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Transforming business decisions with PrecAIse

How can a user-friendly AI tool revolutionise decision-making by transforming complex data into actionable insights through a simple, conversational interface?

In the fast-paced world of business, making informed decisions is crucial. However, companies often struggle to navigate complex data for strategic decision-making. This is where PrecAIse (pronounced “precise”), an AI-powered tool, steps in to revolutionise business decision-making. Designed to help organisations utilise data effectively without needing extensive technical expertise, PrecAIse is a prescriptive AI solution that empowers users with actionable insights through an intuitive conversational interface.

What is prescriptive AI?

Before diving into how PrecAIse works, let’s first understand “prescriptive AI”. Unlike traditional analytics, which explains past events (descriptive analytics) or predicts future outcomes (predictive analytics), prescriptive AI goes a step further. It not only predicts outcomes based on cause-and-effect relationships but also recommends specific actions to optimise them. For example, instead of merely showing trends or predicting behaviours, PrecAIse recommends specific actions to achieve desired outcomes, like improving marketing campaign response rates while staying within budget.

Why do businesses need PrecAIse?

Despite its potential, prescriptive AI faces several barriers in real-world business environments. Many organisations lack the specialised knowledge to harness advanced AI models, such as causal machine learning and optimization. Unlike marketing managers or sales leaders, employees in non-technical roles often find it difficult to interpret complex AI-driven recommendations. PrecAIse bridges this gap, making AI accessible to everyone in the organisation.

Imagine a marketer asking, “Who should we target for our next campaign to achieve the highest conversion rate without exceeding our budget?” Using its conversational interface, PrecAIse can analyse historical campaign data, assess strategy effectiveness, and recommend the top customer segments to contact, ensuring a highly efficient and impactful campaign. The user can also ask “what-if” questions to explore different strategies, evaluate various metrics, and fine-tune the policy based on different constraints, making it easy to adapt the campaign to specific goals and resources.

Key features of PrecAIse

The core of PrecAIse combines advanced AI tools for causal analysis and optimization with a natural, accessible interface. At its heart, PrecAIse is powered by a large language model (LLM) conversation agent, serving as the “face” of the system, allowing users to interact with complex AI models through simple, intuitive language. Behind the scenes, the “brain” of PrecAIse consists of rigorously developed tools that perform detailed causal analysis and optimization, ensuring robust and accurate recommendations. This combination enables PrecAIse to deliver the precision of advanced analytics. At the same time, the LLM interface adds the flexibility and accessibility of natural language, making it a powerful yet user-friendly solution for businesses seeking data-driven insights.

Causal analysis

Understanding cause-and-effect relationships in data is one of the biggest challenges businesses face. Traditional data science approaches often rely on correlations, but correlation does not imply causation. For instance, if a company experiences a drop in sales, it may be unclear whether this is due to a recent marketing campaign, seasonal variation, or a competitor’s actions. PrecAIse’s causal analysis tools help businesses go deeper by estimating the impact of specific actions on key outcomes like sales or customer satisfaction. This enables companies to make informed, data-driven decisions based on what drives their performance.

Policy learning

PrecAIse leverages causal analysis to explore high-fidelity counterfactual predictions, providing insights as if looking through a crystal ball into parallel universes where different actions are applied. PrecAIse’s optimization engine evaluates how different actions—such as marketing campaigns, pricing strategies, or special offers—would impact individual customers in each of these alternate scenarios. By observing outcomes across these virtual scenarios, the optimization engine selects the most effective strategy for each customer segment, customising recommendations to maximise impact while meeting real-world constraints. The final learned policies are presented as an interpretable “prescriptive tree,” allowing businesses to visualise and understand the logic behind each decision. This approach empowers companies to craft data-driven strategies with unprecedented precision and clarity.

Conversational interface for easy interaction

A standout feature of PrecAIse is its “generative” user interface, powered by large language models (LLMs), which enables non-technical staff to interact with the AI using everyday language—no technical expertise required. By relying on open-source LLMs, PrecAIse ensures cost efficiency and transparency, making it a viable option for a wide range of businesses.

For example, a marketing manager can explore “what-if” scenarios, such as “What if we increase our reach to a younger audience?” PrecAIse can then simulate outcomes and provide insights, helping the team adjust strategies on the fly. The generative UI goes beyond simple text responses, offering a robust NLP interface along with interactive displays, including figures, plots, and other visualisations, making complex data both accessible and actionable.

Why PrecAIse is a game-changer

PrecAIse brings a few key innovations to the table:

  • Accessibility: Using natural language processing, PrecAIse enables users without a background in data science to benefit from AI. This democratisation of technology allows for broader adoption within an organisation, making AI-driven insights part of everyday decision-making.
  • Adaptability: PrecAIse is adaptable across various industries and use cases. Whether for campaign optimization, retail pricing, or resource allocation, PrecAIse can be tailored to meet the specific needs of any business.
  • Transparency and trust: One main reason businesses hesitate to adopt AI is the “black box” problem—AI recommendations are often hard to understand. PrecAIse addresses this by clearly explaining its recommendations, helping build trust among users.

The future of business decision-making

As AI continues to evolve, tools like PrecAIse are set to redefine how companies approach decision-making. By providing easy access to powerful prescriptive insights, PrecAIse empowers businesses to make data-driven decisions quickly and confidently. It bridges the gap between technical teams and business users, fostering a collaborative environment where everyone can leverage the power of AI.

In a world where making the right decision at the right time is essential, PrecAIse offers a competitive edge. Whether you’re in marketing, operations, or finance, PrecAIse opens up a new realm of possibilities, making sophisticated AI tools accessible and actionable for everyone in the organisation.

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Journal reference

Sun, W., McFaddin, S., Tran, L. H., Subramanian, S., Greenewald, K., Tenzin, Y., … & Ettl, M. (2024). PresAIse, a prescriptive AI solution for enterprise. INFOR: Information Systems and Operational Research, 1-17. https://doi.org/10.1080/03155986.2024.2383095

Wei Sun is a senior research scientist at IBM Research, working under the MIT-IBM Watson AI Lab. Her research focuses on the intersection of machine learning and optimisation, including data-driven policy learning, constrained prediction, and causal inference. Her work has been applied across industries such as digital marketing, transportation, and financial services. Wei has published over 40 technical papers and holds more than 20 patents. She earned her PhD in Operations Research and MSc in Computational Design and Optimisation from MIT, as well as a First-Class Honours BEng in Electrical and Computer Engineering from the National University of Singapore.

Kristjan Greenewald is a research scientist at the MIT-IBM Watson AI Lab. He earned his PhD from the University of Michigan in 2017, focusing on signal processing and machine learning, and subsequently served as a postdoctoral research fellow in the Statistics Department at Harvard University before joining IBM Research in 2018. His research interests include optimal transport, causal inference, and statistical learning theory, with recent applications in LLM evaluation and alignment, uncertainty quantification, differential privacy, and agentic frameworks.