The Next Generation of Enterprise Systems
The Next Generation of Enterprise Systems
The Next Generation of Enterprise Systems
The Next Generation of Enterprise Systems
From Transactions to Outcomes.
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From Transactions to Outcomes.
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From Transactions to Outcomes.
If you zoom out on the history of enterprise software, there’s a clear pattern.
The early days were about unifying internal processes—massive platforms—ERP systems. Finance, HR, supply chain, manufacturing—they all lived in one place. The goal was simple: cut costs, create efficiency, and keep the business running in sync. It was a back-office revolution, but it was all about the company, not the customer.
Then with the cloud came CRM. The spotlight shifted from internal operations to the sales floor. We digitized lead lists, tracked pipelines, automated emails, and built dashboards. The goal was to drive transactions—win more deals, grow revenue. This era put the customer on the map, but mostly as a number in the pipeline.
“Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results.”
If you zoom out on the history of enterprise software, there’s a clear pattern.
The early days were about unifying internal processes—massive platforms—ERP systems. Finance, HR, supply chain, manufacturing—they all lived in one place. The goal was simple: cut costs, create efficiency, and keep the business running in sync. It was a back-office revolution, but it was all about the company, not the customer.
Then with the cloud came CRM. The spotlight shifted from internal operations to the sales floor. We digitized lead lists, tracked pipelines, automated emails, and built dashboards. The goal was to drive transactions—win more deals, grow revenue. This era put the customer on the map, but mostly as a number in the pipeline.
“Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results.”
If you zoom out on the history of enterprise software, there’s a clear pattern.
The early days were about unifying internal processes—massive platforms—ERP systems. Finance, HR, supply chain, manufacturing—they all lived in one place. The goal was simple: cut costs, create efficiency, and keep the business running in sync. It was a back-office revolution, but it was all about the company, not the customer.
Then with the cloud came CRM. The spotlight shifted from internal operations to the sales floor. We digitized lead lists, tracked pipelines, automated emails, and built dashboards. The goal was to drive transactions—win more deals, grow revenue. This era put the customer on the map, but mostly as a number in the pipeline.
“Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results.”
Now, the rules are changing again and we’re entering the next-gen CRM era.
Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results. They want to sell more, they want to close their books two weeks earlier, they want to accurately predict revenue.
Addressing these requests at the necessary scale starts with unifying data from every customer touchpoint—sales, service, product usage, marketing, support—and using that information to predict, personalize, and orchestrate. It’s less about what we do as companies; it’s about the value they get as customers.
This shift from process to transaction to outcome is the real shift from company-centric to customer-centric. And it’ll require your technology stack to adapt. This is where many people these days would suggest using AI, and soon realise it is not the cure-all they expected it to be.
Now, the rules are changing again and we’re entering the next-gen CRM era.
Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results. They want to sell more, they want to close their books two weeks earlier, they want to accurately predict revenue.
Addressing these requests at the necessary scale starts with unifying data from every customer touchpoint—sales, service, product usage, marketing, support—and using that information to predict, personalize, and orchestrate. It’s less about what we do as companies; it’s about the value they get as customers.
This shift from process to transaction to outcome is the real shift from company-centric to customer-centric. And it’ll require your technology stack to adapt. This is where many people these days would suggest using AI, and soon realise it is not the cure-all they expected it to be.
Now, the rules are changing again and we’re entering the next-gen CRM era.
Customers are looking for outcomes and care less about your features and products. And they expect you to deliver them—not in theory, but in measurable, demonstrable results. They want to sell more, they want to close their books two weeks earlier, they want to accurately predict revenue.
Addressing these requests at the necessary scale starts with unifying data from every customer touchpoint—sales, service, product usage, marketing, support—and using that information to predict, personalize, and orchestrate. It’s less about what we do as companies; it’s about the value they get as customers.
This shift from process to transaction to outcome is the real shift from company-centric to customer-centric. And it’ll require your technology stack to adapt. This is where many people these days would suggest using AI, and soon realise it is not the cure-all they expected it to be.
AI in the Enterprise: The Foundation Problem
The truth about AI in the enterprise—it’s only as good as the data you feed it.
That might sound obvious, but it’s where most companies fail. AI models, prompts, and agents don’t magically work out of the box. If your data is fragmented across systems, riddled with inconsistencies, or missing key context, your AI will give you the wrong answers faster than ever before—and with dangerously convincing confidence.
“Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon. ”
AI in the Enterprise: The Foundation Problem
The truth about AI in the enterprise—it’s only as good as the data you feed it.
That might sound obvious, but it’s where most companies fail. AI models, prompts, and agents don’t magically work out of the box. If your data is fragmented across systems, riddled with inconsistencies, or missing key context, your AI will give you the wrong answers faster than ever before—and with dangerously convincing confidence.
“Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon. ”
AI in the Enterprise: The Foundation Problem
The truth about AI in the enterprise—it’s only as good as the data you feed it.
That might sound obvious, but it’s where most companies fail. AI models, prompts, and agents don’t magically work out of the box. If your data is fragmented across systems, riddled with inconsistencies, or missing key context, your AI will give you the wrong answers faster than ever before—and with dangerously convincing confidence.
“Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon. ”
Right now, nine out of ten companies have a data problem. The solution isn’t sexy, but it’s essential:
Automate data cleansing. Modernize the data stack. Enforce governance.
When the foundation is strong, AI becomes a true force multiplier. It can surface proactive insights, automate entire workflows, and let your teams focus on the high-leverage work—building trust, mapping outcomes, and orchestrating cross-functional execution.
That’s exactly why we built Planhat the way we did.
Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon.
Building upon a foundation of clean, connected data in this way is going to become a prerequisite for any enterprise software that promises AI deployments that people can trust to operate at scale, and without constant oversight.
Right now, nine out of ten companies have a data problem. The solution isn’t sexy, but it’s essential:
Automate data cleansing. Modernize the data stack. Enforce governance.
When the foundation is strong, AI becomes a true force multiplier. It can surface proactive insights, automate entire workflows, and let your teams focus on the high-leverage work—building trust, mapping outcomes, and orchestrating cross-functional execution.
That’s exactly why we built Planhat the way we did.
Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon.
Building upon a foundation of clean, connected data in this way is going to become a prerequisite for any enterprise software that promises AI deployments that people can trust to operate at scale, and without constant oversight.
Right now, nine out of ten companies have a data problem. The solution isn’t sexy, but it’s essential:
Automate data cleansing. Modernize the data stack. Enforce governance.
When the foundation is strong, AI becomes a true force multiplier. It can surface proactive insights, automate entire workflows, and let your teams focus on the high-leverage work—building trust, mapping outcomes, and orchestrating cross-functional execution.
That’s exactly why we built Planhat the way we did.
Our architecture doesn’t just store data—it structures it as time-series, contextual customer information that’s ready for AI to consume and act upon.
Building upon a foundation of clean, connected data in this way is going to become a prerequisite for any enterprise software that promises AI deployments that people can trust to operate at scale, and without constant oversight.
Platform Consolidation and Agents Doing Work
In the enterprise, not all work is created equal. For deterministic workflows—situations where the same action always causes the same result—companies will increasingly consolidate onto core platforms for their most important, repeatable functions.
These will be the major Enterprise platforms of the future with AI built into their core. Their interfaces will be tuned for AI interaction, their workflows fully designed for AI agents to operate in. Over time, the majority of usage will shift from human operators to AI agents—not necessarily because humans are doing less, but because agents will do so much more than we do today, allowing us to prioritise authentic connection.
“The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.”
Platform Consolidation and Agents Doing Work
In the enterprise, not all work is created equal. For deterministic workflows—situations where the same action always causes the same result—companies will increasingly consolidate onto core platforms for their most important, repeatable functions.
These will be the major Enterprise platforms of the future with AI built into their core. Their interfaces will be tuned for AI interaction, their workflows fully designed for AI agents to operate in. Over time, the majority of usage will shift from human operators to AI agents—not necessarily because humans are doing less, but because agents will do so much more than we do today, allowing us to prioritise authentic connection.
“The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.”
Platform Consolidation and Agents Doing Work
In the enterprise, not all work is created equal. For deterministic workflows—situations where the same action always causes the same result—companies will increasingly consolidate onto core platforms for their most important, repeatable functions.
These will be the major Enterprise platforms of the future with AI built into their core. Their interfaces will be tuned for AI interaction, their workflows fully designed for AI agents to operate in. Over time, the majority of usage will shift from human operators to AI agents—not necessarily because humans are doing less, but because agents will do so much more than we do today, allowing us to prioritise authentic connection.
“The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.”
Alongside these AI-first platforms, we’ll see a wave of agent-only solutions built for specialized, non-deterministic work—things like predicting churn before it happens or navigating complex compliance requirements in niche industries. These areas often lack established software leaders, making them ripe for innovation.
