Despite its immense promise and ubiquitous presence in today’s business discussions, determining how to effectively select and implement the right AI-powered knowledge management solution remains a source of confusion and trepidation for many GTM leaders.
The need to operationalize AI is felt acutely across customer-facing teams, where content silos, fragmented tech stacks, and increasingly high customer expectations for fast and accurate turnarounds are mounting challenges. Executives and business unit leaders realize the stakes are high, with many companies now introducing AI committees to determine the most effective path forward. After all, it is becoming abundantly clear that GTM teams that effectively employ AI to harness the full power of their knowledge bases will quickly outpace competitors who don’t.
This report is not about theoretical musings; it's a hands-on toolkit designed to equip GTM leaders with the practical know-how to navigate the complex terrain of AI-powered knowledge management. It’ll outline a clear and proven approach to implementing AI across your organization’s knowledge base and delve into specific ways that AI is being used to make Support, CS, and Sales teams significantly more efficient by expediting routine responsibilities and automating administrative tasks that indirectly contribute to an employee’s core mandate.
After reading this report, you’ll have a clear sense of:
Note: if you’re reading this in a rush, we’ve provided a TLDR summary at the end of each section.
Let’s dig in.
TLDR
Before figuring out how to implement the right AI solution for knowledge management, let’s align on a simple definition and some common use-cases we’re seeing today. AI-powered knowledge management refers to the use of AI technologies to enhance the creation, organization, retrieval, and utilization of knowledge within an organization. When done effectively, knowledge management fueled by AI gives customer-facing teams instant access to the information they need to work faster and create exceptional customer experiences.
Here are some examples of how specific customer-facing teams can benefit:
The possibilities of applying AI across customer-facing teams are game changing, and as a GTM leader, you have an opportunity to shine in helping your organization get ahead by understanding how to assess and make use of the technology.
Take a moment to consider the current state of how your customer-facing teams find and make use of your company’s existing knowledge base. Even young companies quickly build up a considerable store of knowledge across various databases, software platforms, and formats. As Okta’s Businesses at Work shows, the average organization uses over 130 software applications, including a broad range of tools from productivity suites to specialized applications. Customer-facing teams are no exception. As Forrester and Gartner point out, the average employee on a customer-facing team at a SaaS company jumps between 20+ software tools and spends 10+ hours searching for information each week. In other words, most employees on customer-facing teams struggle to quickly find information critical to carrying out their roles, and in the worst case, pass along outdated or inaccurate information to prospects and customers. Even with well managed content systems and resourceful team members, having high volumes of content produced across various teams and systems can get messy. The results can be costly: variance in the quality of customer service, diminishing CSAT scores, and ultimately a loss of business. In fact, Zendesk’s most recent report reveals that an alarming 67% of consumers have switched companies due to poor customer service.
If this sounds familiar, you’re not alone. Vast enhancements in AI can help solve the fragmented state of knowledge for today’s GTM teams, but before assessing your options, it helps to take an honest look at the current state of your knowledge posture across your GTM teams. Try to answer the following questions:
For GTM leaders, knowing the answers to these questions, or at least where to find them, will give you a headstart in ensuring your organization is ready to implement an AI solution. Pro tip: stand out in your AI steering committee by bringing a version of this checklist to the table for the team to collectively answer.
To determine if you are ready to elevate your knowledge posture using AI, evaluate your current state based on the questions posed. Your readiness can generally be categorized into three tiers:
If your responses reveal significant gaps, such as fragmented data systems, lack of integration among tools, and an underdeveloped knowledge base, your organization may be at an early stage of readiness (i.e. you have some heavy lifting to do). At this point, it’s crucial to focus on strengthening your foundational elements, such as improving data management, consolidating tools, and developing a shared understanding across leaders of customer-facing teams for how AI can help with knowledge management.
Customer-facing teams that show some alignment with AI readiness but have areas needing improvement fall into this tier. If you have some semblance of a strategy in place, but face challenges with integration, training, or data consistency, you are somewhat prepared. The next steps involve enhancing integration, streamlining processes, and addressing any remaining gaps to better support AI initiatives.
Companies that demonstrate strong alignment with AI readiness, including well-integrated systems, a comprehensive knowledge base, and a clear AI strategy, are considered well-prepared. At this stage, you are ready to move forward with implementing AI across your customer-facing teams. Ensure continuous evaluation and adaptation to maintain effectiveness and capitalize on AI’s benefits.
