Generative AI in the Enterprise
The past year, with its explosion of generative artificial intelligence (GenAI) based tools, has created a lot of buzz around the business potential of artificial intelligence (AI). The use of AI enabled technology is not new of course, but until the broad exposure created by GenAI, it remained mostly “behind the scenes”. Business functions related to analyzing data and automating tasks were in common use in business prior to the public release of GenAI in 2022. Here are a few examples of “traditional AI”:
Customer Service:
Chatbots: Answering basic questions, resolving simple issues, and directing customers to human agents for complex problems.
Sentiment Analysis: Gauging customer satisfaction from emails, reviews, and social media to improve products and services.
Marketing & Sales:
Targeted Advertising: Using customer data and browsing habits to deliver personalized ads with higher click-through rates.
Lead Scoring: Qualifying leads based on demographics and online behavior to prioritize sales efforts.
Operations & Finance:
Fraud Detection: Identifying suspicious transactions and credit card activity in real-time.
Demand Forecasting: Predicting future sales trends to optimize inventory management and production.
Human Resources:
Recruiting: Screening resumes and identifying qualified candidates based on keywords and skills.
Performance Management: Analyzing employee data to identify top performers and areas for improvement.
Product Development:
Predictive Maintenance: Analyzing sensor data to anticipate equipment failures and schedule maintenance proactively.
Recommendation Engines: Suggesting relevant products to customers based on their purchase history and browsing behavior.
The impact of the broad availability and use of GenAI served to democratize AI to consumers and businesses. The introduction marked a significant shift in the capabilities of AI for business. Here's how it impacted the overall AI landscape:
From Analysis to Creation:
Traditional AI excelled at analyzing data and automating tasks, but genAI pushed the boundaries. It can not only analyze information but also use it to create entirely new content, like text, images, or even code. This opened doors for businesses in areas like marketing, product development, and customer service.
Enhanced Efficiency and Productivity:
GenAI automates tasks beyond simple data entry or classification. It can create marketing copy, product prototypes, or personalized customer communications, freeing up human employees for more strategic work.
Personalized Experiences:
GenAI personalizes content and experiences for customers at scale. It can tailor marketing campaigns, product recommendations, or even chat conversations with individual preferences in mind.
Innovation and Design:
Businesses can use genAI to explore new design ideas, generate product variations, or create marketing materials that resonate with specific demographics. This fosters faster innovation cycles and data-driven design decisions.
Challenges and Considerations:
While powerful, genAI models require careful training and supervision to avoid creating biased or inaccurate outputs. Businesses need to ensure data quality and ethical considerations when implementing these tools.
GenAI has significantly expanded the potential of AI for businesses. It's no longer just about analyzing data; it's about leveraging that data to create, innovate, and personalize experiences at an unprecedented scale.
Business Use and Adoption
Looking at the current level of use / adoption in business is not a simple task. This is common for technologies that are democratized and broadly available to everyone. The high level of virality of publicly available tools like Google Gemini, OpenAI ChatGPT, Anthropic Claude, etc. means that employees are likely to experiment with business use of these tools “unofficially” without management approval or sponsorship to improve personal productivity. More systematic use cases of genAI are available from many providers already, although many are in beta or at least with recent availability. Leading enterprise providers have rushed to include more use cases over the past year+ as well, so the use cases are evolving rapidly.
Generative AI for the Enterprise
Enterprise software vendors are diving headfirst into generative AI, recognizing its potential to revolutionize how businesses operate. Here's a breakdown of how some major players are approaching it:
General Trends
Focus on Efficiency and Automation: Generative AI is being used to automate repetitive tasks, generate reports, and summarize data, freeing up human employees for more strategic work.
Enhanced Conversational Experiences: Chatbots and virtual assistants powered by generative AI are becoming more sophisticated, offering more natural and helpful interactions for customer service, sales, and internal support.
Low-Code/No-Code Development Platforms: Generative AI is blurring the lines between software development and business processes. Platforms are being built that allow users with minimal coding experience to leverage AI for creating custom functionalities.
Specific Vendor Examples
Salesforce: Salesforce is actively integrating generative AI into its software to enhance customer relationship management (CRM) and boost productivity across various business domains. Here's an overview of their key initiatives:
Einstein GPT: Launched as the world's first generative AI for CRM, Einstein GPT is designed to generate AI-powered content across all customer interactions within Salesforce's platform, including sales, service, marketing, and commerce. This tool leverages generative AI to create personalized interactions and improve customer experiences at scale.
Einstein 1: Salesforce has developed AI cloud, now referred to as Einstein 1, a suite of generative AI tools aimed at supercharging customer experiences and company productivity. This platform is built on a foundation of trust and is optimized to deliver powerful AI capabilities within the enterprise environment.
