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How will artificial intelligence integrate insights into marketing and business operations in the future?
In the ever-evolving landscape of marketing and operations technology, the integration of various artificial intelligence (AI) systems holds the promise of revolutionizing how organizations harness market and consumer insights. But what does the future hold for the collaboration of different AI technologies in fuelling businesses with fresh insights? Let’s explore this paradigm shift and its implications.
Instead of viewing insights management as a one-way handover of market insights to business leaders, tomorrow’s intelligence strategists will weave insights into everyday operations. Taking this step involves seamless integration across assets, systems, processes, and interventions: all governed and validated by insights leaders in real-time. It’s a significant shift in thinking and scope that necessitates collaboration between insights leaders and IT leaders to understand how the underlying business architecture supports the transfer of knowledge through the company.
As you embark on this journey together, however, IT and insights leaders will need to adapt today’s business architecture map to one that is embracing AI end to end. Now is the time to re-imagine the systems landscape and lay the foundations for collaboration between multiple AI systems.
The Role of Natural Language Processing in understanding Market Knowledge at scale
Generative AI, empowered by understanding of natural language, simplifies human-to-machine interaction by mimicking human expression and understanding. This shift enables AI technology to convey information in a manner that resonates with human users, streamlining the integration of AI insights into operational workflows. Organizations should prioritize AI solutions with proficient NLP capabilities to ensure accurate interpretation and communication of insights.
In Market Logic’s vision for an AI for insights-powered enterprise architecture, Natural Language Processing (NLP) plays a central role. NLP processes qualitative and, via natural language generation, quantitative market data, translating opinions and patterns into actionable insights. It serves as the translation tool for diverse data sources, facilitating communication between various AI systems and human collaborators.
In a future scenario, generative AI platforms for insights might interact with generic tools such as Microsoft Copilot, to streamline the transfer of market knowledge and data between systems and onto human users. This AI-to-AI collaborative approach could significantly speed up processes and deliver comprehensive outputs based on real-time knowledge and trends.
Ensure your specialist AIs can work as a team
You can expect the new crop of emerging AIs emerging to leverage specialized skills. So, in the same way as you hire individuals based on specific expertise you’ll be hiring ‘teams’ of specially trained AIs.
As you evaluate the benefits of each AI technology in driving improvements within a defined function, take time to consider how this could connect to another step in the process. For example, creating a direct connection between insights discovery and campaign concept generation. Seek out new opportunities for market knowledge and insights to be brought into a wider range of decision points.
At a systems level, standard integrations with common business tools such as Microsoft 365 and Google Workspace will be key in ensuring your business users can tap into specialist support as part of their day-to-day work routine. Add to this open and easy to use APIs (application programming interfaces) between systems and you unlock the door to combining the power of AIs working together. For example, connecting our DeepSights™ AI for insights assistant to Salesforce allows a company’s pet food sales data to be incorporated alongside market data tracking the rise of pet-centred social posts in an AI generated summary report exploring reasons for growth in dog ownership amongst gen Z. Whereas, manual stitching together or that same report might take several hours at best, with AI that time will shrink to seconds.
Orchestrating the new flow of AI-powered data
As you reinvent the new systems architecture for the future flow of insights through your systems, take time to consider how each AI understands and interprets the data it is handling. To avoid an unintended misunderstanding or misinterpretation of the data being shared between AI’s, a central orchestration point for content understanding will need to be introduced. Is all the data being transmitted from one AI to another contextually relevant to the end output? Are all data points clearly drawn from approved and trusted sources? Without an orchestration strategy to monitor insights content quality at scale, speed of AI-assisted operations will not deliver on its business potential. Harnessing AI to create a more systematic method for using insights to drive better decision making, will depend on placing strong data governance at the heart of your new strategy.
Learn more about how to become an AI insights powered enterprise in our free guide for business and insights professionals.
