Reimagining The Enterprise with AI
The Advent of AI
While AI has been riding the peaks of the hype cycle for a few years, businesses have struggled to adapt and implement AI at scale. Recent advancements, such as the launch of the hugely popular service, ChatGPT, has made AI instantly relatable and captured the imagination of millions the world over. This presents the businesses with the perfect opportunity to harness the predictive powers of AI, something even ChatGPT waxes poetic about.
“With AI at their side, the enterprise grew,
Their profits soared, because now they knew,
With technology they could predict and see,
What’s to come and how it will be.”
-A poem about AI in the Enterprise by ChatGPT.
AI and Anticipatory Decision-Making
But generative AI can do a lot more than produce cutesy poems. The new generative AI capabilities of Large Language Models are now being trained on the corpus of a company’s data infrastructure, enterprise systems, machine learning models, and AI applications to help decision-makers anticipate the future and improve the quality of their decisions.
For instance, a business leader could ask, “What’s the most likely sales forecast for this quarter and what can we do to improve it?” The generative enterprise AI application would:
- Understand the intent of this search query,
- Acquire data from internal systems such as CRM and ERP as well as external sources such as econometric data and equity prices,
- Use machine learning models to assess the likelihood of each deal closing and accordingly estimate the likely gap to forecast plan,
- Explain the largest drivers of the gap-to-plan,
- And make recommendations on best actions to close the gap-to-plan.
Similar generative enterprise AI applications will be able to address key leadership questions about expected supply chain risks, asset performance, or carbon emissions by pulling the required data and using relevant AI models for generating forward-looking answers, complete with explanations and action recommendations.
