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 

 can execute complex workflows end-to-end, and they’re capable of reasoning, planning, iterating on their own outputs, and getting better with feedback and time — like humans.

Unlike fragmented SaaS tools that require manual coordination and exist in silos, 

 can orchestrate tasks across a function to deliver actual output. Let’s take a common HR workflow, for example. With traditional SaaS, an employee onboarding would involve multiple steps and tools: enabling access to the right systems and accounts, another for sending mandatory emails and documents, a third for initiating payroll, etcetera. These tools exist in silos, and HR teams manually coordinate tasks and data across them.

But with Agentic AI, the process becomes more seamless. An agentic HR system would automatically retrieve candidate information, set up employee accounts across systems (communication, payroll, benefits), and guide the new employee through each step of the onboarding process, without manual intervention. It might then check in on the employee periodically, track their performance over time, and also learn to interact with other agents handling other aspects of the HR function.

By eliminating the need for multiple separate SaaS tools which are handled manually today, Agentic AI systems can “do” workflows across the organization holistically.

But here’s the even more transformative implication of Agentic AI: there’s no need for the traditional seat-based pricing anymore. SaaS grew because of a pricing structure where companies paid for each user or license to access the software, rather than a lump sum amount as was prevalent pre-SaaS. While it seemed like SaaS had solved the software pricing problem by moving to a more usage-based model, it turned out that most users don’t actually use the software with the same intensity. So enterprises are stuck with a massive number of seat-based licenses, many of which are rarely used.

But with Agentic AI, enterprises can start paying for what the software actually delivers. For example, in the HR onboarding case, instead of paying for multiple SaaS tools, you may only be charged for onboarding workflows actually executed, or the outcome delivered, such as the number of employees onboarded. This can drastically reduce wasted enterprise costs on SaaS. And it would align software expenditure very closely with actual business value.

With such usage- or outcome-based pricing, companies don’t have to worry about paying for idle licenses across multiple SaaS apps. Instead, they can pay for the work that agentic AI systems complete—whether it’s onboarding employees, resolving a certain number of tickets, or generating and closing sales leads, or all of them.

Agentic AI is thus going to usher in a new business model in enterprise software, which will ensure that businesses get RoI from their software investments, as the cost is tied directly to the AI’s contribution to measurable business goals.

The Challenge: SaaS’ Innovator’s Dilemma

Now, this raises a fascinating line of inquiry: What kind of outcomes should be tracked, and how should they be priced? How will enterprise software be developed and sold in the new landscape? How should the modern enterprise evolve to unleash the full potential of Agentic AI?

And while answers to these will follow, one thing is for certain for now: Traditional SaaS is on its way out. In his bestselling 1997 book called The Innovator’s Dilemma, Harvard professor Clayton Christensen wrote about why many well-established companies struggle to embrace disruptive innovations. Incumbents are so focussed on maintaining existing revenue streams, derived from their core services and business model, that they fail to adapt to new technologies and business models — because it would cannibalize their current profits.

And in the case of SaaS companies, the dilemma is sharp: Shifting to Agentic AI would move SaaS away from per-seat pricing, a model that has been incredibly lucrative for them for decades. Already, we’re seeing companies 

 some form of usage-based pricing, or a hybrid of per-seat and usage- or outcome-based pricing, in order to not be left behind. But even here, the AI on offer is often an “add-on”, or for a vertical use-case.

We at Ema imagine a very different future for the enterprise stack, in which multiple autonomous AI agents flourish as an integrated part of the enterprise, working together intelligently, like the organization’s horizontal operating system. This helps cut down the software sprawl and data silos we’ve seen in the current SaaS era. In this new enterprise of the future, outcome-based pricing will be aligned with actual business needs. And for traditional SaaS, the transition isn’t just about adding new technological capabilities. It is also about 

 and strategy, which attacks current revenue. SaaS will now be forced to charge for actual value provided, not just for access to software. And Agentic AI is the disruption behind this, which could leave them lagging behind more agile, horizontal, AI-first competitors.

