refers to this common historical pattern, where breakthrough technologies are often followed by initially slow productivity growth. The sharp takeoff in results only happens years or decades later, when surrounding processes, skills, and business operations have also aligned to generate benefits systematically. Technology alone is rarely enough.

We are now at another such inflection point with AI, where the results from this epochal new technology will accrue to those who know how best to use and operationalize it. AI is 

 to create USD$19.9 trillion in value for global GDP by 2030. But, as recent research has also 

, most businesses don’t have a clear AI strategy in place, and are unsure of how to measure its impact.

Especially when it comes to Agentic AI, the latest evolution of cognitive automation and artificial intelligence, the potential for disruption and the blind spots are both larger. Gartner predicts that 

33% of enterprise software will include Agentic AI

 by 2028, up from less than 1% today. As much as 15% of day-to-day work and decision-making will be handled by autonomous, agentic systems in the near future—creating a generational leap in human productivity across industries and functions.

, businesses have to rethink their systems and processes, as people go from using technology as a limited assistive tool to seeing it as another colleague, an intelligent, adaptive system that is capable of reasoning, taking action, learning from its own outputs and getting better with time, just as other humans would.

The DNA of this AI-first organization of the future will change how tech is deployed throughout the business, how it is procured and priced, how its success is measured, and how humans and technology work together. Based on 

our experiences of serving the world’s leading enterprises

 with Agentic Business Automation as a Service (ABAaaS), here’s what the enterprise ecosystem for Agentic AI success looks like.

The Technology Shift: From siloed, legacy tech stacks to horizontal, agentic systems

Why SaaS is Dead and the Future is Agentic

, current enterprise software is bloated. Cloud computing and SaaS tools were massive leaps from the tedious on-prem software of the 1990s, but enterprises in the 2020s are mired in data silos, operational complexity, and excessive spending on automation that remains largely assistive.

, traditional SaaS has failed to truly release humans from mundane work and to create transformational productivity gains. This is now possible with cognitive automation, particularly Agentic AI, which can autonomously execute complex business workflows end-to-end across the organization.

 utilizes a conversational, intelligent, adaptive layer of AI agents, who iterate and improve on their own output, work with each other and human colleagues, and can access external tools and systems in a secure, efficient fashion to execute complex tasks and create tangible outcomes in line with strategic business goals.

This is a sharp departure from copilot GenAI and traditional SaaS, which remain confined to vertical, assistive use-cases within singular functions like HR or sales and marketing. Current tech architectures are thus vertically siloed and rather static, and need to evolve to more 

 where different functions can talk to each other and share information in a secure, collaborative, and adaptive fashion.

 based on past and real-time data, adapting to changing conditions and taking intelligent actions across the organization autonomously.

must adapt to this new internal landscape where data sources are interconnected, and modular parts are interoperable. It is critical to ensure data integrity, compliance, and the security of sensitive information, while accommodating dynamic data flows and real-time decision-making by AI. Enterprises may adopt AI-specific frameworks, such as 

, to address the transparency and security aspects of interconnected systems.

Most importantly, enterprises need to deploy ABA that balances the versatility of public AI models with the safety, security, and specificity of private models. This hybrid approach solves the major problem with current enterprise GenAI, such as inaccuracies and hallucinations. 

 to optimally select the best combination of public and private LLMs for each task, producing the highest accuracy outputs at the lowest costs and latency, while maintaining strict control over sensitive enterprise data.

Enterprises that instead rely entirely on single models face the threat of inaccurate outputs, obsoletion, and the costs of maintaining and seeking the right models for evolving needs, which is a significant technology and investment risk.

When deployed correctly, ABA has the power to convert human teams into 

. The division of labor between humans and machines will continue to 

 towards machines, especially for more kinds of repetitive work. And as software shifts from being assistive to autonomous and accountable, enterprises will have to evolve how they procure and price such technology too.

"With Agentic Business Automation, software will go from being a mere tool that assists humans to being a collaborative, intelligent partner that makes teams harness their full potential."

