How Anchora Cut Resourcing Admin Time With Productive AI

As Operations Director, I’m responsible for making sure everything behind the scenes runs smoothly. That includes the tools and processes we use, how we resource projects, contract review, procurement, and a lot of finance.

A big part of my role is ensuring that our operational data is accurate. We rely on that information for financial reporting, business case development for hiring, and decisions about how we run the business. That means everything needs to be set up correctly from the beginning. Projects need the right information, resource allocations need to be accurate, and we need to trust the data we’re working with.

I’m also responsible for all resourcing across the business. I’m the only person who makes bookings in Productive, which allows us to manage priorities, avoid scheduling conflicts, and maintain control over resource allocation.

As you can imagine, that creates quite a lot of administrative work. Or at least, it used to

Productive AI has been amazing. It’s really taken the complexity out of using Productive for me.

I started with relatively simple things, like asking AI to create reports or explain how to do something. Previously, I might have needed to look up instructions or ask support for help. Now I can just ask the AI, and quite often it’ll actually do what I need instead of simply telling me how.

But I’ve gone well beyond that. Practically everything I do in Productive now starts with asking AI to do it for me. Instead of manually creating, moving, or editing resource bookings, I give AI instructions on what I want to happen. The same goes for setting up projects, adjusting cost rates, creating budgets, and reporting.

It’s completely changed how I interact with the platform. Rather than navigating through different screens and updating individual fields, I can explain what I need in plain language and let AI handle the repetitive work.

Resourcing, without a doubt.

Because I’m the only person managing bookings, even relatively small changes can create a lot of work. If someone was unavailable, a project was renewed, or we needed to reassign resources, I previously had to edit individual bookings. Now I can simply tell AI what needs to change.

For example, we recently created a new project for a contract renewal. I asked AI to recreate the bookings from the original project. Then I realized those bookings needed to be tentative, so I asked it to change them. After that, I needed to reassign some bookings from one person to another.

Each time, I just gave AI another instruction. I also use it whenever someone is absent. Instead of going through their schedule and manually rearranging everything, I ask AI to split their bookings around the absence.

Another example is managing contractor cost rates. We work with contractors on a time-and-materials basis, so we need to forecast their hours accurately. Previously, updating those cost rates meant editing individual fields for each day of the week.

It was incredibly painstaking, especially when you’re dealing with multiple cost rate periods.
Now I can tell AI what needs adjusting, and it makes the changes for me. I’d estimate that resourcing now takes about 5% of the time it used to. That’s a massive improvement, particularly when you consider how many little changes happen across projects every day.

Yes, and that’s another area where it’s made a big difference.

We have standard, complex Excel templates for estimating and planning projects. I’ve adapted our estimate spreadsheets to output information in a format Productive AI can understand and use.
For example, I can take information from one of our resource plans, paste it into the AI assistant, and ask it to create the budget or bookings for a particular project. I’ve also included Productive’s terminology in those templates, so AI understands how we want things structured, including milestones and sections.

For more complex resource plans, I sometimes upload a separate file with the correct headings. That works particularly well when we’re dealing with projects that have multiple stages, gaps between activities, or more complicated scheduling requirements.

The important thing is that I no longer need to manually recreate all that information inside Productive. I can use the planning processes we already have, provide AI with the relevant information, and let it handle the setup.

I also use AI to create work breakdown structures for internal projects. Instead of thinking through every task and entering everything individually, I can describe the activity and have AI create a starting structure, including tasks and assignees.

One of my favorite examples is a Skill I created to help us find people with particular technical expertise.

This is actually something we’d been trying to solve for a long time. People throughout the business are constantly asking questions like, “Who knows how to work with this platform?” or “Who has this particular skill, and when are they available?”

Previously, we didn’t have a proper way to track everyone’s skills in Productive.