As the limitations of SaaS become more apparent—through excessive costs, coordination complexity, and data silos—the rise of Agentic AI marks the next generation of enterprise software. Companies need no longer settle for seat-based, fragmented tools that have ended up creating more complexity than they solve.

Agentic AI is the new frontier: one where automation works end-to-end, across functions, and where businesses only pay for real usage or outcomes delivered. Enterprises that recognize this will see clear rewards—greater efficiency, tangible RoI, and a new era of intelligent, autonomous automation.

Why SaaS is Dead and the Future is Agentic

When designing Ema's agentic OS, we faced a fundamental challenge: How do we create an AI system that thinks and acts like a human and works collaboratively with other human employees? Traditional workflow engines couldn't meet our ambitious goals, so we developed the Generative Workflow Engine™ (GWE).

Just as the prefrontal cortex serves as the brain's executive center, orchestrating complex cognitive functions, the Generative Workflow Engine™ (GWE) acts as the central command of Ema's agentic operating system. In this role, GWE functions as a sophisticated planner, executor, monitor, and optimizer, coordinating the activities of specialized agents much like how the prefrontal cortex manages various brain regions. This parallel structure allows for high-level decision-making, adaptability, and continuous improvement, mirroring the human brain's remarkable ability to tackle complex challenges and learn from experience. It coordinates various AI agents, each with unique expertise, to form an intelligent agent mesh capable of tackling complex tasks.

This revolutionary approach goes beyond the simple task automation of conventional workflow engines, which have been used in enterprise applications for decades. Unlike conventional workflow engines, GWE leverages built-in small language models to generate and orchestrate workflows with runtime context awareness and intelligence. By orchestrating these diverse components, GWE empowers Ema to navigate a wide range of scenarios across various industries, from customer service to internal productivity applications, with unprecedented efficiency and accuracy.

GWE operates in two phases: build time and run time. At build time, GWE takes AI Employee information and creates the agent mesh. At run time, it orchestrates the agent mesh to execute the tasks of the AI Employee. Its primary responsibilities include:

Given a high-level goal, GWE generates an appropriate agentic workflow to tackle the problem.

: Identify and select the most suitable agents for specific tasks in the workflow.

: Establish interconnections between agents and provide initial configuration instructions.

: Customize the collective intelligence of the agent network by incorporating and learning from customer-specific data and patterns.

The resulting agent mesh serves as the dynamic, runtime incarnation of the AI Employee, bringing its capabilities to life in real-time. Interestingly, the build process for an agent mesh in GWE closely parallels human organizational processes:

 Begin with a high-level goal and decide the capabilities of the underlying agent mesh, akin to a manager creating a detailed job description.

 GWE generates an agent mesh based on this description, mirroring the recruitment of a new employee.

 Setting up the agent mesh is akin to configuring an employee's access to various systems and resources.

 The process of training the agent mesh corresponds to the training and skill development of human employees.

: Coordinate the activities of multiple agents in real-time, ensuring seamless collaboration and efficient task execution.

Evaluate quality and performance of the agent mesh and agents

: Continuously assess the effectiveness of both individual agents and the overall network, identifying areas for improvement.

: Provide feedback and guidance to individual agents, enhancing their performance and adapting their behaviors based on observed outcomes.

GWE offers multiple approaches to create and refine the Agent Mesh, catering to different user preferences and levels of technical expertise:

: Similar to describing a job to a hiring manager, users can outline the desired functionality conversationally. GWE interprets this natural language input to generate an appropriate agent mesh.

: For those who prefer visual control, GWE provides a graphical interface to visualize the structure of the agentic mesh. This allows precise placement of agents and definition of their communication pathways, offering a hands-on approach to mesh design.

: Advanced users can edit the JSON configuration file directly, providing the highest level of control over the mesh structure and agent properties.

