Top 10: AI Tools for Business
AI tools are fundamentally changing how organisations operate.
By enhancing productivity, refining customer experiences and accelerating data-driven decision-making – AI tools are having a collective impact on global business operations – from intelligent search and conversational AI to advanced analytics and comprehensive machine learning (ML) platforms.
As enterprises monitor this shift towards practical, integrated AI solutions that deliver demonstrable return on investment and competitive advantage, AI Magazine highlights some of the top AI tools for business in the world.
10. Microsoft Power BI
Capabilities for business: Business intelligence and data visualisation, providing real-time data analysis and observations for informed decision-making
CEO: Satya Nadella, CEO of Microsoft
Launched: 2015
Microsoft's business intelligence platform has evolved from its 2011 origins as “Project Crescent” to incorporate Gen AI capabilities through Copilot integration.
It processes data from multiple sources to create interactive dashboards for real-time analysis – a large-scale example of its capabilities are proved through Heathrow Airport deploying real-time passenger flow monitoring.
The platform allows non-technical users to generate reports using natural language prompts, reducing the time required to extract business intelligence – and Microsoft is continuing to develop AI features that complement rather than replace traditional BI functions, making data analysis accessible to broader organisational roles.
9. Amazon SageMaker
Capabilities for business: Full-cycle ML workflows, enabling developers and data scientists to create, train and deploy ML models on the cloud
CEO: Matt Garman, CEO of Amazon Web Services (AWS)
Launched: 2017
In November 2017, AWS launched SageMaker to address the ML workflow from data preparation through model deployment.
The cloud-based platform is unique in its ability to provide tools for developers and data scientists to build, train and deploy ML models at scale and includes features such as SageMaker Pipelines for automation and Model Registry for artefact management.
Cisco transformed its ML deployment in two weeks through SageMaker, whilst AstraZeneca accelerated drug discovery processes by reducing manual workload and Rocket Mortgage reported efficiency gains in ML operations.
8. Google Cloud AI Platform / Vertex AI
Capabilities for business: Integrating AI into cloud services and end-to-end AI deployment, offering a comprehensive suite for building, training and deploying ML models
CEO: Thomas Kurian, CEO of Google Cloud
Launched: 2021
Google’s comprehensive AI suite launched with App Engine in 2008 and evolved to include Vertex AI for managed ML.
The platform integrates with Google Cloud services including BigQuery and offers AutoML capabilities alongside Vertex Pipelines – spanning retail, financial services, healthcare and manufacturing sectors.
From the tool, healthcare provider Sami Saúde achieved 13% productivity gains and AES reduced energy safety audit costs by 99%.
7. ThoughtSpot
Capabilities for business: Real-time, AI-powered business analytics, enabling intuitive, data-driven decision-making for all users
CEO: Ketan Karkhanis, CEO of ThoughtSpot
Launched: 2012
Founded in 2012 by Ajeet Singh and team, ThoughtSpot addresses the bottleneck created by traditional BI tools requiring SQL expertise.
The platform enables natural language queries against data sources, generating instant visualisations through its “Spotter” AI Analyst.
Users can ask questions in plain English rather than writing complex database queries – and the system connects to cloud platforms whilst eliminating data silos through seamless analytics integration.
Business users across sales and marketing functions gain self-service access to real-time insights without depending on data teams through the tool.
6. Glean
Capabilities for business: Enterprise knowledge search and AI-powered answers, eliminating knowledge silos and reducing time spent searching for internal information
CEO: Arvind Jain, Founder & CEO
Launched: 2021
Launched in 2021 after two years in stealth mode, Glean addresses knowledge fragmentation across enterprise SaaS applications.
Founder and CEO Arvind Jain identified the problem during his tenure at Rubrik, where employees spent excessive time searching for information across cloud applications.
The platform’s abilities include searching over 100 workplace tools including Google Drive, Slack, Jira and Notion whilst maintaining permission structures – and creates a universal search layer that transforms information retrieval from human expert dependency to self-service knowledge access.
An example of the tool’s capabilities is shown through Super.com saving 1,500 hours monthly and accelerating employee onboarding by 20%. Additionally, Deutsche Telekom deployed an employee concierge serving 80,000 staff members for IT and HR requests.