I started by adding a custom field to each person’s profile to record the platforms and technologies they specialize in. Then I created an AI Skill that looks at that information, identifies people with the required expertise, and checks their availability. Now, instead of manually looking up someone’s skills and then checking their schedule, we can simply ask AI.

It can tell us who has the relevant expertise, when they’re next available, and what their capacity looks like. We’re combining information about people’s skills with their actual availability, all through a single question.

That’s been massive for us. Our CEO has always wanted to be able to ask who has a particular skill and when they’re next available. And now we can actually do that. The custom fields alone wouldn’t have solved the entire problem. We’d still need someone to manually search through profiles and schedules.

It’s AI that makes all that information immediately useful.

Yes. Most of the Skills I’ve created are designed to solve specific problems people regularly bring to me, or to help us manage operational risks.

For example, our project managers often want a calendar showing when everyone is on leave and when public holidays fall. That’s not something I could easily generate through Productive’s regular reporting setup, but AI can create the information in a table when I ask.

I’ve also created a Skill that helps with setting up different types of projects. We have templates with specific task lists and assignments depending on the type of work we’re doing. Instead of repeatedly setting everything up, I’ve put those instructions into a Skill so AI can follow the same process.
Another one I’m working on is project certification checking.

It is our policy to ensure that people working on live projects are certified for the platforms they work with.

I’ve started building a Skill that cross-references the platforms listed in a project’s custom fields with the certifications held by the people assigned to that project. Once we’ve finished adding everyone’s certification information, the idea is that AI will identify whether everyone assigned to a project has the relevant certifications and flag any gaps.

That would be useful for our project managers, particularly when communicating with clients.
These aren’t necessarily glamorous AI use cases, but they solve real operational challenges. They help us make better use of our data, reduce repetitive work, and manage risks more effectively.

I’m constantly encouraging our project managers to use it whenever they need to work with Productive.

A lot of the informational questions people ask me are ones they can now ask Productive AI directly.
For example, someone might ask when a particular employee is next available. Instead of me checking the schedule and giving them an answer, they can ask AI directly. It’s faster for them, and it means I’m not spending time looking up information that they can access themselves.

That’s one of the things I really like about it. AI doesn’t just help me complete my own work more efficiently. It also makes information more swiftly accessible to everyone else. Our project managers are incredibly busy, and our next priority is to get them to set up their own Skills and Agents in Productive’s AI to reduce their administrative workload.

I’ve developed Skills around my own responsibilities because I know exactly where our operational bottlenecks are. But I’m interested in learning how other project managers using Productive use AI, because I’m sure there are plenty of opportunities we haven’t explored yet.

One of the biggest opportunities I see is connecting the information we have in Productive with data from other systems.

We use Claude as our enterprise AI platform, and we also have important business information stored in systems like SharePoint and HubSpot.

Productive is excellent for managing our operational data, but stakeholders often need answers that involve information from multiple places.

For example, we might want to combine project information from Productive with client details from HubSpot and documents stored in SharePoint. That’s where connecting Productive to Claude through MCP becomes particularly interesting. Rather than having to search across different platforms or manually bring information together, the goal would be to ask a question and have AI find the relevant data wherever it lives.

I’m also interested in taking some of the processes I’ve already built with AI Skills and automating them using AI Agents. For example, I currently ask AI to split existing bookings when someone is absent. I’d love to make that happen automatically when an absence is added.

For me, it’s how much complexity it’s removed from everyday operations.

I don’t have to think about how to navigate every part of Productive or manually update every individual entry. I can focus on what needs to happen and ask AI to make it happen. That’s especially valuable in resourcing, where something that sounds simple can involve updating dozens of individual bookings or cost rates.

And beyond the time savings, AI has helped us solve problems we’ve wanted to address for a long time, like making employee skills and availability searchable in one place.

It’s also changing how people access information. Instead of everything having to go through me, we’re getting to a point where people can find answers on their own.

Ultimately, Productive AI is helping us spend less time managing the administrative details of running the business and more time focusing on the work that actually needs our attention.