\”According to the Ark Invest Big Ideas 2025 report, software agents are expected to enhance enterprise productivity. Companies utilizing agents can potentially increase output with their existing workforce and streamline operations towards more valuable tasks. Artificial intelligence (AI) is also projected to revolutionize knowledge work, with the amount of software used per knowledge worker expected to rise significantly by 2030 as businesses invest in productivity solutions. AI agents are anticipated to drive the adoption of digital applications and transform human-computer interaction.
The 2025 Connectivity Benchmark Report by MuleSoft and Deloitte Digital reveals that 93% of IT leaders plan to implement autonomous AI agents within the next two years, with nearly half already doing so. However, the transition to agentic AI adoption and a digital workforce will require a focused strategy on data and knowledge management. The research highlights the challenge of data segregation across various enterprise applications, hindering IT teams’ ability to create a cohesive experience. Only a fraction of enterprise applications are integrated and share information across the organization, prompting the need for improved data management practices.
Disconnected data poses a significant obstacle to legacy modernization efforts for many organizations, with integration challenges cited as a major barrier. The struggle to integrate end-user experiences and manage data silos continues to plague IT leaders. The importance of effective knowledge management in facilitating AI adoption and creating a digital labor force is emphasized by Accenture research.
To delve into the impact of knowledge management on the successful adoption of AI in the enterprise, I spoke with Michael Maoz, an expert in CRM, CX, KM, and customer service. Maoz highlighted the critical role of knowledge management in leveraging AI technologies, emphasizing the need for abundant, clean, and accessible data to support initiatives like Agentforce.
Successful companies excel in developing and delivering content in various formats while ensuring easy access for customers. Strong knowledge management cultures, centralized knowledge creation processes, and integrated systems play a vital role in content delivery. Companies like Amazon’s Ring division and Dyson prioritize contextually relevant documentation across channels to enhance customer experience and reduce support costs.
The advent of Gen AI and agentic AI underscores the importance of high-quality knowledge content in addressing customer issues. Maoz stressed the need to de-risk Gen AI projects by applying rigorous data governance practices and ethical considerations to ensure accurate and unbiased content delivery. Scaling AI initiatives to incorporate structured and unstructured data requires strong data governance and privacy measures to mitigate risks and ensure ethical use of AI technologies.\”