: Importantly, these methods are not mutually exclusive. Users can start with one approach and seamlessly switch between methods as needed. This flexibility allows for iterative refinement, combining the intuitive nature of conversational input with the precision of GUI or direct configuration.

GWE acts as a sophisticated planner, creating a workflow to achieve user-defined goals by coordinating various AI agents into an intelligent mesh. While GWE guides this process, human approval is required to finalize the agent mesh, ensuring alignment with intended goals and specific requirements.

Think of agents as specialized individuals within Ema's ecosystem, each possessing unique expertise. These agents have access to a wide array of knowledge, skills, reasoning abilities and actions that allow them to interact with various applications effectively. While these capabilities work in harmony, each has its own unique characteristics and degrees of flexibility.

Focused and specialized, not general purpose

In the field of artificial intelligence, it's tempting to build all-in-one agents, but our Generative Workflow Engine™ focuses on specialization. Our agents are designed to be experts in specific tasks, not generalists. Our approach of using specialized agents offers several key advantages:

: Each agent maintains a high level of competence in its designated area, ensuring tasks are handled with maximum efficiency.

: Specialized agents make it easier to trace and understand the decision-making process, enhancing the system's transparency.

: Agents can easily be used for similar tasks by different AI employees, increasing overall system efficiency.

: The system can be expanded by introducing new specialized agents as needed, without disrupting existing functionalities.

: Specific functionalities can be updated or enhanced without affecting the entire system.

This specialized approach mirrors human organizations, where teams of experts collaborate on complex tasks. Our research has shown that this structure outperforms a single, monolithic entity in handling multifaceted challenges, resulting in improved overall effectiveness, and enhanced reliability of the agentic mesh.

Here are some examples of agents in Ema’s agent library:

 Expertly extracts and interprets information from various document types.

 Efficiently gathers and synthesizes information from diverse sources.

 Given a set of rules and content, it validates whether the rules are met or not.

 Creates original content, based on given parameters.

 Specializes in writing, debugging, and optimizing code across different programming languages.

 Crafts appropriate and context-aware responses in conversational scenarios.

 Execute action in a given system of record, for example, “close the ticket in Zendesk” or “send an email to prospective customer”.

: Reviews tasks, such as validating data analysis results or cross-checking complex reports.

Our agents are capable of understanding enterprise knowledge bases, acquiring new skills and taking actions in the enterprise.

Agents can quickly retrieve relevant information from our knowledge bases, enabling them to provide responses and take actions that are grounded in accurate enterprise data. This capability helps eliminate the risk of hallucinations, ensuring that the information shared with users is reliable and trustworthy.

Our agents are equipped with a variety of essential skills, including intent detection, synthesis, summarization, reasoning, language understanding, and presentation. These skills are powered by 

 - our proprietary mixture of experts model combining the reasoning power of 2T parameters over 100+ LLMs. Agents have access to all the skills and they can adapt their skill usage based on the context of the task, the user's preferences, or the specific requirements of a given scenario, ensuring optimal performance across different situations.

When needed, agents can perform specific actions within our systems, such as invoking APIs, triggering processes, and interacting with external systems. This ability to execute actions empowers our agents to complete tasks efficiently and deliver comprehensive service to users.

Every Ema agent has reasoning ability powered by 

. These reasoning skills function as an agent's brain, providing situational intelligence and enabling them to:

Process information and decide on actions needed to achieve their objective using a chain of thought reasoning.

Request assistance from the Generative Workflow Engine™ (GWE) when needed.

Seek human intervention in appropriate situations - ensuring sensitive decisions receive appropriate oversight.

Agents within a mesh communicate with each other through both conversational and programmatic means. This dual-mode communication system offers several key advantages:

Human-like problem-solving and reasoning:

 The conversational aspect allows agents to engage in dialogue-style problem-solving, mimicking human thought processes and decision-making patterns.

 Natural language communication makes it very easy for GWE to monitor the interactions between agents and change agent instructions and prompts.

Agents can adapt their communication style based on the task at hand, switching seamlessly between natural language interactions and structured data exchanges.