The Process Shift: Rethinking Procurement and Pricing with Agentic Business Automation (ABA)

Traditional procurement processes are built around predictable seat-based SaaS pricing models, which charge for access to software rather than for the usage or outcomes delivered by such software. Such standard SLAs, with low accountability for ROI from vendors, do not align with the usage- and outcome-based capabilities of ABA, which can be priced by the quantum of work executed or the outcome generated by software.

This means that enterprises can choose vendors that offer 

with vendor incentives that are aligned with strategic business goals. This type of contract will ensure businesses only pay for the value delivered by software, fostering

from vendors. Contracts may thus move from rigid, narrow deliverables towards

, such as new productivity gains unlocked, increased decision-making efficiency, or more time for creative and strategic work. These results are better achieved with horizontal platforms, where agentic systems across functions can unlock new gains and more accurate results, ensuring long-term scalability—versus narrow, point-based solutions, which will remain confined to a specific function.

Vendor selection will thus be shaped by this consideration, so that there is long-term alignment between enterprise objectives and the vendor’s technological capabilities and business model. Ema’s horizontal platform empowers enterprises with a suite of AI employees across functions like sales and marketing, HR, customer support, as well as general use-cases like document generation. Ema also has a Generative Workflow Engine™ (GWE) that allows custom configuration of a pre-built library of agents to complete any complex enterprise workflow. This holistic approach ensures that intelligent agentic systems can excel at their own functions and also dynamically collaborate across them, breaking down data silos and enabling organizations to scale efficiently with evolving business needs.

Enterprises should also ensure that contracts align with 

 such as GDPR or CCPA, to ensure compliance and the safety of sensitive information. Ema's data governance redacts sensitive information before passing it on to public LLMs, enforcing compliance with leading industry standards through top-tier encryption, and customizable, private models.

To avoid vendor lock-in, enterprises should choose providers that support modular integrations and have a hybrid approach, so as to seamlessly transition if business goals or partnerships need to change. Only by revamping procurement processes to prioritize

 accountability, security, and adaptability,

 can enterprises capitalize on the transformative potential of Agentic Business Automation, while building resilient partnerships that scale with their needs.

The Metrics & Mindset Shift: Success in a post-ABA world

Given that technology is only one part of the equation in how productivity grows, enterprises must redefine the fabric of how work gets done, the role of software and its relationship to human beings. While traditional success metrics like cost reduction and task efficiency will remain useful and important, they may not capture the broader value of Agentic Business Automation for an organization.

The promise of ABA lies in its ability to create new opportunities from increased creative thinking and higher-order decision-making, greater cross-functional collaboration, while human beings are freed from repetitive work. For example, ABA may enable organizations to harness real-time intelligence from previously siloed data, driving new opportunities and better, faster decisions that positively impact the business both strategically, through new avenues of gain, and also tactically, via efficiency and cost reduction in existing avenues. For example, a retail enterprise using ABA might see a 30% reduction in repetitive stock management tasks, along with a 15% increase in time spent on customer engagement strategies, along with a new customer engagement possibility that wasn’t obvious before. Such higher-order, composite benefits may need new metrics to be quantified and seen as part of the organization’s success, such as:

Develop a set of metrics to assess not only how quickly decisions are being made, but also the quality of those decisions, such as whether they are made possible by using data synthesized across previously siloed enterprise systems.

Track the volume and time taken by workflows that involve multiple departments and systems over time, aiming for an improvement in these metrics.

Reduction in mundane work and increase in creative, strategic work: 

Tasks and workflows that were not previously automatable by copilot GenAI or traditional SaaS may be tackled efficiently by ABA. Track the kind of work that ABA can automate, and the percentage of time spent on it by humans, and the increased proportion of time spent on high-value, decision-making activities.

Creating the enterprise ecosystem to guarantee Agentic AI success will require robust change management practices.