Salesforce Slack Integration: The integration of generative AI within Slack, known as Slack AI, aims to enhance workplace productivity. This tool allows users to tap into AI-powered search, thread summaries, and soon, a digest feature, making it easier to access collective knowledge and stay organized within Slack.
Einstein Copilot: This feature serves as a conversational AI assistant that offers customizable actions for business tasks across sales, service, marketing, and more. It uses a reasoning engine to interpret user prompts and generate trusted AI responses based on company data, ensuring compliance with industry and company policies.
Einstein Studio: Einstein Studio is a set of tools that lets businesses leverage the power of artificial intelligence (AI) within the Salesforce CRM platform. It caters to different needs and offers a range of functionalities:
Build custom AI models: Einstein Studio empowers businesses to build and deploy their own AI models using their specific data (bring your own model - BYOM). This allows for highly customized solutions tailored to address the unique needs of the company.
Leverage pre-built AI models: In addition to BYOM, businesses can also leverage pre-built AI models from Salesforce or other providers. This offers a faster and more accessible way to incorporate AI functionalities without the need for extensive development.
Low-code conversational AI: Einstein Studio includes tools like Prompt Builder and Copilot which use a low-code approach. This means businesses can build and customize chatbots or virtual assistants without requiring in-depth coding knowledge.
Data privacy: A key aspect of Einstein Studio is its focus on data privacy. Businesses retain ownership of their data throughout the AI development process
Marketing GPT and Commerce GPT: These new products are intended to personalize every marketing campaign and shopping experience by automatically generating content that can engage customers more effectively. The aim is to leverage generative AI to optimize marketing strategies and e-commerce operations.
By embedding these generative AI capabilities into its platforms, Salesforce is enhancing the efficiency and effectiveness of business operations, ensuring that its CRM tools remain innovative and valuable to its users.
Oracle: Oracle is actively integrating generative AI capabilities throughout its software stack to enhance various business functions across multiple industries. This integration spans from its cloud applications to its database and infrastructure services, offering robust AI-driven solutions.
Across Business Functions: Oracle's generative AI capabilities are being embedded in different sectors like sales, software development, healthcare, and customer support. For instance, in sales, these capabilities can automate the creation of customer profiles and training modules. In healthcare, they can automate administrative tasks and generate personalized treatment plans.
Fusion Cloud Applications: Oracle has enhanced its Fusion Cloud Applications Suite, including Human Capital Management (HCM), Customer Experience (CX), Enterprise Resource Planning (ERP) and more, with generative AI capabilities. These enhancements help in automating tasks such as content creation for marketing, summarization of customer interactions, and providing real-time feedback in recruitment processes.
Database and Infrastructure: Oracle is also embedding AI into its database portfolio, allowing customers to leverage generative AI to build custom applications using private enterprise data. This is supported by tools that facilitate building, training, and managing AI models, like the integration with open source libraries and the OCI Data Science platform.
OCI Generative AI Service: The Oracle Cloud Infrastructure (OCI) Generative AI service provides a foundation for these capabilities, offering enterprise-grade AI that emphasizes data management, security, and governance. This allows for extensive customization and integration of AI across Oracle's products, from enterprise applications to databases.
Oracle’s strategy focuses on embedding generative AI throughout its entire stack, aiming to deliver seamless, efficient, and highly integrated AI capabilities that align closely with business needs, thereby enhancing productivity and innovation across industries.
Microsoft: Microsoft has been actively integrating generative AI capabilities into its software offerings across various platforms and business functions. Here's an overview of how they are doing it:
Microsoft Copilot in Power Platform: Microsoft has launched Copilot capabilities within its Power Platform, including Power Apps, Power Automate, and Power Pages. In Power Apps, Copilot helps users build apps using natural language inputs, significantly speeding up the development process and improving success rates. Power Automate utilizes Copilot to enable faster creation of automation flows and more intuitive interaction with AI for non-developers. Power Pages benefit from Copilot by assisting in website creation and content management through AI-driven suggestions and automations.
Dynamics 365 Enhancements: In its Dynamics 365 suite, Microsoft has incorporated generative AI to enhance functionalities in ERP and CRM systems. Copilot capabilities in Dynamics 365 help streamline complex business processes, automate data entry, enhance financial operations, and optimize supply chain management. For example, project managers can use Copilot to swiftly generate project plans and status reports using conversational inputs.
Retail and Customer Experience: Microsoft is also transforming the retail sector with its AI integrations. Using the Microsoft Cloud for Retail, retailers can offer personalized shopping experiences similar to a concierge service, facilitated by generative AI. This includes personal shopper-like interactions where customers can inquire about products using natural language. This capability not only enhances customer experience but also optimizes operations and insights for store associates and managers.
