Managers and company executives are under more and more pressure to make moral choices at work. According to estimates, bad decisions cost businesses at least 3% of their income on average. For a $5 billion company, it means losing about $150 million annually. Poor decision-making is not limited to producing financial consequences such as mishandled social media; delayed shipments, and IT failures among others, that can break up a reputation as well, with high costs attached.

Greater use of AI to close ‌data – insight gaps and aid in decision-making in time-bound situations is being adopted by more companies. Virtual assistants, AR/VR, task mining, analytics, and BI platforms are key tools in this category. The Oxford Training Center’s AI for Decision-Makers course aims to equip corporate decision-makers, executives, and business leaders with the knowledge and abilities necessary to apply artificial intelligence in business planning.

What is decision-making?

A ‌process known as decision-making is employed by individuals or organizations to consider the various available options for formulating the best approach to a given problem or situation. It includes the decision-making process, collection of data, consideration of solutions and selection of the best option, implementation, and evaluation of outcomes. Strategic use of a process-oriented approach minimizes risks and maximizes outputs, which are prime in business for making a difference.

Decision-making is important in business since it influences long-term viability, resource allocation, and organizational goals. Stakeholder collaboration is often required for effective decision-making, which may take the form of authoritarian (one decision-maker), consultative (consults others), and democratic (agreement by the group). Whether the decision is routine or incorporates high-impact, unclear aspects can affect how complicated the process is. 

How can AI systems improve decision-making?

Virtual role-playing to train employees in realistic business scenarios, real-time tracking and enhanced prediction of on-the-ground business developments, and emerging generative AI tools that can respond to inquiries and serve as advisors and virtual “sounding boards” for decision-makers are the three main ways that AI-powered technologies can promote quicker and better decision-making.

1. Better tracking and forecasting

Businesses may now determine the origins of their raw materials and inputs, who created or supplied them, and if these inputs were manufactured and sourced in an ethical and environmentally responsible manner through technological supply chain tracking that provides ever-more-detailed data. AI-enabled technologies are also being used by seaports to better coordinate and expedite decisions, enhance operational efficiency, and lessen their negative effects on the environment. Every day, harbor managers must make thousands of decisions, such as arranging for just-in-time ship arrivals, figuring out acceptable water levels, controlling container traffic volumes and flows, making sure terminals have enough space for loading and unloading, issuing safety alerts, and more. There is little room for error, and AI can help prevent mistakes.

2. Virtual role-playing under actual circumstances

AI-powered solutions are already being used by many industries to give managers and employees the ability to make decisions in a range of business situations, both expected and unforeseen. Dealing with challenging, sensitive, or irate clients is possibly the most taxing experience for rookie call center workers. Utilizing Strivr’s virtual reality technology, the massive US telecom company Verizon immersed trainee customer representatives in virtual worlds where they could switch roles with customers and view problems from their point of view. 

3. Establish trust between humans and machines

There are several risks and difficulties even if AI systems are being utilized more and more to supplement human decision-making and even replace it in some cases. These dangers include, but are not limited to, possible bias, ethical transgressions, data-provenance issues, and accuracy. They also bring up some important issues for companies that spend money on this kind of technology. 

Best AI tools for decision-makers

To facilitate quicker, better-informed, and more scalable decision-making, the top AI solutions for decision-makers in 2025 integrate sophisticated data analysis, automation, and natural language processing. Based on current expert ratings and market knowledge, these are some of the best choices:

1. DataRobot

One of the top AI decision-making platforms, DataRobot, automates every step of the machine learning process, from data preparation to model deployment. It provides explainable AI features that ensure openness and trust, particularly in regulated industries, by assisting decision-makers in understanding how predictions are formed. It is perfect for enterprise-level decision assistance because of its scalability and security.

2. Domo

Domo is an AI-enabled end-to-end data platform that improves data discovery, cleaning, and visualization. In addition to pre-built AI models for sentiment analysis and forecasting, it has an intelligent chat interface that lets customers ask queries in natural language regarding their data. Decision-makers can now more easily obtain relevant insights without requiring extensive technical knowledge.

3. Cognos Analytics by IBM

In combination with AI-powered automation, IBM Cognos Analytics facilitates dashboard and reporting creation by ‌customers. Its AI assistant allows the ability to make data-driven decisions and helps to generate visualizations and find trends. With all its strengths, it has a steeper learning curve and is ideal for businesses that demand good analytical skills.

4. AnswerRocket

Business users may ask questions in simple English with AnswerRocket and receive quick, AI-driven answers. Because of its superior performance reporting, sales analysis, and forecasting capabilities, decision-makers without technical expertise can use it. However, in contrast to larger platforms, it provides fewer sophisticated features.

5. Claude and ChatGPT

ChatGPT and Claude offer context-aware answers, document summarization, and knowledge retrieval for conversational AI and research support. Decision-makers may quickly assimilate complex information, prepare reports, and brainstorm with the aid of these tools. Claude is a good substitute for delicate business settings since it places a great emphasis on security and privacy.

6. The Notion AI 

Concept By prioritizing work, organizing notes, and making AI-powered recommendations, AI improves productivity. Incorporating decision assistance into routine tasks, it assists decision-makers in more effectively managing projects and workflows.

How to select an AI tool for customer insights and decisions?

A systematic strategy that matches the capabilities of the tool with your business goals is necessary when choosing the best AI solution for consumer insights and decision-making. Based on professional advice and current market research, the following are important actions and factors to take into account:

1. Define your business objectives and use cases

To begin, clearly define your goals for using AI-driven customer insights. Do you want to improve product development, minimize churn, use your marketing efforts to the best advantage, segment your audience, or measure consumer sentiment? Different technologies specialize in different tasks, such as behavior monitoring (e.g., Mouseflow), conversation analytics (e.g., Gong.io), and customer success prediction (e.g., ChurnZero). 

2. Review data sources and integration abilities

Make sure the solution can swallow up and process all of the various forms of client data you have, for instance, surveys, support tickets, sales calls, site behavior or data, social media, or CRM data. For deep insights, multichannel customer data needs strong data integration & real-time analytics, which is offered by tools like Synerise & mParticle. For smooth workflows, integration with your current software stack, which includes CRM, marketing platforms, and communication tools, is essential.

3. Assess AI features and analytical depth

Seek out artificial intelligence (AI) products with sophisticated capabilities like automated reporting with useful suggestions, predictive analytics for predicting consumer behavior, and natural language processing (NLP) for sentiment and theme analysis. For instance, Crescendo.ai provides real-time sentiment analysis and ticket classification, while Insight7 is excellent at analyzing qualitative data from surveys and interviews. In addition to analyzing data, the technology should produce understandable, useful insights.

4. Examine scalability and pricing

The cost structures of AI tools vary greatly; some offer unique enterprise pricing, while others charge by ‌resolution or user. Consider your anticipated level of use and your budget. For instance, corporate solutions like Synerise require unique bids, whereas Crescendo charges about $2.99 per resolution. Think about future expansion and whether the tool can grow with your company without incurring unnecessarily high costs.

5. Verify vendor security and support

Make sure the AI supplier complies with industry-specific data protection and security regulations and provides dependable customer service and training materials. This is particularly crucial when working with private client information. Strongly regarded vendors with open and honest policies lower risks and increase the success of implementation.

Establishing your precise objectives and data sources is the first step in choosing the right AI solution for consumer insights and decisions. Next, assess products according to their AI skills (predictive modeling, sentiment analysis), simplicity of integration, usability, cost, and vendor support.