The hiring question
A consultancy wins another project, but the delivery lead cannot start. The scope is in a proposal, the client’s exceptions are in meeting notes and someone has to piece them together before kickoff.
An operations hire might be the right decision. Someone may need to manage capacity, resolve competing priorities and look after clients. Those responsibilities deserve attention. But a role built largely around copying details and chasing handovers calls for a closer look at the process.
Before adding a role around that workload, follow a recent client from signed proposal to project setup. Test whether the same information can be prepared once, checked by the delivery lead and passed into the tools the team already uses.
A useful handover
The handover needs to capture the agreed objectives, deliverables, dates, client contacts and open questions. Some details come from a signed proposal. Others are buried in meeting notes. A useful AI step could assemble a draft, point back to its sources and flag what is missing.
The delivery lead would then review it. A date mentioned during an early call should not override the signed scope. An idea discussed as a possibility should not quietly become a commitment. The review is where the team confirms what the project actually means.
After approval, a predictable automation could create the project, assign its owner and request outstanding inputs. Those steps do not need an AI agent choosing its own path. They need the right information and a dependable connection between systems.
For this consultancy, the distinction is practical. AI helps interpret scattered information. Once someone approves the result, creating a project and assigning its owner can follow a fixed sequence. Each part of the handover gets an approach suited to the job.
The consultancy now has a testable proposal: prepare the handover once, review it properly and pass the approved information into the tools the team already uses. Whether that is worth building depends on what happens when it meets real work.
Testing the draft
Take a completed project and try preparing its handover this way. Can the delivery lead trace the important details to their sources? Does the draft miss the exception that changed the scope? How much correction does it need?
Then try a less tidy project. Give it conflicting dates, an incomplete brief or an unusual request. These cases help establish whether the process can recognize uncertainty and send it to the right person.
Keep the people doing the work involved. The onboarding manager may explain that a field nobody uses is mandatory, while a detail the delivery lead always needs has nowhere to go. Improving the handover may require changing the template as much as configuring AI.
Before the team starts using it, agree who can change the process and who handles a failed handover. Give it access to the project information it needs. If the source documents conflict or an approval is missing, the delivery lead should get a clear question to resolve before setup continues.
Only then does a promising demonstration become a candidate for everyday use. The test is whether the team can get a client ready for delivery with less effort and enough confidence to rely on the result.
Using the capacity
Suppose the new process releases a few hours each week. What happens to them? The delivery lead could use that time to prepare for kickoff. The account manager could respond to another qualified inquiry. Or those hours might disappear into a backlog elsewhere in the business.
That is why the growth question needs to stay in view. Track the total effort from signed agreement to a delivery-ready project, including checking, corrections and exceptions. Look at whether clients start sooner, whether clarification requests fall and whether the people released from administration can take on useful work.
Compare that improvement with the cost of building and running the process. A reduction in effort is capacity the business can use. It becomes a cash saving only when an actual expense changes, and additional revenue only when the business can win and serve the extra work.
If the consultancy cannot deliver another project because it lacks a specialist, a cleaner handover will not solve that shortage. It may still improve service, but the investment should be judged on that basis. AI has a better chance of earning its place when the business is honest about the constraint.
Your next client
Your version of this story might begin with a recruitment assignment, a maintenance request or a new advisory client. Watch what happens after someone says yes. Notice who has to interpret the request, who rewrites the information and who chases it into the next stage.
Before you decide you need another hire or a new AI platform, put one of those handovers on paper. Record how often it happens, the effort it takes, the decisions people make and what gets held up. Then choose a result worth testing: a client ready sooner, fewer corrections or more time for the team to do the work customers pay for.
That is a concrete starting point for AI adoption in a service business. It also gives you a way to judge a proposed solution before committing to a wider rollout.
At Superfactor, we can help you examine that workflow, decide where AI and automation add value, and design and engineer the changes around your team. Bring us the part of winning a new client that everyone quietly dreads. We can start there.
