Guest contributor |
Since the emergence of generative AI businesses have been eager to harness the power of the new technology. If used successfully, AI can be a powerful tool for transforming how organisations manage, analyse and leverage their information assets. However, successful implementation requires a holistic approach that goes beyond simple technology adoption.
One area which demonstrates the need for a balanced strategy are the productivity applications of Microsoft 365. These apps – including Word, PowerPoint, Excel, Teams, SharePoint and more – are used at all levels of most organisations around the world and are simple on an employee and team-wide level. They are, however, only a collection of tools. Businesses still require a strategy to oversee their effective use, and without a clear information management plan, a company of a certain size can accumulate hundreds or even thousands of data silos across different libraries, sites and channels. This makes valuable knowledge harder to find and leverage.
AI can help solve the problem of information chaos. Large language models can harvest insights from vast amounts of data and save a tremendous amount of time that workers would otherwise spend searching for information. However, the accuracy of this process is affected by the quality of the data. If none of the content is classified and governed, the AI won’t be able to tell the difference between a poorly written draft and a carefully crafted piece of knowledge from a subject matter expert.
For a generative AI app to be successful in addressing information chaos, it requires well-curated source content to build a robust index and a user prompt bolstered by additional contextual information. Optimally, the prompt offers an explicit context, such as limiting the answer to a specific type of project plan documents.
Implementing generative AI for information management therefore requires more than just technological solutions. Businesses must assess their readiness and establish proper governance structures.
The first step should be to develop a clear strategy for information management, knowledge utilisation and AI integration. This should align with overall company objectives and clearly articulate how generative AI will contribute to achieving these goals. Company leaders must understand the importance of managing information, and key stakeholders from various departments should be involved in defining this strategy to ensure it addresses their diverse needs and concerns while fostering a sense of ownership across the company.
Next, the company should establish and enforce effective data governance processes. Ideally, these should be automated to ensure consistency and compliance. Clear policies should be in place for data retention, organisation, classification and access control. One aspect to consider during this step is deciding how to determine the relevance of data for AI processing, evaluating the potential value of different data types alongside the legal and ethical considerations of using them.
Companies should also assess the ability of their current IT infrastructure to support the content strategy and governance requirements they’ve developed. It is essential to ensure that systems and integrations can facilitate connectivity, confidentiality and curation. When evaluating generative AI solutions, consider factors such as vendor trustworthiness, pricing models and overall cost-benefit analysis. It’s important to choose solutions that integrate well with existing systems and can scale to meet future needs.
The business should then take action to assess the current level of automation in their content processes and identify opportunities for expansion. The goal should be to progress from basic automation focused on consistency and compliance to more advanced apps that support decision-making and provide real-time insights. Implementing generative AI solutions often involves reimagining existing processes entirely, and organisations should look for opportunities to transform how work is done and how value is created.
The final step is to evaluate how well current content capabilities support end users across different functions and industries. Organisations must consider the varying needs of different user groups and ensure that AI enhances, rather than complicates, their work processes. Assessing the company’s change management capabilities and end users’ readiness to adopt new tools is equally important, as successful implementation often requires significant changes in work practices and mindsets. Businesses should invest in training programmes, create champions within different departments and develop a communication strategy to build enthusiasm and address concerns about AI adoption.
Businesses can position themselves to reap the full benefits of AI by following this framework and addressing the foundational elements of information management. The journey to effective AI implementation may be challenging, but the potential rewards – including enhanced productivity, improved decision-making and unlocked innovation – make it a worthwhile endeavour for forward-thinking leaders across all industries.
As we move further into the era of AI-augmented information management, the companies that navigate this transformation successfully will be well-positioned to thrive in an increasingly data-driven business landscape.
Ville Somppi is senior vice president of industry solutions at M-Files
Discover more insights like this in the Winter 2024 issue of Technology Record. Don’t miss out – subscribe for free today and get future issues delivered straight to your inbox.