It’s possible for one customer-facing team to be further along than others, but it’s important to understand that the full value of AI in knowledge management can only be realized when each team is adequately plugged in and contributing to / gaining value from your knowledge base.
Still, by assessing your organization against these tiers, you can identify where improvements are needed and develop a clear path towards effective AI implementation.
While internal solution development certainly has its benefits, such as customization to specific needs and greater control over features, there are potential pitfalls as well. For starters, nearly half of IT projects exceed their initial budgets. Another 49% take longer than expected to complete, and 14% fail altogether. The bottom line? Building in-house might be the right path for your company, but it will cost time and money, and it’s important to factor in unexpected delays, budget overruns, and the ongoing need for maintenance and support. For a full breakdown of the build vs. buy discussion, check out this blog post.
The reason many companies opt to build knowledge management solutions, rather than buy, is because they think it’s more cost effective than a third-party SaaS solution. In the short term, this may seem true. But over time, the talent and money required to manage, sustain, and improve your knowledge base is likely to cost a lot more than leveraging a third-party solution. In addition, predicting the total cost of your build can be challenging and needs to take into account ongoing maintenance costs over the years.
In contrast, a third-party product allows you to budget for the exact cost over time, providing a clear financial picture. It also gives you access to a continuously updated platform that your team doesn’t have to manage. This means you can focus your resources on core business activities rather than getting bogged down by system maintenance and updates.
Once you’ve landed on the solution that’s right for you, the next step is roll it out across your teams. But where do you begin? To effectively implement an AI-powered knowledge management system, a phased approach can help ensure a smooth transition, demonstrate early value, and maximize the benefits of AI. Here’s a strategic three-phase plan for your team. Note: we've tested and validated this approach with dozens of leading global SaaS companies. In short, it’s powerfully effective.
By proving the value of AI search in specific teams, you build a case for broader adoption and generate early wins that showcase the benefits of AI-enhanced knowledge management.
Automating workflows builds on the AI search implementation and demonstrates how AI can enhance productivity by reducing manual effort and making employees drastically more efficient.
Consolidating the tech stack reduces complexity and enhances the coherence of your knowledge management system, leading to a more integrated and efficient operational environment.
Adopting a phased approach allows your organization to demonstrate value early, expand functionality incrementally, and streamline technology integration over time. By starting with AI search, moving to automated workflows, and finally consolidating your tech stack, you can effectively leverage AI to resolve knowledge fragmentation and enhance the performance of your customer-facing teams. This structured rollout not only minimizes risks but also maximizes the potential benefits of your AI-powered knowledge management system.
If you buy into the three phased approach above, there are still a few additional areas to consider when rolling out a cohesive and robust strategy alongside your AI-powered knowledge management system. For GTM leaders, these factors are crucial to ensure a successful deployment and integration. Spearheading this initiative can position you as a key resource in AI adoption and knowledge management within your company.
Align AI with Business Objectives: Ensure that the AI solution you choose aligns with your broader business goals. Whether it’s enhancing customer satisfaction, reducing response times, or speeding up sales cycles, the solution you select should support these objectives. Clearly define the metrics for success in advance and communicate these goals to all stakeholders.
Engage Key Stakeholders: Involve representatives from different teams—Support, Sales, and Customer Success—in the selection and implementation process. Their insights will help tailor the AI tool to meet specific needs and ensure broader acceptance. Regular updates and feedback sessions will keep everyone aligned and invested in the project.
Focus on Change Management: Transitioning to an AI-powered system can be a significant shift for your teams. Develop a comprehensive change management plan that includes training programs, user support, and communication strategies. Addressing potential resistance and ensuring users are comfortable with the new system is critical for successful adoption.
Plan for Continuous Improvement: No AI system is a set-it-and-forget-it solution. Establish a process for ongoing evaluation and refinement. Collect feedback from users, track performance metrics, and stay updated on advancements in AI technology. Regularly updating and optimizing the solution will maximize its effectiveness and value.
Ensure Scalability: Consider the scalability of the AI solution. As your organization grows and evolves, your knowledge management needs will change. Choose a solution that can scale with your business and accommodate increased data volume, more users, and additional functionalities as needed.
