The Human + AI Partnership – Designing Workflows That Think

Where Intuition meets Intelligence

As we explored in our last article, Teaching AI your Values, prompts are where AI learns how to think. Workflows, on the other hand, are where that thinking is expressed in action. 

At Palladin, we believe that the future of Salesforce delivery is not just intelligent, but thoughtful and collaborative. The systems we are designing are not meant to replace people, they are meant to think with them. This is because we believe that true innovation isn’t about removing humans from the process, but rather amplifying their potential through design. 

For years, automation has been our hero: assignment rules, email alerts, auto–routing, and flows that execute tasks at scale. But today, in 2025, things have changed, the conversation has evolved. We now have a Salesforce ecosystem with AI embedded across it, from Einstein to Agentforce. It seems like automation is no longer enough. Now, instead of asking “What can we automate?” We are focusing on “How can we design systems that think alongside people?”

An AI driven workflow, or as we like to call it: a thinking workflow, doesn’t just execute a sequence of steps. It observes, learns and adapts. It is a design shift from the If-then logic we are so used to. The real differentiator now is designing workflows that think intelligently and empathetically. 

From Automation to Intelligence

For a long time, automation in Salesforce and many other systems meant efficiency. These systems execute, but they don’t understand. They follow steps in algorithms, instructions, but they don’t know why. Nowadays, with new technologies like Einstein, Data Cloud, and conversational AI in Agentforce together, the goal isn’t just doing things faster, but helping people throughout their thought process. For example, a thinking workflow can be:

1. Context interpretation
   
2. Provide reasoning, not just output
   
3. Guide, instead of dictating
   
4. Always keeps human in control

Listing below some industry examples to explore how this can be applied across real world use cases. 

Media Example

In the media industry, things move fast! Opportunities shift with campaign cycles, advertising budgets, performance results and seasonality. A traditional workflow might be something like:

1. Assign a lead to a seller
   
2. Trigger reminders for the seller
   
3. Send templated outreach

However, this misses context. The workflow doesn’t consider if the brand is mid-campaign or not. If the opportunity is through an agency, it doesn’t consider if it manages multiple brands with different buying behaviors. Or something as simple as whether it is too early or late to reach out. If we add AI to this workflow, it becomes a thinking workflow – looking for patterns within the dataset to provide a level of predictability and added reasoning. 

In the real world – this can lead to recognizing that an advertiser had a successful campaign last quarter. Their new product launch is in six weeks. A warm outreach now is well-timed. Consequently, the agent can draft a message for the seller’s review. The work becomes less administrative, more strategic, and even more human because the seller gets to focus on the relationship itself.

Telecommunications Example

Telco service teams frequently address various issues, including network outages, device provisioning problems, billing disputes, and complex multi-party service dependencies. While a traditional workflow might simply route cases based on category, a more advanced thinking workflow digs deeper. 

For example, it can detect that “Customers in this region are reporting the same fiber issue,” and then proactively surface the known fix, the current incident link, and a recommended text for proactive communication. This sophisticated approach involves detecting patterns before humans do, presenting knowledge in plain language, and recommending the immediate next steps. 

Once again, the AI does not make the final decision. That responsibility remains with the human agent. The AI provides essential visibility and data, while the human agent provides judgment, forming a powerful partnership.

Intelligence That Feels Human

At Palladin, we have tested this design approach across industries, through conversational monitoring tools, SDR support agents, and knowledge retrieval assistants. However, the tools aren’t the story. The design principles are:

Principle	What It Means	Example
Context First	AI must understand the user’s role and intent before responding.	A Salesforce admin agent that tailors insights for Admins and Developers to assist in their day-to-day tasks.
Human in Control	Users always approve or adjust AI outputs.	An SDR Agent suggests, but never sends emails without human confirmation.
Transparent Reasoning	Trust grows when AI shows its work.	An Agent that references the company’s knowledge base and cites its information sources.
Continuous Learning	Feedback improves future recommendations.	All agents evolve from usage patterns and corrective input.

As you can see, the future of work is not “hands off”. It is hands-together: people and AI thinking side by side. The workflow of the future is contextual, conversational, collaborative, and adaptive over time. That’s why at Palladin, our goal isn’t just to create AI but to help organizations create AI-powered workflows that improve human performance, providing greater clarity and confidence within the Salesforce platform.

In short, the goal isn’t to replace people. The goal is to design workflows that make people more powerful, by design.