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 

, “There’s a claims processing person who’s in charge of that process, [but] Agentic AI is where AI becomes in charge of that process. It’s full-on automation, and it’s not far-fetched for tens of thousands of business processes.”

, ABA’s stories of success already abound. Large companies like 

 use Ema’s Customer Support AI Employee to automate as much as 80% of ticket resolutions, even solving for multilingual contexts and cyclical volume fluctuations (eliminating the complexity of seasonal hires), driving brand trust and customer satisfaction for these enterprises.

Ema’s Customer Support AI Employee ingests relevant enterprise-specific context by integrating with existing systems and tools, such as Notion, Zendesk, Freshdesk, or whatever else is in place. Domain-understanding allows Ema to soon operate with the vocabulary of a seasoned technical support agent, while also categorizing tickets that should be handled by humans and proactively assisting them in faster resolution.

, what traditionally requires human beings and several tools can now potentially involve one ABA HR solution that autonomously screens resumes, conducts initial assessments, follows up with a personalized screening process for those likely to convert, onboards new employees, and tracks their career over time. The 

 for HR leaders in adopting AI has been integrating with existing systems. But because ABA can be custom-trained in enterprise contexts, it will better adapt to the organization’s goals and needs, without compromising accuracy and efficiency. ABA’s unique features and applications extend 

 with the potential to improve efficiency and ROI in every department and business workflow. It will lower operational costs, increase the accuracy of output, break down data silos, and free up humans to work on more strategic, higher-value tasks.

The Future of Enterprise Automation with ABA

Agentic Business Automation (ABA) is poised to disrupt how companies approach work, and what to expect from their software and employees alike. Instead of simply optimizing limited routine tasks, Agentic AI systems will be integrated in business workflows to handle complex functions autonomously and end-to-end.

You will thus have autonomous AI employees working hand-in-hand with humans, as ABA grows normal in enterprises. Entire job functions and economies will get restructured as the division of labor between people and machines 

, especially for repetitive tasks. Lower-order, rule-based work like data entry, once core to many positions, will become AI-driven, and more high-skilled labor will emerge and grow in demand. 

 of current work can be automated by 2030, predicting large 

Talent deriving from unique cognitive abilities, emotional and leadership skills will become more sought after. As humans and AI colleagues start to work together, organizations will relook at their internal structure and how work is allocated, creating systems for training and learning in an evolving workplace and world.

Several other key transitions await for companies embarking on this revolution in human productivity. To realize the full potential of ABA, their approach to technology will also have to change, with new questions that demand answers: How best can you source and procure ABA? How do you measure ABA’s ROI? How do you manage safety, compliance, and data security as ABA becomes the norm in enterprises? And how do you navigate the trade-offs between building custom solutions in-house, versus adopting more horizontal, conversational platforms like Ema, which already offer proven Agentic Business Automation as a Service (ABAaaS) to several customers? We are excited to answer these and other questions that impact enterprises and the future of work, as we shape and study the new category of Agentic Business Automation.

Introducing Agentic Business Automation (ABA): The Next Big Leap in Enterprise Productivity

Today, we at Ema are announcing a series of strategic partnerships that further fuel our growth and momentum. We are joining 

 and also forming collaborations with major global enterprises such as 

 is a two-year partnership for high-performing B2B startups that helps them accelerate growth with additional support in uncovering leads and GTM. 

Microsoft channels and customers will gain direct access to Ema's suite of AI employees designed for enterprise needs as part of the collaboration.

 Ema will also benefit from Microsoft's extensive resources, including co-selling opportunities and advanced technological support.

Our participation in the Pegasus program, along with our partnerships with leading enterprises, represents a crucial step in our rapid growth since emerging from stealth in March. By integrating our agents within our partners’ ecosystems and aligning with industry giants to provide tailored solutions, we're expanding the potential of AI-driven productivity while enabling teams to focus on strategic and high-value work.

Further, our momentum is set to grow through our alliances with 

Hitachi Digital Services, ISG, NDI, and Wipro 

to co-sell Ema’s agentic AI. Many of these partners will offer bespoke AI employees tailored specifically for their clients using Ema’s innovative platform—a unique differentiator that sets them apart from other agentic AI offerings. These partnerships will leverage Ema’s Universal AI Employee to execute tasks across various functions such as customer support, employee support, sales enablement, compliance, and revenue operations.

About Wipro's partnership with Ema, a portfolio company of Wipro Ventures, Anup Purohit, the Chief Information Officer at Wipro Limited, said, “At Wipro, collaborating with industry partners is central to our innovation strategy. We are harnessing AI in every aspect of our business, both to drive internal transformation and in client solutions to become an AI-powered Wipro. Through our partnership with Ema, we have created AI employees for our internal stakeholders in HR, Finance, Procurement, and Global Operations. Together, we are redefining the potential of AI to elevate business performance.”

