The pressure to innovate in today’s fast-moving business environment is intense. Companies must continuously enhance their offerings to meet changing consumer demands, augment productivity, and stay ahead of the competition. However, developing new products and features can be a time-consuming process. Enterprise generative AI tools can help. By harnessing the power of AI to generate new content and ideas, businesses can accelerate product development and make innovation a more manageable goal. This article will explore how to build AI tools and integrate them into your existing product development process to boost efficiency, drive innovation, and gain a competitive edge with minimal disruption.
Lamatic’s solution, generative AI tech stack, can help you achieve your objectives. This powerful tool can quickly integrate into your team’s existing processes and streamline the use of generative AI tools to boost productivity and innovation.
What is Enterprise Generative AI?
Enterprise generative AI focuses on automating creative and decision-making processes at scale for large organizations. This solution is tailored to businesses' unique needs, such as security, compliance, and integration with existing tech stacks. Enterprise generative AI also differs from traditional AI applications by leveraging advanced machine learning algorithms to perform complex tasks across various business functions.
Why Even Consider Enterprise-Grade AI?
Large organizations often require specific software that integrates into their existing infrastructure, scales to support their workforce, and meets their high-security bar to protect their data, people, and brand. With the rise of artificial intelligence (AI) technologies in the workplace, enterprise-grade technology has never been more important than today. Enterprise organizations increasingly adopt generative AI solutions to enhance communication, optimize workflows, and increase productivity.
According to the 2024 Work Trend Index Annual Report, the use of generative AI has nearly doubled in the last six months, with 75% of global knowledge workers using it. But the reality is that many of your employees are likely using AI technology that your IT team hasn’t properly vetted and produced. And that’s a problem. There is a crucial distinction between relying on unsecured, public AI tools and investing in enterprise-grade generative AI. Choosing a:
- Secure
- Reliable
- Scalable
- Robust AI solution
It is essential for enterprises dealing with sensitive data, intellectual property, and other private information. Enterprise-grade AI should be the foundation of any organization’s strategy to drive efficiency and effectiveness. If AI aims to make every employee more productive, then enterprise-grade AI compounds those gains.
Protection of Intellectual Property and Data Privacy
According to the 2024 State of Business Communication report, some of the primary concerns that business leaders have about generative AI are protecting their company’s security, privacy, and intellectual property. Utilizing unsecured, publicly available tools, like the free version of OpenAI’s ChatGPT, whose generative AI models aren’t tailored to enterprise-level security standards, can lead to breaches of sensitive enterprise data and other compliance risks. Investing in enterprise-grade AI is one way to alleviate these concerns.
Enterprise AI vendors have robust security protocols and ensure that companies that use their large language models (LLMs) retain full ownership of their training data and generated content. This is critical for companies in industries that handle proprietary and sensitive information, such as healthcare, legal and financial services, or governmental agencies. It’s also equally important for any company that handles customer data, as maintaining trust is vital for all businesses.
Evaluating Enterprise AI Vendors: Ensuring Data Privacy and Security with Responsible AI Practices
At Grammarly, one of our core principles of responsible AI is preserving the privacy and security of all customers. This means we go above and beyond to ensure compliance with enterprise-grade regulations and security requirements, safeguard intellectual property, and fine-tune our models on safe, fair, unbiased, and secure datasets.
Many other enterprise AI companies, including Microsoft Azure, also have robust, responsible AI initiatives that ensure data privacy and security are maintained at the highest level. While evaluating enterprise AI vendors, be sure to ask about the details of their responsible AI programs and how they handle your data and intellectual property.
Access to High-Performance, Tailored AI Models
When it comes to enterprise-level operations, high-performance generative AI models are essential. Enterprise-grade AI impacts the full organization, not just one single person or one single use case, so it must be able to handle many tailored use cases and large amounts of data and requests. Enterprise AI offers higher levels of operational efficiency for businesses by providing customizability and scalability capabilities that aren’t available in open AI use tools.
Customizability
From conversational AI chatbots to document and meeting summarization, tailored solutions are key to optimizing workflows. Enterprise-grade AI allows for fine-tuning proprietary data, ensuring highly relevant and accurate outputs tailored to specific business use cases. This leads to superior performance, greater accuracy, and more reliable outputs instead of relying on generic AI systems that lack customizability.
Scalability
Enterprise AI platforms are designed to handle vast data processing and can scale seamlessly as your business grows. These systems also support high-volume use, enabling consistent performance for your entire workforce. Scalable AI models can efficiently manage high volumes of simultaneous tasks, so you never have to worry about system slowdowns.
The Benefits of Using Enterprise-Grade AI Platforms
Using unsecured AI tools may save initial costs, but they can fall short regarding large-scale enterprise needs. Unsecured AI systems may expose businesses to performance bottlenecks, limited use cases, or the inability to scale AI models to meet growing needs.
Enterprise-grade AI platforms enable companies to fine-tune foundation models on specific datasets, ensuring the system’s outputs are aligned with the company’s goals. These platforms also provide in-depth support for metrics tracking, enabling businesses to measure the success and impact of AI implementation on operational efficiency.