Address Ethical Considerations: AI implementation should adhere to ethical standards. Ensure that the AI system operates transparently and fairly, avoiding biases that could affect decision-making or customer interactions. Maintaining ethical practices will enhance trust and credibility both internally and externally.
By thoughtfully addressing these strategic considerations, you’ll not only enhance the effectiveness of your AI-powered knowledge management system but also establish yourself as a go-to resource within your organization. Your proactive approach will demonstrate leadership and ensure a smoother transition to AI-enhanced operations, ultimately contributing to the overall success of your customer-facing teams.
Implementing AI-powered knowledge management systems also requires a focus on security to protect sensitive information and ensure compliance. There are likely individuals on your IT and legal teams that will have a strong say in what’s needed to adhere to your company’s standards, but having some baseline knowledge can further entrench you as a knowledgeable asset. Here’s what your legal and IT teams are likely thinking about:
Data Protection and Privacy: Your IT and legal teams will be focused on ensuring that the AI system complies with data protection regulations such as GDPR, CCPA, or HIPAA. This involves verifying that the system encrypts sensitive information both in transit and at rest, and that it has mechanisms in place for data anonymization and secure access controls. They’ll also ensure that the AI tool can handle data subject requests like data deletion or access, in line with legal requirements.
Compliance and Certification: AI tools should meet industry standards and certifications to ensure robust security practices. Your IT team will likely assess whether the AI provider complies with certifications such as SOC 2 Type II, ISO 27001, or others relevant to your industry. This helps in verifying that the AI vendor follows best practices in data security and privacy.
Integration Security: The integration of AI tools with existing systems must be handled carefully to avoid creating new security vulnerabilities. Your IT team will examine how the AI solution interfaces with other systems, ensuring that APIs and data exchanges are secure and that there is no risk of exposing sensitive data through integration points.
User Authentication and Access Control: Ensuring that only authorized personnel can access the AI system and its data is critical. Your IT team will focus on implementing strong authentication methods, such as multi-factor authentication (MFA), and role-based access control (RBAC) to manage permissions and prevent unauthorized access.
Data Handling and Governance: Effective data governance practices are essential for maintaining data integrity and compliance. Your legal team will be concerned with how the AI system manages data retention, storage, and disposal policies. They’ll ensure that the AI tool aligns with company policies for data handling and that there are clear procedures for managing data breaches or security incidents.
Vendor Security and Risk Management: Assessing the security posture of the AI vendor is crucial. Your IT team will review the vendor’s security practices, including their incident response protocols, regular security audits, and history of data breaches. Ensuring that the vendor adheres to rigorous security standards helps mitigate potential risks.
By understanding these key considerations, you can better navigate discussions with your IT and legal teams, ensuring that the implementation of your AI-powered knowledge management system aligns with your company’s security and compliance requirements. This knowledge not only reinforces your role as a key stakeholder but also helps in making informed decisions that protect your organization and its data.
After each key phase of implementation, it’s critical to assess how it’s going. Some of the assessment will depend on your company’s unique goals, but these are the common KPIs we see across many of the customer-facing teams at organizations that we work with:
For qualitative KPIs, your AI team can collect feedback from both employees and customers to gauge their overall satisfaction with the new system and the amount of collaboration it’s fostering. While these can be more difficult to assess, they can also provide unique insights into the overall impact AI is making on the company culture surrounding knowledge management. If your teams are feeling more capable and confident since they started using AI for knowledge management, that’s a good sign that things are moving in the right direction.
In conclusion, as a GTM leader, your role in implementing AI for knowledge management is essential to unlocking your company's full potential. Developing a thoughtful strategy and adhering to the three-phased approach—starting with AI search, expanding with automated workflows, and consolidating your tech stack—will enhance your team's efficiency and effectiveness. By methodically proving value, scaling capabilities, and integrating systems, you’ll drive faster decision-making and more streamlined operations. This strategic focus positions you as a key driver of innovation, enabling your organization to harness AI’s benefits effectively and stay ahead in a competitive landscape.
About Ask-AI
Ask-AI is dedicated to support global companies implement and operationalize AI across their organizations. Our universal AI platform provides GTM teams with instant access to the information they need to double their work output. To learn more, visit our website or request a demo with one of our AI experts.
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