Enhanced observability and explainability:

 The transparency of agent interactions, particularly in conversational mode, makes it remarkably easy to trace the flow of information and decision-making processes within the agent mesh.

 Programmatic communication enables rapid and precise exchange of complex data structures when needed, enhancing overall system performance.

The Generative Workflow Engine™ (GWE) is designed with advanced capabilities that allow it to learn, adapt, and optimize its performance over time. Here are three key features that make GWE a powerful and evolving system:

GWE maintains a sophisticated long-term memory. This LTM encompasses the context of the GWE, its agents, configurations, and instructions received from humans. By synthesizing and storing this information, GWE can apply learned patterns to future tasks. For example, it might remember to always use formal language in responses or to avoid sending requests during weekends, based on previous instructions.

Similar to a new employee learning on the job, GWE has built-in capabilities to receive and learn from human feedback. Using Reinforcement Learning from Human Feedback (RLHF), GWE can adjust its behavior, tweak ML models, and update its LTM. This feedback is then passed to the right agents in the mesh, allowing each to improve their specific tasks. Unlike human employees who may take weeks or months to learn, the agent mesh can adapt in minutes to hours, significantly accelerating the learning process.

GWE doesn't just learn; it actively optimizes its performance. By monitoring inter-agent interactions and incorporating human feedback, GWE can suggest improvements to both individual agents and the overall mesh structure. This might involve adjusting agent instructions, such as prioritizing certain knowledge bases or employing more robust evaluation mechanisms. GWE can also recommend structural changes to the agent mesh, like adding new agents or reconfiguring the mesh flow for better accuracy. This ongoing optimization process, guided by GWE and overseen by humans, ensures that the system continually evolves to meet changing requirements and improve its performance in any given task.

Through these capabilities, GWE creates a dynamic, adaptive system that becomes more efficient and effective over time, always striving to deliver optimal results.

Bringing it all together: Ema’s AI Employees

Here is an example of an agent mesh for an AI Employee writing Business Proposals:

Business proposal writing teams take an RFP (Request for Proposal), understand the requirements and write-up a completed proposal. These documents are often 200+ pages long and need hundreds of human hours scavenging through thousands of other documents to synthesize the right content.

Ema’s Business Proposal Writer AI Employee can generate responses to a new RFP in just minutes saving enterprises thousands of person-hours and enabling them to grow their topline without growing headcount. While the actual product for Business Proposal Writer uses a far more complex Agent Mesh, we illustrate with a simplified example below:

A document parser extracts key sections: Introduction, Scope of Work, Vendor Requirements, Evaluation Criteria, and Submission Guidelines.

Rule extractor generates initial rules, such as vendor experience, project timeline, and budget ceiling, calling for human input on cybersecurity compliance.

Researcher agent gathers data on previous RFPs, best practices, and market rates, ensuring relevance and reliability.

Writer agent drafts the RFP, ensuring consistency and identifying gaps.

Rule validator reviews for accuracy and requests human validation on unclear budget details.

Writer revises sections based on feedback.

Responder agent finalizes the document, incorporating revisions and ensuring formatting consistency.

Action executor sends the final document to stakeholders for approval.

Generative Workflow Engine™: Building Ema’s Brain

Polyglots and polymaths have been around since time immemorial - humans with an extraordinary ability to communicate fluently in multiple languages and drawn upon wide knowledge and learning. However, there have always been limitations to their capabilities simply because of the boundaries of human ability to learn and capacity to retain. Agentic AI, however, unlocks agents with an indefatigable hunger and ability to learn new languages, tools and applications.

Thus, this new age of AI brings with it the concept of an omniglot agent. Omni- means "all" and -glot refers to languages, tools, and applications. An "omniglot" is an entity who speaks all languages, possesses multiple skills, and has deep knowledge of various domains, along with the ability to utilize many tools.

