Artificial intelligence transforms knowledge work permanently. It’s a human change that not only enhances our work but also makes it more meaningful. Gofore’s principal AI consultant Pasi Lehtimäki shares five viewpoints for using AI and data in organisations.
Data management is crucial
In the utilisation of artificial intelligence, data management remains paramount. Generative artificial intelligence can handle almost any topic at a general level. However, the greatest benefit in organisational processes is achieved when artificial intelligence is provided with the organisation’s own data as input or when it’s trained with the organisation’s own data. In such cases, data management, consolidation, quality, and integrations become crucial.
When organisations have control over data management processes, they can leverage the full potential of artificial intelligence. AI assists in processing stored data but also aids in the data collection process itself, which includes data collection, validation, quality assurance, classification, enrichment, and metadata.
Integrate AI into core processes and systems
Microsoft 365 Copilot and generative AI tools can assist in daily tasks, but a leap in organisational efficiency requires the utilisation of artificial intelligence in processes covering multiple employees, stages, and core systems.
Utilising AI in core processes necessitates data management and potentially the implementation of tailored solutions.
Organisations experimenting with their own generative AI-based applications and transitioning them into production is currently very popular. These experiments help to harness the potential and benefits of AI gradually and with minimal risk.
Enable personalised services efficiently
Data and AI have long enabled the identification of individual customer needs and tailored service delivery. However, this has typically relied on structured, numerical, or categorical data.
Generative AI and its ability to understand textual data enable the development of personalised services based on non-structural textual data. In other words, more personalised service delivery and process automation can be achieved simultaneously.
Ensure data security throughout the data processing stages
If confidential information is processed with AI, potential security and privacy risks also increase. Therefore, it’s crucial to ensure secure data usage and compliance with legal frameworks when utilising AI.
AI governance models help define processes where data handling is secure and legal requirements are met.
The rapid development of AI capabilities has led to the rapid redefinition of guidelines and processes. While organisations want to leverage AI, secure processes must be defined first to avoid overreaching.
AI governance models help define the processes within an organisation that enable both bold experimentation and the implementation of security and privacy measures under all circumstances.
Move from reacting to forecasting
The rapid advancement of generative AI has sparked new interest in AI. However, when examining bottlenecks in organisational processes, it’s often found that solving the problem doesn’t necessarily require the use of generative AI or textual data analysis.
A significant portion of organisations’ data processing problems can still be solved using traditional statistical methods or machine learning. One of the most typical use cases is forecasting phenomena or development over time.
Implementing time forecasts allows for anticipating phenomena, adjusting operations, or fixing issues before they even occur in the process.
Utilising AI in various situations requires broad expertise in various intelligent algorithms, enabling the selection of the most appropriate ones for each situation.
Is your organisation considering how to make knowledge work more meaningful and efficient using generative artificial intelligence?
