The cost of data: A critical hurdle for Co-pilot implementation
Written by Jens Eriksvik
Organizations are increasingly turning to AI tools like Microsoft Co-Pilot to enhance productivity and streamline workflows. Designed to work seamlessly within the Microsoft 365 ecosystem, Co-Pilot enables smarter collaboration, faster data access, and automation of routine tasks. While the potential benefits are substantial, successful implementation requires careful planning to navigate challenges such as data preparation and governance.
Microsoft Co-Pilot transforms productivity by enabling faster data discovery, automating routine tasks, and improving collaboration across teams. Its strengths lie in its natural language interface, seamless integration with the Microsoft 365 ecosystem, and scalability to adapt to organizational needs over time. By reducing the time spent on mundane tasks and enhancing data accessibility, Co-Pilot empowers employees to focus on high-value work, fosters alignment across departments, and supports compliance through built-in security features.
As a scalable, adaptive AI tool, Co-Pilot drives agility, enhances employee engagement, and acts as a key enabler in long-term digital transformation strategies. With the right implementation, it delivers operational efficiency and strategic impact.
However, deploying Co-Pilot can come with a significant "data cost"—a key challenge that organizations must address upfront. This cost stems from the extensive resources required to prepare SharePoint’s often unstructured and diverse datasets for effective AI integration. If underestimated, it can lead to delays, reduced ROI, and even compliance risks.
“AI tools like Co-Pilot can only be as effective as the data they rely on. A strategic focus on data preparation not only ensures smoother implementation but also strengthens the organization’s ability to scale AI capabilities in the future”
- Jens Eriksvik, CEO at Algorithma
How co-pilot works and its architectural challenges
Microsoft Co-Pilot integrates advanced AI capabilities into the Microsoft 365 ecosystem, relying on natural language processing (NLP) to interpret queries and generate relevant responses. Its architecture includes:
NLP core: Advanced models interpret user inputs to deliver contextually relevant results.
Data access layer: Facilitates secure data retrieval via APIs and role-based access controls (Graph).
Integration layer: Ensures seamless functionality across applications like Word, Excel, and Teams.
Compliance and security framework: Incorporates encryption and monitoring to meet regulatory standards.
Large Language Model (OpenAI): The LLM generates a response based on the user's prompt and the context provided by the grounding data. Learn more about LLMs here.
While robust, Co-pilot’s architecture depends heavily on structured, secure, and well-governed data. In organizations with sprawling or unmanaged SharePoint environments, this dependency creates challenges:
Data structuring and metadata creation: Co-Pilot requires well-organized data with consistent metadata to deliver accurate responses. Structuring untagged or unstructured data demands substantial resources.
Security and access controls: Auditing and standardizing access permissions are critical to prevent unauthorized access and protect sensitive information. Secure implementation requires encryption, role-based access control, and monitoring.
Compliance with regulations: Safeguarding data to meet regulations like GDPR involves encryption, secure API usage, and automated auditing systems, significantly increasing costs. Compliance failures risk financial and reputational damage.
Data cleanup: Removing obsolete files, managing duplicates, and implementing version control improve Co-Pilot’s output but require extensive effort, particularly in sensitive data environments.
Ongoing security governance: Ensuring sustained security involves continuous monitoring of Co-Pilot’s interactions, incident response, and regular audits to identify vulnerabilities and ensure data remains compliant and protected.
The true scope of data costs
Implementing Microsoft Co-Pilot comes with significant data preparation challenges, which can escalate costs and strain resources if not managed effectively. Preparing SharePoint for Co-pilot demands a comprehensive approach to organizing unstructured data, resolving compliance issues, and addressing resource constraints
The total cost of ownership for Co-pilot deployment rises significantly when data preparation is included. Costs often stem from:
Hiring or reallocating data specialists.
Purchasing or upgrading tools for metadata management and compliance auditing.
Addressing access control inconsistencies. Failure to account for these costs upfront can disrupt budgets and delay the realization of Co-pilot's benefits.
The complexity of unstructured data and regulatory requirements significantly amplifies the cost of implementing Co-Pilot. SharePoint environments often house vast amounts of redundant or outdated documents, demanding advanced tools and skilled personnel for organization and tagging. Without robust metadata frameworks, Co-Pilot’s effectiveness is severely hindered. Additionally, ensuring compliance with regulations like GDPR requires substantial investment in governance frameworks, monitoring tools, and expert oversight. Mishandling these complexities not only escalates costs but also exposes organizations to legal and reputational risks, underscoring the importance of proactive planning and management.
While the costs of preparing data for Co-Pilot may seem daunting, they also present a unique opportunity to establish a stronger data foundation. By investing in structured metadata, streamlined access controls, and robust compliance frameworks, organizations not only enable Co-Pilot to perform effectively but also future-proof their data infrastructure. However, these costs and efforts must be carefully taken into account and actively managed when embarking on the Co-Pilot journey. A strategic approach to data preparation ensures that the upfront investment is controlled, risks are mitigated, and the organization reaps long-term benefits such as enhanced operational efficiency, stronger security, and readiness for future AI-driven initiatives.