At Ema, we are building an Agentic AI that embodies the essence of an omniglot, seamlessly navigating the complexities of enterprise data, applications and complex workflows while facilitating effective communication across diverse systems and stakeholders. The ability to work across systems and tasks make an omniglot agent an invaluable asset to any organization. We refer to our Omniglot agent as a AI Employee, representing its multifaceted capabilities.

An omniglot agent would have the ability to process data from all types of data sources - databases, spreadsheets, or cloud services. The agent can analyze data and identify intricate patterns, providing valuable insights that help organizations make informed decisions. Ema's data and knowledge agents are able to connect with all data sources including enterprise data and web data. We continually expand our range of compatible data sources to enhance Ema's capabilities.

Ability to take actions across all enterprise applications

An omniglot agent can take actions across all applications, regardless of whether they have APIs (Application Programming Interfaces) or not. This flexibility allows it to integrate seamlessly into existing workflows, ensuring that it can work with the tools and systems that businesses already use. Some of these enterprise workflows can be quite complex. Ema's action agents are able to connect with all types of applications and workflow systems. We continually expand our range of supported applications to enhance Ema's capabilities.

An omniglot can speak all languages, whether they are human languages or computer programming languages. This means that it can understand and communicate effectively with people from different functions within an enterprise as well as interact with various coding languages used in software development. Ema's agents fluently process and generate both natural languages and computer code.

Omniglots operate effectively in multiple business verticals, including sales, support, marketing, employee experience, human resources, legal, pharmacy, insurance and financial services. Their adaptability allows them to cater to the specific needs of different sectors, providing a versatile solution for various business challenges. Ema can operate across all the verticals and also adapt to multiple roles within a given vertical.

Omniglot agents can operate in all environments, including SaaS, or in the customer's cloud. This architectural flexibility allows them to adapt seamlessly to various operational contexts, ensuring that they can deliver their capabilities effectively, regardless of the infrastructure in place.

What empowers Ema’s Agentic AI to be an Omniglot?

At Ema, we innovate on our Agentic AI platform to deliver an omniglot AI agent that allows enterprises to streamline their workflows. Our proprietary technology allows us to infuse Ema with omniglot-like capabilities. Primarily, Ema’s technical systems operate at two levels:

 Ema’s Generative Workflow Engine™ is like the prefrontal cortex of Ema. It leverages a small transformer model to translate conversations into workflow graphs. It accepts high-level, natural language goals from users and formulates granular plans orchestrating a mesh of 

 to act seamlessly in tandem to optimally achieve those goals. We refer to the team of AI agents working together for a specific goal as an Ema AI Employee.

Once the plan is set into motion, Ema’s AI agent library springs into motion. Agents use their repository of knowledge, skills and actions to achieve their respective objectives. Here, our proprietary technology provides almost omniglot-like capabilities to our agents:

 Agents are able to build knowledge graphs by synthesizing information from diverse data sources. Through combining data from various domains and formats, agents build a comprehensive knowledge base that informs their decision-making and responses. This rich understanding of enterprise data enables them to provide accurate insights and recommendations tailored to specific business needs.

, our proprietary 2T+ parameter mixture of experts model. By intelligently combining insights from 100+ language models, our agents can reason and generate human-like responses, making their interactions more natural and effective as well as minimizing hallucinations. This empowers Ema’s agents to tackle complex queries and provide highly accurate responses at the lowest possible cost, enhancing their overall performance and effectiveness.

 Our agents seamlessly integrate with various software systems and are adept in tool use. These agents are empowered to call APIs, initiate RPA workflows, automate tasks, and facilitate communication between different applications.

In summary, Ema is powered by a mesh of AI agents that are orchestrated through our proprietary Generative Workflow Engine™ operating on its strategy to achieve a high-level goal. Each of the AI agents are supercharged with an ability to combine diverse data sources into a knowledge graph, leverage the power of the 100+ LLMs (through our EmaFusion™ model) in formulating their response, and communicate with a variety of enterprise applications to perform their objective.

How can Ema’s Omniglot Agentic AI Help your Enterprise?