These strategic partnerships our significant milestones for us, and solidify our position as a front-runner in the Agentic AI space. As Ema continues to push the boundaries of what is possible with AI in the enterprise sector, these collaborations will pave the way for the widespread adoption of intelligent, task-specific AI agents that promise to revolutionize workplace efficiency and creativity.

Ema Accelerates Growth Via Strategic Partnerships with Microsoft, Wipro, Hitachi Digital Services, ISG, and NDI

GenAI has improved enterprise automation, but it remains assistive, not autonomous. Chatbots and co-pilots can increase productivity in specific tasks, but they require human oversight and still struggle with inaccuracies, limiting impact.

Agentic AI steps beyond GenAI by fully executing workflows. Unlike GenAI, agents are goal-driven and capable of adapting, learning, iterating, collaborating with other systems and humans, and completing tasks end-to-end. 

The future of enterprise AI is a collaborative Agentic AI systems, promising a more transformative leap than Gen AI, which has added incremental value so far. As AI agency evolves, Agentic AI will rehaul current workflows and redefine how humans and technology work together.

, at the Highland Park factory in Michigan, USA, Henry Ford introduced one of the world’s first moving assembly lines to produce the Ford Model T. Instead of workers putting together entire cars by hand, the process had now been broken down into 

. Workers stood in line as a moving conveyor belt brought car parts to them, and this dramatically sped up production as everyone performed their specialized tasks over and over.

What was taking more than 12 hours could now be done in 93 minutes. The production cost became low enough that the car could be sold to America’s middle-class, not just its elites. The Ford assembly line revolutionized not just the automobile industry, but also how work would be done by humans and technology forever.

In every industry from manufacturing to services, work is now split into specialized, repeatable functions—such as production, finance, marketing, and sales. Only now, instead of a conveyor belt, the technology that assists humans has evolved. Employees use various SaaS tools to execute their tasks, but human coordination remains necessary. Tasks have manual and digital components, with humans still at the center, coordinating across workflows, planning ahead, making decisions.

, despite rapid software advancements. Workers spend hours switching between systems, searching for the right data, and navigating multiple data silos. My decade in the world of Robotic Process Automation (RPA) revealed how automation could make a significant difference to productivity—but the scripted and brittle nature of RPA also became a constraint. Our hope was that more cognitive technologies would provide the gains of automation while also being fluid and intelligent.

That said, even with GenAI, while productivity seems to have improved within specific workflows, enterprises still resemble the early assembly line in their fragmented, vertically segregated operations. Human beings are managing layers of ‘digital conveyor belts’ across them.

It is the next evolution of AI—Agentic AI—that can truly step-change the technological component of modern work. Autonomous, collaborative AI agents will not just assist human employees, but actually complete workflows across the enterprise, from decision-making through execution. And this will be as transformative for white-collar work as the assembly line was for industrial production, reshaping how businesses operate and work gets done.

GenAI: Two Years On, Its Impact and Limitations

ChatGPT and the slew of GenAI products that followed it through late 2022 grabbed our collective attention and imagination. Millions of people were suddenly using an interactive technology that could understand and speak just like them, producing outputs that were thought to be intrinsically human, such as code, writing, images, videos, even 

This watershed moment in the history of technology led to adoption and innovation in both consumer and enterprise sectors. In a 

, two-thirds of over 1,300 respondents reported that their organizations were regularly using GenAI, nearly double the percentage from just ten months before. With GenAI estimated to add 

 to the global economy, corporate leaders are eager to capture this value.

But two years on from ChatGPT’s “wow moment”, rising adoption of GenAI has also led to the understanding that it can bring along with significant risks. Organizations are still early in their GenAI journeys, but they have first-hand learnings on what works and what doesn’t now, separate from the general hype around the technology.

 from deploying GenAI within customer support, legal, and sales and marketing use-cases, via both cost reduction and increased revenue, but research also highlights the many challenges that have come along with it:

 by McKinsey, Deloitte, Asana, and Anthropic is that inaccurate outputs from AI are a major concern for enterprises. There is a need for high-quality training data and for customized models, leading to an 

 on data governance as well as solutions that can be tailored to the enterprise. Hallucinations are occasionally tolerable within internal use-cases, but with customer-facing applications, they stand to risk reputation, trust, and revenue.

 were unable to get most of their GenAI experiments into production, indicating that deployment and scalability issues plague these projects. Most planned AI investments are stalled by delays, as integrating AI within existing systems, without a disruption to current operations, is a challenge.

 of more than 2770 enterprise leaders found that 40% of them were struggling to define and measure the impact of their GenAI initiatives. Although most were seeking productivity gains, less than half were actually tracking changes along relevant metrics. 

 do not have a formal AI strategy in place, leading to misalignment between business goals and AI projects, which in turn can create sub-optimal RoI.

