A specialized service provider reached a common growth ceiling: project managers were spending about 25% of their time on a process with repetitive administrative tasks. By acting as the manual link between client preferences, the statement of work, and various software systems, the team’s growth was restricted by their physical bandwidth rather than market demand. Gestalt was engaged as a consultant to review and analyze a key process that happens about 300 times per year to recommend how AI and automation could be implemented to improve the current process. The goal was to uncover and recommend solutions to increase individual project capacity, speed-to-market, and the customer experience while maintaining the rigorous data integrity required by the client, in a consumer-facing industry.
The Challenge: The Administrative Bottleneck
The client’s process was well-documented and logically sound but labor-intensive. Every new onboarding of a client project triggers a process that requires significant manual effort from the client success team:
- Manual Information Extraction: Project managers review unstructured agreements and scopes to identify critical dates, service tiers, and project requirements.
- Redundant Configuration: That same information is then manually re-entered across their project management tool and internal programs.
- Accuracy Risk: In an industry where a single data error can lead to financial exposure, refund requests, and damaged client trust, speed can never come at the expense of accuracy.
The Strategic Analysis
To begin, the client’s general AI readiness was assessed through interviews with the owner and departmental leadership. The findings revealed an organization experienced in change management, a culture open to innovation and operating in new ways, and well documented processes. The discovery phase also highlighted an opportunity for policy and training related to safe AI use to protect the organization, brand, and team.
In the initial discovery interviews, the manual process that gets repeated 300 times per year became the clear target to understand how AI could impact efficiency. From here the project moved into the process review and analysis stage, further discussions and process analysis uncovered the immediate opportunity: the organization had modern, capable infrastructure, with existing software tools in place, but lacked the “backbone” and system to move information automatically. By automating the data-entry and data-movement tasks, the team could transition from repetitive manual data entry managers to strategic auditors, increasing their capacity to manage more projects and focus more on the client experience and strategy.
In order to allow the client choice in the right fit, three concepts were presented as possibilities.
- Expanded Tool Use: Exploring the AI features of existing tools to use on a case-by-case basis within the existing use of the tools.
- Integrated Workflow: Adding a central backbone within the existing workflow to manage the large language models, existing tools, new tools, and automation while keeping the project managers in the loop for auditing and oversight.
- Advanced Agents: An agentic solution providing greater autonomy to the technology requiring more development and computing resources
The Solution: The Integrated Workflow
Rather than recommending the complex agentic AI system, which would have introduced unnecessary costs and unpredictable variables, the recommendation was the integrated workflow approach. The updated process suggestion focused on connecting existing programs with new AI and automation driven tools through a technology “backbone” to ensure a predictable and cost-effective flow of information.
The Updated Workflow Design
- Document-to-Data: A customized AI pipeline turns unstructured statements of work into usable data.
- Project Initialization: The system builds out the project workspace in the existing project management tool, triggering specific tasks and sub-tasks based on the unique variables identified in the statement of work.
- The “Live” Feedback Loop: Transcripts from client strategy sessions are processed to identify changes in scope, flagging them for the project manager to approve and sync.
- Automatic System Sync: Validated data is pushed directly into the internal, web-based system, eliminating manual configuration and the potential for “copy-paste” errors.
The Results: Success Manager Capacity Gains
By aligning the technology with the client’s existing systems, a measurable, strategic shift in their operational economics is possible:
| Metric | Outcome |
|---|---|
| Capacity Increase | 13% increase in individual project load without additional hours. |
| Scalable Growth | Ability to onboard dozens of additional clients annually prior to new headcount. |
| Financial Resilience | Reduced risk of financial exposure and compliance errors via AI cross-referencing. |
Conclusion: The Value of Strategy
This project highlights that AI success is not about adopting the most “advanced” tools available; it is about a consultative approach that balances organizational readiness, the highest ROI path, and a right-sized solution for a specific business model. By designing a system that respects the need for human oversight while automating repetitive data-entry tasks, the company has the potential to implement a system that will allow for increased revenue at a higher net profitability target.
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