In practice, these agents might live and operate directly inside unified customer platforms like Salesforce or Planhat, drawing on clean, structured customer data to execute tasks without human intervention (perhaps not so clean in Salesforce, but nevertheless). They’ll also connect with other enterprise systems and join cross-platform workflows—working alongside humans to drive outcomes at scale.
All this ultimately ends with a platform that streamlines processes, surfaces insights, and enables automation—which sounds a lot like an ERP. The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.
Alongside these AI-first platforms, we’ll see a wave of agent-only solutions built for specialized, non-deterministic work—things like predicting churn before it happens or navigating complex compliance requirements in niche industries. These areas often lack established software leaders, making them ripe for innovation.
In practice, these agents might live and operate directly inside unified customer platforms like Salesforce or Planhat, drawing on clean, structured customer data to execute tasks without human intervention (perhaps not so clean in Salesforce, but nevertheless). They’ll also connect with other enterprise systems and join cross-platform workflows—working alongside humans to drive outcomes at scale.
All this ultimately ends with a platform that streamlines processes, surfaces insights, and enables automation—which sounds a lot like an ERP. The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.
Alongside these AI-first platforms, we’ll see a wave of agent-only solutions built for specialized, non-deterministic work—things like predicting churn before it happens or navigating complex compliance requirements in niche industries. These areas often lack established software leaders, making them ripe for innovation.
In practice, these agents might live and operate directly inside unified customer platforms like Salesforce or Planhat, drawing on clean, structured customer data to execute tasks without human intervention (perhaps not so clean in Salesforce, but nevertheless). They’ll also connect with other enterprise systems and join cross-platform workflows—working alongside humans to drive outcomes at scale.
All this ultimately ends with a platform that streamlines processes, surfaces insights, and enables automation—which sounds a lot like an ERP. The fundamental difference is that this new generation of enterprise software is light, modular, and—critically—built around the needs of the customer, rather than those of the company.
Winning B2B Platforms
Winning B2B platforms of the future will need to enable seamless collaboration and automation across core workflows. It’ll require:
Data-first architecture: Structured, time-series customer data designed for AI and commercial impact.
Unified platform: Sales, CS, Product, and Service in one modular but connected system - no silos.
AI-native workflows: Agents that automate repetitive tasks, surface insights, and operate across the customer journey without friction.
Collaboration by design: People and agents working together inside a single, connected workspace.
Flexibility: Support for diverse industries, business models, and customer success motions without forcing a one-size-fits-all approach.
The next era of B2B systems will be shaped by how well they balance deterministic and non-deterministic workflows—using AI-powered platforms to execute critical, repeatable processes with precision, while integrating specialized agents to handle complex, adaptive work—powered by clean data, unified workflows, and automations.
Winning B2B Platforms
Winning B2B platforms of the future will need to enable seamless collaboration and automation across core workflows. It’ll require:
Data-first architecture: Structured, time-series customer data designed for AI and commercial impact.
Unified platform: Sales, CS, Product, and Service in one modular but connected system - no silos.
AI-native workflows: Agents that automate repetitive tasks, surface insights, and operate across the customer journey without friction.
Collaboration by design: People and agents working together inside a single, connected workspace.
Flexibility: Support for diverse industries, business models, and customer success motions without forcing a one-size-fits-all approach.
The next era of B2B systems will be shaped by how well they balance deterministic and non-deterministic workflows—using AI-powered platforms to execute critical, repeatable processes with precision, while integrating specialized agents to handle complex, adaptive work—powered by clean data, unified workflows, and automations.
Winning B2B Platforms
Winning B2B platforms of the future will need to enable seamless collaboration and automation across core workflows. It’ll require:
Data-first architecture: Structured, time-series customer data designed for AI and commercial impact.
Unified platform: Sales, CS, Product, and Service in one modular but connected system - no silos.
AI-native workflows: Agents that automate repetitive tasks, surface insights, and operate across the customer journey without friction.
Collaboration by design: People and agents working together inside a single, connected workspace.
Flexibility: Support for diverse industries, business models, and customer success motions without forcing a one-size-fits-all approach.
The next era of B2B systems will be shaped by how well they balance deterministic and non-deterministic workflows—using AI-powered platforms to execute critical, repeatable processes with precision, while integrating specialized agents to handle complex, adaptive work—powered by clean data, unified workflows, and automations.
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