, businesses are dealing with concerns about data privacy, cybersecurity, explainability, and the impact of AI on organization structure and culture. Given how much there is to navigate with a technology evolving as rapidly as AI, we believe the issues with GenAI will not stop at the above.

The Real Gap: GenAI Doesn’t Do Work. Agentic AI does.

If the modern enterprise needs to undergo a major overhaul in order to incorporate a new but promising technology, it might as well evolve in the best possible way, so as to result in tangible RoI, aligned with strategic business goals.

And that’s where GenAI stops short, and Agentic AI goes long. While co-pilots and chatbots paved the way in generating text, images, and code, their role remains largely assistive to humans. They perform tasks at the “prompt” of a human, and their outputs are within the realm of existing vertical functions, be it customer support or sales, relying on human oversight for improvement, coordination, and completion.

The real transformative leap for businesses comes with Agentic AI, which is autonomous and can own and execute workflows end-to-end. AI agents are goal-driven software entities that can reflect and iterate on their own outputs, use external tooling to generate better results, identify goals and plan ahead, and also collaborate with other agents to execute complex workflows from start to finish. A caveat is that most early enterprise AI experiments are built off of 

, leading to hallucinations and data privacy concerns. But 

 such as Ema custom-train models on enterprise data, performing queries with the highest accuracy and at the lowest costs, while tackling security and data privacy via top-tier encryption and data governance policies.

According to a recent Garter webinar, Agentic AI has been gaining momentum through 2024, and is capable of delivering transformational value to businesses. Unlike GenAI, which has been delivering incremental value so far, Agentic AI systems promise to automate the work of entire roles, be it business analysts or quality assurance specialists or marketing. Indeed, the 

 between GenAI and Agentic AI is the latter’s ability to tackle complexity and multiplicity in the nature of tasks, learn from its own outputs, connect to other tools, plan ahead, and collaborate with other agents and also humans.

, Gartner estimates that 33% of enterprise software will include Agentic AI, up from less than 1% today. These agentic systems will autonomously handle 15% of day-to-day work and decision-making, offering a real boost in efficiency for businesses.

But the promise of Agentic AI doesn’t stop with individual AI agents. Systems of AI agents will function best together as an intelligent, adaptive ecosystem, breaking down larger business goals into smaller problems and sub-tasks, executing and optimizing workflows end-to-end.

Just as building a birdhouse requires the intelligent application of multiple tools—not just a hammer, but also nails, wood, and glue applied towards a visual plan of the birdhouse—effective automation in the enterprise demands multiple agents specializing in different parts of large and complicated workflows, aiding and collaborating with each other towards bigger goals.

To unlock this transformative potential, businesses must first understand their internal processes and data. AI agents are waiting to be trained and deployed, but their success depends on the clarity of work assigned to them, and the quality of the data they’re trained on. Enterprises need to define their business goals, codify their current workflows, and create data readiness and systems where collections of agents can work and drive business value together.

The Future of Agentic AI in the Enterprise

Given the pressures to stay AI-forward, leaders must 

 the trap of adopting tech for tech’s sake. The right strategy is to start by identifying pressing business goals and challenges, and working backwards from there to see how tech, and AI, can help.

A “tiger team” of AI agents can be applied towards meaningful, measurable business strategies and outcomes. But this does not necessarily mean jettisoning the technologies in use before. For example, Intelligent Document Processing (IDP) may still read data from documents, Robotic Process Automation (RPA) may access data and trigger next steps, while an Agentic AI layer on top can use these outputs for another workflow, while also evaluating the quality of their outputs, and ensuring that they are in line with larger strategic goals and decisions. A “human-in-the-loop” control mechanism should be deployed, where human specialists validate outputs for accuracy, brand goals, and compliance, creating a flywheel for the ongoing improvement of Agentic AI systems.

Furthermore, there are benefits to thinking of agents, or a mesh of agents, as adding more workers to the organization. These agents will adapt, learn, and scale, meaning business processes will have to be rearranged as AI takes on tasks that previously required more manual coordination across departments. The shift from human-led decision-making to AI-driven execution will streamline operations and change entire job functions. But they will also create new efficiencies and possibilities.

The technology is still evolving, and AI agency lies on a spectrum. On the one end are LLM- or GenAI-based assistants that perform specific tasks under defined conditions. At the other end are Agentic AI systems that learn from their environment, can make decisions, collaborate with others, and perform tasks independently.