, with leaders actively championing the strategic integration of AI at various levels of the organization, so that it is aligned with larger business needs. 

 will help, starting with targeted ABA deployments that expand as operations and teams grow more comfortable using intelligent software systems to achieve business goals.

 and track the workflows that are changing from the start, and to what effect, creating a strong internal pulse on how systems are evolving and how people can adapt to them. It will be necessary

 continuously train and upskill employees

 to get the best results from ABA, via prompting and interpreting AI outputs, making AI-augmented decisions, and collaborating with agentic systems securely, efficiently, and confidently.

Conclusion: Build the Enterprise Ecosystem for Agentic AI Success

To unlock the full potential of Agentic Business Automation, enterprises need to embrace a transformative, learning mindset across technology, processes, and cultural and organizational structure. Legacy tech stacks will move towards integrated, horizontal systems; procurement strategies will prioritize security, accountability, and adaptability from vendors; and new success metrics will be adopted to reflect Agentic AI’s broader impact on productivity, decision-making, and the quality of how work gets done. Software will go from being a mere tool that assists humans to a collaborative, intelligent partner that makes teams harness their full potential.

Organizations that adapt iteratively and quickly will see a significant competitive edge in the era of Agentic Business Automation. With Ema’s advanced capabilities, combining accuracy, security, and simplicity in one go, enterprises can begin their journey towards 10x productivity and innovation.

Building the Enterprise Ecosystem for Agentic AI Success

Beyond the rigid, brittle nature of RPA, and the limited, assistive capacities of Gen AI, the next big leap in enterprise productivity will come from the newest entrant in cognitive automation—Agentic AI.

When applied to business processes in an efficient and secure manner, Agentic AI gives birth to 

, where an intelligent, collaborative mesh of AI agents executes complex workflows end-to-end.

 is transformative for a wide range of use-cases across industries and sectors, reordering job functions, economies, and the very nature of work.

The history of enterprise automation is a story of great ambitions and mixed results. Ever since humans started working, they also looked for ways to work more efficiently. And the solutions that were invented each had their own strengths and weaknesses; every new technology has had certain benefits but also inherent limitations and challenges.

The journey of Robotic Process Automation (RPA) began in the late 1990s, as a technology that would assist humans by doing the most repetitive, specialized tasks. These were mostly physical tasks that didn’t need knowledge, understanding, or insight, which RPA could perform through codified rules in specific workflows. Early applications included website scraping, data migration, and specialized sets of actions that would be executed the same way each time.

But with a technology so rule-based, RPA was also brittle and rigid. For these “

, the slightest change in process would result in inaccuracies and broken workflows. RPA was thus 

It was long believed that more cognitive automation would provide better results, as software would deal with natural language, reasoning, judgment, establishing context and possibly the 

 of things to generate insights. But even as late as 2018, technologists 

 that cognitive automation was a while away.

Fast forward to 2024, and cognitive automation has arrived, and how. ChatGPT and other Gen AI products, which generate text, images, videos, interact with humans conversationally and even produce ‘insights’, have been among the 

 historically (at rates 2-3x the internet or personal computer). But in the enterprise, Gen AI has come with significant 

GenAI vs Agentic AI: From Assistance to Autonomy

, Gen AI only assists humans within existing workflows, like customer support, HR, or sales and marketing. It 

 with inaccuracies and hallucinations; data privacy concerns; scalability and deployment issues; and sub-optimal RoI, often from organizations having no clear AI strategy. Given that its adoption is driven by integration into existing tools, mostly in a co-pilot, chatbot, or assistive capacity, the 

problems that plague traditional enterprise SaaS will only exacerbate.

 We believe that the real improvement in enterprise productivity lies in the next chapter of cognitive automation. The days of bloated, overpriced enterprise software are numbered—thanks to Agentic AI. Applied to business processes, Agentic AI is creating a new category, one that disrupts everything from how technology operates in the enterprise to how value is realized from it, and how humans and technology work together.