Addressing data costs through a use-case driven approach to Microsoft co-pilot
Implementing Microsoft Co-pilot can transform organizational productivity, but its success depends on navigating significant data preparation challenges. Adopting a use-case-driven approach ensures that Co-pilot is implemented in a way that minimizes costs, aligns with business priorities, and delivers measurable results. By focusing on specific use cases and addressing data costs systematically, organizations can avoid common pitfalls and maximize the value of their investment.
Define and prioritize use cases
Before deploying Co-pilot, identify and prioritize use cases where its capabilities can deliver the most value. For instance, consider how Co-pilot can enhance workflow efficiency by automating document retrieval and classification within a specific department. Think about how it can improve collaboration by enabling smarter search and shared insights across team sites. Additionally, explore how Co-pilot can be leveraged for precise analysis of structured datasets, providing valuable data insights. By focusing on targeted applications like these, organizations can avoid the costs of a broad, unfocused deployment and direct resources to areas with the highest potential impact.
Assess and address data challenges
Each prioritized use case should include a thorough evaluation of the data environment. Ask key questions such as: Are the datasets required for this use case structured and tagged appropriately? What is the scope of unstructured or redundant data that must be cleaned up? Use this assessment to create a focused plan for data preparation, avoiding unnecessary efforts across unrelated datasets. Read more about the data challenge in this article.
Start with narrow, manageable pilots
Deploy Co-pilot in a controlled pilot phase focused on a single department or process. For example, you could test Co-pilot in HR to retrieve and classify policy documents, starting with structured, non-sensitive data. This approach limits the scope of data preparation, reduces upfront costs, and provides a valuable opportunity to refine metadata frameworks and access controls before scaling up to a wider deployment.
Implement robust governance
Data governance is essential to manage data costs and ensure compliance. Establish clear, role-based access to sensitive datasets to minimize security risks. Ensure data handling aligns with regulations like GDPR to avoid fines and reputational damage. Continuously monitor Co-pilot’s interactions with data to detect any issues early on. Incorporating governance into each use case reduces long-term costs associated with data breaches or non-compliance. Read more about this in our article on data governance.
Invest in scalable data structuring
For each use case, focus on creating scalable solutions for data organization. Implement metadata tagging frameworks that are consistent across use cases. Utilize automation tools to accelerate the cleanup of unstructured or duplicate files. The efforts made during these initial pilots can serve as a foundation for future deployments, effectively spreading the cost of data preparation over time.
Leverage hybrid approaches
For use cases requiring specialized capabilities, combine Co-pilot with targeted tools or processes. For instance, use Co-pilot for general knowledge retrieval but complement it with specialized systems for advanced analytics or compliance tasks. This hybrid strategy ensures Co-pilot is used where it excels while mitigating data preparation costs for tasks it may not handle efficiently.
Measure and refine the approach
For each use case, track costs, outcomes, and efficiency improvements. Ask questions like: Did Co-pilot deliver measurable time savings or improved accuracy? Were data preparation costs within expected ranges, and how can they be reduced in future deployments? Refining the approach based on real-world results ensures that the benefits of Co-pilot outweigh its costs.
By applying Algorithma’s use-case-driven approach, organizations can address Co-pilot’s data costs strategically. Targeted use cases avoid unnecessary data preparation across the entire SharePoint environment. Pilots and phased scaling help spread the investment in data preparation over time. Embedding strong data governance early reduces compliance risks and ensures data integrity long-term. This focused strategy transforms data preparation from a hurdle into a strategic enabler, allowing organizations to unlock Co-pilot’s potential while managing costs effectively.
Alternatives to consider
To avoid the potential pitfalls of implementing a broad AI assistant like Co-Pilot, organizations can consider a more focused approach tailored to their specific needs. This could involve deploying targeted solutions for specialized functions, such as automating workflows, generating content, or conducting in-depth data analysis. These solutions, whether within or outside the Microsoft 365 ecosystem, can offer more precision and reduce the complexity of implementation.
Another option is a hybrid approach that combines the versatility of a broad AI assistant with the efficiency of specialized tools, addressing diverse organizational requirements. For tasks requiring customization or oversight, organizations might explore open-source platforms for tailored development or integrate human-in-the-loop systems to maintain quality control and accountability.
Finally, traditional, non-AI solutions should not be overlooked. In some cases, they may provide the most effective and cost-efficient way to meet specific objectives without the additional overhead of AI integration. By adopting a multifaceted strategy and evaluating factors such as scalability, resource availability, and organizational goals, businesses can select the most appropriate tools and methods to maximize productivity while managing costs and complexity.
Learn more on how to effectively implement and scale AI assistants in the article “Overcoming barriers to scaling AI assistants”.
