AI, the Shift from Content Generation to Decision Making
It was in November 2022 that OpenAI unveiled ChatGPT, a chatbot powered by a large language model called Generative Pre-trained Transformer. This innovative chatbot allowed users to steer and refine conversations according to their desired length, format, style, and even language.
As we look back at the past year of AI advancements, it begs the question: where is artificial intelligence heading in the next year or two? With the leaps and bounds we’ve seen in AI development, it’s clear that the next step involves a shift from content creation and analysis to decision-making, possibly incorporating workflow automation.
One notable prediction is the transition of generative AI’s backbone, the Large Language Model, towards a more domain-specific language model. Zuko Mdwaba, Salesforce Area VP / Africa Executive, highlighted the estimated time-saving benefits of generative AI in desk work, suggesting that it could save individuals approximately five hours per week in the future.
Mdwaba further explained that chatbots and virtual assistants will simplify the employee experience by automatically booking appropriate spaces for team needs. AI will also provide quick responses to inquiries, guide employees to resources, and facilitate service requests.
Moreover, businesses are projected to transform the way they measure performance and productivity, shifting their focus from inputs to outcomes. Mdwaba anticipates an increase in the adoption of semantic query capabilities, leveraging structured data such as sales figures and customer demographics, as well as unstructured data like blogs and social commentary.
According to Mdwaba, data volumes are expected to rise by an average of 23% over the next 12 months. To stay ahead of the competition, teams are racing to ensure the quality of the data underlying their generative AI initiatives. This entails investing in technical solutions that harmonize data sources while reducing data gravity and paying closer attention to defining data governance protocols and cultivating strong data cultures.
Mdwaba emphasized that the ethical and transparent use of generative AI could set companies apart in customers’ minds. This points to the importance of navigating the evolution of AI with a focus on ethical transparency and collaboration between users and AI systems.
So, what does all of this mean? AI is evolving at a relatively fast pace, and content generation and analysis are set to shift towards more solid decision-making in the following ways:
1. Refined machine learning models: AI will utilize more complex patterns and a better overall understanding of data to generate nuanced content. Think of Elon Musk’s AI companion, Grok, which exhibits personality when responding.
2. Enhanced natural language processing: Future AI models will have advanced capabilities to understand human-like language, bringing contextual understanding to their responses and interpretations of human communication subtleties.
3. Integration into business processes: AI will play a more significant role in decision-making within businesses. Collaboration between users and AI systems will be crucial for seamless interaction, with any resistance potentially hindering technological momentum and growth.
The continued growth of AI promises more sophisticated decision-making capabilities and increased integration into business processes. Transparency and collaboration are key in ensuring ethical and responsible AI development.
As AI advances, it is essential to focus on the ethical implications and transparency surrounding its use. By understanding and embracing these principles, AI has the potential to greatly enhance our lives and drive innovation in various industries worldwide.
