Gen AI is the new baseline for enterprise software
Top-tier tech companies such as Microsoft, Salesforce, and Google are setting a new standard for AI enterprise software. Gen AI capabilities are becoming a must-have. Gartner projects that over 80% of software providers will embed gen AI into their products by 2026, driven by a demand from organizations for smarter productivity tools, more dynamic user experiences, and automation at scale.
Despite the buzz, harnessing gen AI for real impact requires companies to reinvent their processes, job roles, and traditional workflows. A recent McKinsey report highlights a paradox: 92% of enterprises plan to increase their AI investments, yet only 1% of leaders consider their own operations as ‘AI mature’. Achieving widespread adoption of gen AI tools and obtaining tangible business outcomes hinges on how businesses navigate challenges related to data quality, governance, and integrating AI into their core operations, rather than just isolated use cases.
On the data quality frontier, unlocking the value of AI demands not just smart algorithms but also context-rich, human-annotated data to train and refine those models.
How major tech companies are embedding gen AI into their platforms
Supercharged with gen AI, software platforms now augment human capabilities, automate complex processes, and deliver unprecedented levels of efficiency across a wide array of use cases.
Let’s explore how leading tech companies are integrating gen AI into their enterprise platforms:
- Microsoft 365 Copilot blends generative AI into Word, Excel, Outlook, and more, allowing users to draft documents, summarize meetings, and automate repetitive tasks. Developers at ESW, an international e-commerce firm, are using GitHub Copilot to code more efficiently and meet tight deadlines, boosting productivity by 25%. And EY’s Global Tax Practice created a legal and research agent in Copilot Studio that can sift through vast amounts of specialized documentation and deliver relevant data in seconds.
- Salesforce Einstein GPT uses internal CRM data to auto-generate emails, customer summaries, and chatbot responses, personalizing interactions at scale. Its AI agents have notably lowered customer service response times by 60%, boosted customer satisfaction, and increased retail conversion rates by 30% through personalized recommendations and demand forecasting.
- Google Workspace leverages gen AI for enhanced communication and data handling, with real-time suggestions in Docs and Sheets. Gemini, their next-generation multimodal language model, amplifies these capabilities even more. Hundreds of businesses are now using gen AI to brainstorm ideas, draft email responses 35% faster, craft compelling product descriptions, and accelerate research. This helps teams speed up tedious tasks from budgeting to summarizing conversations with clients and creating mock-ups, boosting daily productivity.
- IBM focuses on enterprise AI solutions through its Watsonx platform. Automotive company Honda, for example, is leveraging gen AI to extract knowledge from technical documents, reducing documentation modeling time by 67%. In the telecommunications sector, Vodafone has reduced the turnaround time of journey testing from 6.5 hours to less than a minute.
- Amazon Web Services (AWS) offers a suite of AI and machine learning services designed to help businesses build, train, and deploy AI models at scale. For instance, Adobe is using AWS solutions to train foundational AI models for creative use cases, such as generating high-quality images and designs.
Why enterprise-AI needs human data annotation
Enterprise AI software requires high-quality data annotation to ensure that AI-generated outputs accurately reflect the needs of business users.
AI-powered platforms often deal with complex, nuanced, and domain-specific data, including medical images, financial documents, and customer service interactions. If these systems are not carefully trained on relevant, precise, and context-rich data, they become prone to errors, hallucinations, and harmful bias.
Human annotators bring contextual understanding, judgment, and the ability to interpret ambiguity, which AI models are not able to replicate. They are essential for:
- Establishing ground truth data by labeling data according to specific guidelines. This labeled data is the foundation upon which AI models learn to recognize patterns and make accurate predictions.
- Ensuring accuracy and reducing AI bias. Diverse teams of human annotators are critical in identifying and mitigating biases in training datasets, ensuring fairness and ethical AI performance, particularly in sensitive areas like recruitment or finance.
- Handling complexity and nuance: From capturing linguistic complexities (sarcasm, irony) to identifying visual subtleties (for example, in image medical annotation), human intelligence is key to interpreting data correctly.
A human-in-the-loop (HITL) approach is necessary even after deployment. This involves creating feedback loops where humans refine AI outputs over time, correcting errors, and providing labels for uncertain data points.
Preparing for the next generation of AI software
Looking ahead, gen AI will continue to expand its role in enterprise software, powering decision support, knowledge management, and hyper-personalized user experiences.
Companies must be able to train AI models that consistently deliver reliable results. This demands human oversight, deep domain expertise, and implementing thorough quality assurance processes throughout the AI lifecycle.
Trusted by four out of five global tech titans, Sigma combines human expertise with cutting-edge AI to create software that works smarter, faster, and more ethically. Our curated team of over 25,000 expert annotators delivers high-quality annotated and validated data at scale, meeting dynamic enterprise needs across multiple languages and domains.
Ready to build the next generation of AI software? Contact us to explore how we can power your next breakthrough with human-annotated data.