As we move towards the latter end of the spectrum, enterprises will have to mitigate new challenges, just as adding new workers and restructuring the organization would involve. But one thing is for sure: Just as Ford atomized work down to speed up production, Agentic AI will unleash new innovations at both the task-level and in how work gets allocated. Technology will no longer simply assist humans, as it has for more than a century. Instead, automation and humans will together form a large, self-sustaining workforce — one that makes optimal decisions, drives productivity, and aligns with meaningful, strategic goals.



Ian Barkin is a seasoned innovator in digital operations and intelligent automation, and serves as an advisor to several companies in the field, including Ema.

Gen AI vs Agentic AI: From Assistance to Autonomy

SaaS revolutionized enterprise software with easy deployment and scalability, but its inefficiencies—rising shelfware, data silos, and per-seat pricing—are now major pain points for businesses.

Agentic AI solves for SaaS’ core limitations, eliminating data silos and automating workflows end-to-end, making software more intelligent, autonomous and outcome-driven, rather than just assisting humans.

Pricing models will evolve as Agentic AI adoption increases, with SaaS’ seat-based model being replaced by usage- or outcome-based pricing, resulting in significant cost savings and improved RoI for enterprises.

” captured the public imagination. In it, venture capitalist and inventor of the first web browser, Marc Andressen, argued that software would ‘eat’ into every part of the economy, transforming infra-heavy industries like retail, telecom, and entertainment, with ease of access and instant delivery.

For the enterprise world, that transformation was led by SaaS, or Software as a Service. SaaS moved software to the cloud, becoming a revolutionary alternative to cumbersome on-premise solutions. It did away with lengthy installations and the need for ongoing maintenance, enabling ‘one-click’ deployments, on-cloud collaboration, and scalability.

But fast forward to 2024, and what we’re seeing is more complicated. Businesses are grappling with bloated SaaS systems, with a 

 reporting that 42% of IT professionals believe their most crucial challenge is finding unused or underutilized SaaS licenses within their business. About a third believed that 20-39% of their SaaS spending was wasted.

How did we get here? And as SaaS companies rush to integrate GenAI into their solutions, what does the future of enterprise software look like?

The landscape is shifting again. But instead of more co-pilots and chatbots within SaaS, the future lies in Agentic AI. This new technology stretches what we think software is capable of, by actually “doing the work”—executing workflows end-to-end, optimizing tasks, and delivering business outcomes—instead of just being tools that aid (and sometimes complicate) human work.

And as this new paradigm emerges, traditional SaaS stands to become obsolete in comparison to outcome-driven, autonomous AI agents.

There’s no doubt that SaaS revolutionized enterprise productivity: It liberated IT teams from tedious on-prem installations, security patches, and complex maintenance work. Enterprises could now deploy and scale their software easily, by enabling access for each ‘seat’ or user who needed it, with asynchronous collaboration between teams and functions via the cloud. As of 2023, SaaS was 

 at USD$273 billion, with businesses running an average of 

This market is estimated to grow to USD$1.32 trillion by 2032. SaaS has gradually become the 

 cost center for many companies, right after staff and office costs. A significant proportion of these purchases fall under 

 costs (i.e. outside official IT governance processes). The very features that made SaaS successful, like on-demand installation and per-seat scalability, have resulted in a “SaaS sprawl”, with businesses struggling to map their SaaS spends and the RoI from it.

 the overspend on software through Covid-19, businesses are 

 to downsize and ensure tangible RoI, with some companies cutting software budgets by 30%. There is real fatigue—the 

 cost per employee on SaaS tools is $3500 per year—causing internal scrutiny on underused seats and licenses. Where there should be automation, there now seems to be excess.

The other big problem with the current state of enterprise SaaS is that of 

. Businesses rely on SaaS applications to manage core processes, but many of those applications don’t integrate with each other. For example, marketing and sales may function on overlapping sets of data, but because they are stored in separate applications, this results in complications tracking down the right data, dealing with duplicate or inconsistent records, and a loss of efficiency. The cost of this coordination overhead, according to a 2022 Forrester 

, is that knowledge workers are spending an average of 12 hours per week “chasing data”, which could be better spent on more value-added tasks.

Furthermore, because SaaS apps exist in fragmented environments, the organization is unable to make use of all its intelligence on any given customer, across departments and data sets, and accordingly act on relevant information. And the addition of GenAI co-pilots and chatbots within SaaS won’t necessarily solve these issues, because the data silos will still exist, and human employees will have to coordinate workflows across them.

SaaS sprawls, but the fundamental problem remains: that of actually simplifying work in order to deliver tangible business outcomes.

The Next Gen of Enterprise Software: Agentic AI

Agentic AI addresses these core limitations of traditional SaaS. Autonomous 

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