KEY TAKEAWAYS:

• 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, productivity remained stagnant 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.

The "productivity J-curve" 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 expected to create USD$19.9 trillion in value for global GDP by 2030. But, as recent research has also found, 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.

But to truly capture the value of Agentic Business Automation (ABA), 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

As we wrote in 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.

Despite its ubiquity, 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.

Agentic Business Automation (ABA) 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 horizontal, integrated systems, where different functions can talk to each other and share information in a secure, collaborative, and adaptive fashion.

This allows ABA to generate insights and contextually relevant outputs based on past and real-time data, adapting to changing conditions and taking intelligent actions across the organization autonomously.

Data governance policies 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 NIST’s AI Risk Management Framework, 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. EmaFusion™, for instance, uses a proprietary mixture-of-experts model 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 super teams. The division of labor between humans and machines will continue to shift 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 usage- or outcome-based pricing models, 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 greater accountability from vendors. Contracts may thus move from rigid, narrow deliverables towards shared success metrics, 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 standard data protection regulations, 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:

• Improved decision-making: 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.
• Cross-functional collaboration: 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. The transformation must be led from the top, with leaders actively championing the strategic integration of AI at various levels of the organization, so that it is aligned with larger business needs. Iterative implementation will help, starting with targeted ABA deployments that expand as operations and teams grow more comfortable using intelligent software systems to achieve business goals.

Incorporate strong feedback loops 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 to 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.