5. Databricks Lakehouse Platform
Capabilities for business: Unifying data lakes and data warehouses for comprehensive data engineering, ML and analytics
CEO: Ali Ghodsi, Co-founder and CEO
Launched: 2013
The lakehouse architecture emerged in the late 2010s as enterprises sought single platforms for structured and unstructured data – but the platform was founded in 2013 by Apache Spark creators from UC Berkeley.
The unified approach eliminates data movement between disparate systems, reducing costs and complexity whilst enabling both business intelligence and machine learning workloads on consistent infrastructure.
The platform combines data lake flexibility with warehouse reliability through Delta Lake's ACID transactions and Unity Catalog governance.
Adobe uses the platform to unify data and AI at scale, whilst Rolls-Royce Civil Aerospace monitors jet engines in real-time.
4. IBM Watson Assistant
Capabilities for business: Conversational AI applications, particularly for designing, developing and deploying AI-powered chatbots and virtual assistants
CEO: Arvind Krishna, Chairman and CEO of IBM
Launched: 2011
IBM unveiled Watson in 2011 as part of the DeepQA project led by David Ferrucci.
The Assistant focuses on conversational AI applications with natural language understanding for complex multi-turn conversations – and integrates with messaging and CRM channels whilst providing analytics dashboards for performance monitoring.
The platform demonstrates specialisation over general-purpose models, providing business-specific conversational AI that handles enterprise requirements for reliability, context awareness and measurable performance in customer-facing applications.
Sicredi improved customer service operations and Technology Dynamics reduced customer care delivery time from months to weeks with the tool.
Furthermore, beyond chatbots, Watson applications include fraud detection, risk assessment and predictive maintenance.
3. Salesforce Einstein AI
Capabilities for business: CRM-integrated AI insights, enhancing customer relationship management, sales and service
CEO: Marc Benioff, Chairman & CEO of Salesforce
Launched: 2016
Launched in September 2016, Einstein AI integrates predictive analytics and ML into Salesforce’s CRM ecosystem.
Einstein enables hyper-personalisation through tailored content recommendations and send-time optimisation for marketing campaigns. It analyses customer data including purchase history and browsing behaviour to predict needs and preferences. Additionally, it illustrates human-AI collaboration, with agents reviewing and editing AI-generated responses rather than fully automated interactions, maintaining quality whilst improving efficiency.
The platform provides lead scoring, opportunity insights and engagement optimisation across sales, marketing and service functions.
Iron Mountain achieved an 80% close rate on AI-generated support replies and reduced chat abandonment by 70%.
2. ChatGPT
Capabilities for business: Conversational AI and content generation, enhancing customer service, automating content creation and streamlining operations.
CEO: Sam Altman, CEO of OpenAI
Launched: 2022
OpenAI released ChatGPT as a research preview in November 2022, rapidly demonstrating enterprise applications beyond initial consumer adoption.
The Gen AI chatbot is widely known and used across the world – automating customer service, content creation and operational processes through conversational interfaces.
The tool also streamlines marketing and social media content production, reducing research and drafting time.
ChatGPT has accelerated business operations across the world, but also like many other AI tools, has acted as a trigger for conversations about enterprise requirements for validation processes and human oversight to prevent reputational damage in customer-facing applications.
1. Microsoft Copilot
Capabilities for business: Productivity AI platform, enhancing productivity and collaboration across Microsoft 365 applications.
CEO: Satya Nadella, CEO of Microsoft
Launched: 2023
Introduced as Bing Chat in February 2023 and rebranded to Copilot in November 2023, Microsoft’s AI assistant integrates with the Microsoft 365 ecosystem including Word, Excel, Outlook and Teams.
The platform automates document drafting, meeting summarisation and data analysis through natural language prompts.
There are many companies across the world that benefit from Microsoft Copilot – for example, Coca-Cola documenting operational efficiency improvements, reducing routine document production time by half. Additionally, Shopify experienced 15% reduction in commit times using GitHub Copilot.
The tool democratises AI access for non-technical business users whilst creating dependency on Microsoft’s cloud infrastructure – and CEO Satya Nadella continues to drive strategic integration across Microsoft offerings, leveraging existing enterprise software dominance to accelerate Copilot adoption through seamless workflow integration.
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