Since we at Ema are close witnesses to this new category, serving 

 to automate their most complex business workflows, we have the unique opportunity to name, shape, and study this technological and societal leap.Welcome to 

 — where Agentic AI executes complex business workflows across the enterprise, through a conversational, adaptive layer of intelligent, collaborative AI agents, who iterate and improve on their own output, work with each other and human colleagues, and access the right tools and systems in a secure, efficient fashion to complete tasks end-to-end and achieve tangible outcomes in line with strategic business goals. A revolution in enterprise productivity is underway.

What is Agentic Business Automation (ABA)?

 is the next chapter in enterprise automation, where Agentic AI is embedded into everyday business processes. ABA involves autonomous, goal-driven software entities owning and executing workflows end-to-end, via a conversational, adaptive, intelligent mesh of AI agents, who work together and with human colleagues to execute tasks.

 to produce accurate and specific results, solving for 

common issues created by Gen AI in the enterprise

, especially inaccuracy and hallucinations. These usually stem from a lack of high-quality training data and models that do not understand enterprise context, the latter of which ABA solves for. ABA can also access other tools, internal or external, relevant systems, applications, databases, and APIs, in a secure and efficient manner so as to produce the best results at the lowest costs.

Unlike traditional Robotic Process Automation (RPA), which relies on strict, rule-based instructions, or Gen AI copilots, which remain assistive rather than autonomous and strategic, 

 provides a more cognitive, holistic automation that acts across the enterprise and has continuous improvement baked into its design. Here are the key features that enable this, distinguishing ABA from everything that’s come before:

Generating and executing workflows end-to-end: 

Instead of adhering to simple but brittle, rule-based scripts, ABA can spawn and execute its own complex, multi-step workflows to solve problems. For example, ABA in supply chain wouldn’t be limited to reordering stock at certain pre-set triggers; it would generate, execute, and iterate over workflows that span forecasting, procurement, and logistics.

ABA can act independently by planning, executing, and adjusting workflows in line with strategic business goals, specific enterprise contexts, and evolving external conditions. This autonomy and real-time adaptation are fundamental shifts from previous technologies, as ABA can make decisions and shift gears, reducing the need for constant human supervision.

Self-improvement through iteration and feedback: 

ABA’s capacity to learn from feedback and real-time data, generated by each input-output cycle, enables it to get better with time, just as a human would. A customer support ABA would not only use historical data on what’s worked and what hasn’t, but also real-time sentiment analysis, and continuously update itself to provide better answers.

Unlike prior automation embedded in data silos and complexity, ABA can access the right tools and systems securely, working with other agents and humans to refine its output. An HR ABA would involve multiple HR agents synchronizing data across ERP and CRM, working with each other to manage overall HR goals, creating a connected, intelligent automation experience.

ABA in the Enterprise: Transformative Impact and ROI

Because of its capacity for autonomy, execution, iteration, and tooling, ABA can provide a strategic yet hands-on form of automation across industries and functions.

, ABA can use a mesh of agents to continuously track stock levels (Inventory agent), forecast supply by studying historical as well as real-time data (Forecast agent), and route and ship inventory accordingly (Shipment agent). This entire agentic AI system could also work with a “human in the loop” for any critical or strategic decisions. According to 

, top supply chain organizations already 

 to optimize these processes at more than twice the rate of low-performing peers.

 across auditing, compliance, fraud detection, and financial analysis and forecasting. These are usually dependent on rule-based systems that only handle static data and conditions, with humans coordinating across them. But 

 estimates that 90% of finance functions will deploy at least one AI-enabled solution by the year 2026, while only 10% will see headcount reductions. This indicates the potential of cognitive automation to correct for weaknesses and repetition in how humans work, while aiding their strengths in higher-order problem-solving.

 ABA can handle the entire workflow from claim verification and validation to approval and disbursement. Instead of a human coordinating across these tasks, ABA will orchestrate document processing, data analysis, fraud detection, and final approval, autonomously verifying claims and speeding up the process for better customer satisfaction.

According to Mike Gualtieri, Principal Analyst at 

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