The flexibility of Ema’s Agentic AI enables value creation in every potential function for a myriad of use-cases. Ema’s platform allows creation of customized, multi-agent AI employees to revolutionize your workforce.

Our enterprise customers are already deriving value from our platform across a wide range of functions including 

 across both multi-cloud and on-prem deployments.

With our focus on robust security and compliance including SOC 2, ISO 27001, HIPAA, NIST & GDPR, Ema offers the most performant and trusted Agentic AI platform to deliver value to your enterprise.

 Ema to transform your enterprise and discover what you can achieve.

Explore Ema's Agentic AI Platform, your versatile AI solution for customer support. Ema excels in engaging interactions, informed decision-making, and seamless workflow automation.

Ema's Agentic AI

Rise of the Omniglot: Agentic AI to Tackle Enterprise Automation


First, we’ve no “manager only” role in the company.

We expect every manager to also play an IC role. We don’t have any ‘manager only’ position in Ema, this includes senior leadership as well. Our managers spend a percentage of their time going deep and building/creating themselves. For example, in engineering, managers will write low level designs, build prototypes, write code, perform code reviews, and delve into any technical topics as needed. Our managers actively reduce mundane busywork to be able to focus on excelling in their IC role.

…we do not care about hiring world class managers or developing management skills for our employees. In fact just the opposite. We'll invest in growing our managers while making sure they are capable of and willing to do IC work.

Ema managers build championship teams, don’t win popularity contests.

Hire people who are better than yourself. 

Continue to raise the bar on talent for the team and for the entire company. This means consistently finding people who are better qualified than the manager and the existing team.

Primary job of managers is to unlock the true potential

 of every individual and create the most productive teams. Imagine you are trying to coach the Math Olympiad team for your country. You won’t fill the team with average math students, you want to find extraordinary talent and you want to stretch them and coach them to be the world's best. That’s the philosophy we follow at Ema.

Your job is not to please everyone in the team. Your only job is to empower our teams to create maximum impact for the company.

Invest your time and resources in high performers.

 These are the individuals who will drive the team's success and impact. Ensure that their skills are matched perfectly to their roles. By concentrating on high performers, you create an environment where excellence thrives. Let low performers go quickly. Tolerating low performance demoralizes high performers. Read the book “

 of ‘adequate performance deserves a generous severance package’. We expect every employee to go above and beyond. As managers your job is to uncover the next level of performance for your team and for the company.

"The only way to discover the limits of the possible is to go beyond them into the impossible." – Arthur C. Clarke

Ema managers encourage their teams to challenge the status quo and explore new frontiers. Impossible goals help us think through the constraints more crisply and allows us to think outside the box. For example, encourage the team to think ‘why does it need 3 months to build this feature? Can it be done in 3 weeks? How about 3 days? How about 3 hours?’, ‘Can this feature scale to 10000 documents? How about a million documents? How about a billion documents?’ ‘Can we get 100 customers in 30 days? How about in 7 days?’ Ask questions and challenge assumptions. That’s how we innovate.

…you should encourage your teams to cut corners to get something done. As an enterprise software company trust is everything for us. We build robust, secure and compliant software. Embracing the impossible is a thought experiment that allows us to constantly challenge the status quo thinking.

Don’t be afraid to “micro-manage”, we call it “micro leadership”.

Brian Chesky’s philosophy of managing products

. Our managers are hands-on leaders who immerse themselves in the details of their team's work. They set clear expectations for their direct reports and actively engage in all aspects of the project. This deep involvement allows managers to accurately assess the quality of work produced and gain valuable insights into team members' performance. By staying closely connected to the day-to-day operations, our leaders can make informed decisions, provide meaningful feedback, and drive continuous improvement across the organization.

…you should continue to micro-manage every individual all the time. Once you gain a deeper level of alignment with your team, you want to empower the team to move faster and not be a bottleneck.