Security and Compliance: In terms of security, Microsoft has introduced Security Copilot in Microsoft Defender for Cloud, which provides enhanced risk management and remediation guidance. This helps security teams manage and secure generative AI applications and the data they interact with. Microsoft Purview has been updated to secure and govern AI usage, ensuring data protection and compliance across AI-driven applications.
These initiatives demonstrate Microsoft’s commitment to embedding generative AI across its products to improve efficiency, enhance security, and create more intuitive user experiences across various business domains.
Google: Google is actively embedding generative AI across its software suite to enhance functionality and user interaction, focusing particularly on Google Cloud, Google Workspace, and its search engine.
Google Cloud and Workspace: Google has integrated advanced AI models into its Google Cloud services, notably through Vertex AI and the introduction of the Gemini 1.5 Pro model. This model offers significant improvements in handling large contexts, which can be crucial for businesses needing to process extensive datasets or detailed content across various formats. Google is also enhancing its Workspace tools (Docs, Sheets, Slides, and Gmail) with AI-powered features that help streamline workflows and enhance productivity by automating routine tasks and generating content.
Search Engine Enhancements: Google's Search Generative Experience (SGE) is significantly revamped with generative AI to provide faster responses, enhanced information retrieval, and a more intuitive user experience. The generative AI helps users by pulling together comprehensive insights and context for their queries, which allows for a deeper and more efficient search experience. For example, it can synthesize complex answers and provide a conversational interface where users can follow up with more specific questions, maintaining the context throughout the interaction.
Development Tools for Third-Parties: Google is also focusing on providing developers with robust tools through APIs like PaLM and environments like MakerSuite. These tools are designed to help developers easily integrate and experiment with AI in their applications, encouraging innovation and the development of new AI-powered solutions.
Through these initiatives, Google is leveraging its long-standing AI research to transform how businesses operate, enhance productivity tools, and reshape interactions with digital content, ensuring that their offerings are at the cutting edge of technology.
Challenges and Considerations
Monetization: Deciding how to charge for generative AI features within their software is a challenge. Some believe it will become a standard feature, while others see specialized AI tools as a potential revenue stream. There seem to be 2 broad camps, those that embedded genAI and include it in the products when available like Oracle; and those that are charging additional fees for the use of genAI like Salesforce and Microsoft. Those fees range from a subscription uplift to consumption based pricing models.
Ethical Considerations: Ensuring data privacy, mitigating bias in AI outputs, and maintaining transparency in how generative AI arrives at its conclusions are crucial aspects that vendors need to address.
Explainability and Trust: Generative AI models can be opaque. These vendors need to ensure users understand how the AI arrives at its outputs and build trust in its accuracy.
Data Security and Privacy: Generative AI often relies on vast amounts of data. Enterprise software vendors need robust security measures to protect user data and comply with privacy regulations.
Generative AI is transforming enterprise software. As these technologies mature, we can expect even more innovative applications that streamline workflows, enhance user experiences, and empower businesses to make data-driven decisions. Enterprise software vendors are actively integrating generative AI across their products, with a focus on conversational interfaces and empowering non-programmers. However, challenges around explainability, data security, and monetization need to be addressed.
AI Strategy
To successfully take advantage of the genAI capabilities that are emerging in enterprise software companies need to build an overall strategy to guide the implementation and use. Building a successful enterprise AI strategy requires a well-defined roadmap. Here's a breakdown of the key steps involved:
1. Business Goals First:
Start by outlining your overarching business objectives. What are the key performance indicators (KPIs) you want to improve? Identify areas where AI can potentially add value, like process automation, cost reduction, or enhanced customer service.
2. Identifying AI Opportunities:
Evaluate your current workflows, data infrastructure, and technological capabilities. This will help you understand where AI can be realistically integrated and what high-impact use cases exist for your business.
3. Building the Team and Tech Stack:
Define the technology and talent required to implement your AI strategy. Do you need to invest in new AI expertise or can you upskill existing employees? Explore the AI technology landscape to determine the most suitable tools and platforms for your needs.
4. Phased Implementation Roadmap:
Don't try to do everything at once. Create a phased implementation plan with clear milestones and timelines. This allows for a measured approach, making it easier to adapt and learn from each stage.
5. Stakeholder Alignment and Ethics:
Ensure all stakeholders, from executives to employees potentially impacted by AI, are aligned with the strategy. Develop a clear AI policy that addresses ethical considerations around data privacy, bias, and transparency.
6. Monitoring and Improvement:
Track the performance of your AI initiatives against the defined KPIs. This will help you identify areas for improvement and ensure your AI strategy continues to deliver value to the business.
Remember, this is an ongoing process. As you gain experience with AI, you can refine your strategy and explore new opportunities to leverage this powerful technology for continuous improvement.