"Success is not final, failure is not fatal: It is the courage to continue that counts." – Winston Churchill

Setbacks are an inevitable part of any journey towards success

. As a leader, it's crucial to foster resilience within your team. Encourage your employees to view failures as valuable learning experiences rather than defeats. Create an environment where calculated risks are encouraged and mistakes are seen as opportunities for growth.

Teach the importance of persistence in the face of adversity.

 Help your team understand that significant achievements often come after overcoming difficult challenges. Celebrate not just the final success, but also the determination shown along the way.

 means using feedback to constantly improve and maintaining a mindset of tactical enhancement. We reject the notion that something working well cannot be improved. Foster a culture where feedback is actively sought and implemented. Regularly review processes, identify areas for improvement, and celebrate progress. Empower your team to own their development, stay curious, and apply new knowledge. Prioritizing continuous improvement keeps your team agile, competitive, and ready for future challenges.

 Treat each crisis as an opportunity to not only improve the product, but also improve the process and team bonding. Startups will have crises.

Lead by example in demonstrating resilience

. Be open about your own struggles and how you've overcome them. This transparency can inspire your team to adopt a similar mindset. Provide necessary support and resources to help your team bounce back from setbacks, such as mentoring programs or professional development opportunities.

By cultivating resilience, you're equipping your team with a powerful tool for long-term success and personal growth. Remember, true resilience isn't about avoiding failure, but about how effectively you can learn, adapt, and keep moving forward.

Understand what it means to create impact at Ema

. Shift focus from mere busyness to meaningful outcomes that drive the organization forward. Encourage your team to ask themselves: "How does this task contribute to our company goals?"

Define clear metrics for success that align with the Ema’s mission

 and strategic objectives. These should go beyond simple activity measures like tasks completed, instead focusing on tangible results and value created for our customers. Remember, sometimes the customer could be an internal customer.

Teach your team to differentiate between high-impact work and low-value busywork.

 Empower them to challenge processes or tasks that don't contribute significantly to the company's goals. Foster an environment where quality of output is valued over quantity of input.

Regularly review and prioritize projects based on their potential impact

. Be willing to reallocate resources from low-impact activities to high-impact initiatives. Encourage your team to regularly assess their to-do lists and calendars, ensuring they're dedicating most of their time and energy to impactful work.

Celebrate and reward impact-driven achievements rather than just effort or activity.

 This reinforces the importance of focusing on outcomes and encourages a results-oriented mindset across the organization.

…we do not try out new things and experiment constantly. Generative AI is still in its early phase and we’ll learn by trying out new things. Many things we try will fail. Learning what does not work and articulating the insights clearly is  meaningful impact for the company. 

Build trust, excellence and accountability, not entitlement.

Build trust with and within your team. Lack of trust results in conflicts, politics and slow decision making. Building trust requires

: Be reliable and consistent in your actions. Follow through on commitments and promises.

: Communicate openly and honestly. Share information and decisions promptly and clearly.

: Ensure you follow up on tasks and promises 

 needing reminders. This demonstrates reliability and respect for others' time and contributions.

Coach yourselves and your team on Fast Decision Making: 

Most teams slow down and don't demonstrate excellence because decisions are stuck in never ending discussions. Encourage holistic and efficient decision making but refrain from decision paralysis due to hypothetical scenarios. As managers never:

Agree and don’t support a decision – this is sabotage and will not be tolerated.

Don’t agree and don’t support -> you are disengaged. You should not be here.

Excellence requires information, communication and operating cadence:

 Excellence demands clear information, communication, and operating cadence. At scale, well-defined processes and automation are crucial. A regular planning rhythm ensures smooth execution, while concise documents aid decision-making. Managers should focus on impactful execution, ask the right questions, provide timely feedback, and guide the team with clear communication. Effective delegation and encouraging team members to identify and address issues are essential practices.

 is crucial for a productive and positive workplace. Managers should maintain high and positive energy. High energy levels are contagious and can significantly impact team productivity and morale. Prevent negative energy and heavily call out cynicism and negative talk about colleagues or customers.

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