Integration Capabilities Across Your Enterprise Tech Stack
Another key differentiator between one-off AI solutions and enterprise-grade technology is their integration capabilities. One-off solutions require employees to break their existing workflows to use AI functionality for simple tasks. Enterprise-grade generative AI is equipped to enhance, not compete with, the capabilities of your existing technology.
Generative AI applications that can be easily integrated into existing enterprise systems and data ecosystems help to:
- Streamline tasks
- Enhance decision-making
- Improve communication
- Automate routine
- Time-consuming processes
When your enterprise AI works with the rest of your tech stack, it improves the ROI across that entire tech stack because your team is more efficient using every tool and more productive across established workflows. You want to look for an enterprise-grade generative AI tool that:
- Meets your customizability requirements
- Can scale with your business
- Easily integrates with your existing tech stack
With Grammarly, you get more strategic AI communication assistance across over 500,000 apps and websites than any other AI writing partner. The benefits span every tool you’ve invested in.
Control Over AI Models, Use, and Updates
The final significant differentiator from investing in enterprise-grade AI is the ability to maintain control over the lifecycle of the AI systems in place. Unsecured AI tools often leave businesses vulnerable to risks related to unpredictable updates, lack of support, or changes to the platform. Enterprise-grade AI, on the other hand, gives enterprises more control. This can include control over how data is handled and stored, how and where employees can use the AI applications, and how the AI models are upgraded and maintained.
Enterprise-grade solutions often include responsible AI guidelines and practices critical to ensuring sustainable AI operations. These tools ensure proper validation and fine-tuning, aligning the AI with ethical standards, including reducing biases in training data and ensuring the long-term sustainability of AI-driven operations. By maintaining control over the AI lifecycle, enterprises can ensure that their AI solutions remain adaptable and secure as their business evolves, which is hard to guarantee with unsecured AI systems.
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Top 25 Enterprise Generative AI Tools
1. Lamatic
Lamatic offers a comprehensive Generative AI tech stack that empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our generative AI tech stack.
2. Glean
Glean is an enterprise-ready digital workplace assistant that harnesses next-gen AI to provide precise answers and insights based on a company’s internal data, all while maintaining robust security and governance protocols.
Key Features
- Tailored enterprise knowledge: Glean understands your company’s unique context and relationships, delivering highly relevant, customized information.
- Connectivity across 100+ business apps: With connectors for 100+ apps, Glean offers expansive search coverage and APIs to integrate additional enterprise applications.
- Generative AI-powered summaries: Glean generates concise document summaries, quickly saving users time by providing the most relevant information.
- Personalized search: Using a knowledge graph, Glean delivers personalized results tailored to each user’s needs.
- Real-time indexed results: Search results are continuously updated, and access is permission-based to ensure users see only authorized data.
- Generative AI for workflows: Whether in engineering, support, or sales, Glean automates tasks and answers complex queries to increase productivity.
3. GitHub Copilot
GitHub Copilot is a revolutionary AI assistant for developers powered by OpenAI's Codex. It integrates with popular code editors and provides intelligent code suggestions, documentation help, and even automated code completion.
Key Features
- Smart code suggestions: It provides real-time code snippets and suggestions, streamlining coding efforts.
- Integration with major code editors: Copilot seamlessly works with editors like Visual Studio Code, enhancing the coding speed.
- AI-Driven code generation: Developers can rely on Copilot to write boilerplate code and tackle complex programming tasks.
4. Superhuman
Superhuman is an AI-powered email client designed to improve email management by offering a faster and more organized experience.
Key Features
- Smart email categorization: Uses AI to prioritize important emails, helping users focus on what matters most.
- Keyboard shortcuts: Quick navigation through an inbox with powerful shortcuts.
- Insights & Analytics: Track open rates, response times, and more, offering insights into email habits for optimized communication.
5. Stampli
Stampli uses AI to automate invoice processing, reducing manual work and increasing the efficiency of financial operations.
Key Features
- Automated invoice processing: Stampli captures and processes invoices automatically, minimizing the need for manual data entry.
- Customizable approval workflows: Set up workflows that align with internal policies for seamless approval processes.
- ERP integration: Stampli integrates with major ERP systems, enabling smooth data flow between finance tools.
6. Gong AI
Gong leverages AI to analyze sales interactions, providing data-driven insights to improve sales performance and drive revenue growth.
Key Features
- Automated call analysis: Gong uses AI to break down sales calls, delivering insights on customer interactions.
- Coaching tools: Provides targeted coaching based on AI-driven analysis of sales calls.
- Revenue insights: Gong identifies growth opportunities and tracks performance metrics to enhance sales strategies.
7. ChurnZero
ChurnZero helps companies prevent customer churn by offering AI-powered insights and automating customer success workflows.
Key Features
- Customer health scores: AI-powered health scores identify at-risk customers, allowing teams to intervene proactively.
- Automated workflows: Automates communication workflows, ensuring timely and personalized follow-ups.
- CRM integration: Works seamlessly with CRM systems, providing real-time customer data.
8. Helpshift
Helpshift provides AI-driven automation for customer support, improving response times and reducing manual tasks.
Key Features
- AI-powered chatbots: Handles common customer inquiries automatically, freeing up human agents for more complex tasks.
- Ticket routing: Automatically assigns support tickets to the correct agents, ensuring efficient problem resolution.
- Analytics: Offers insights into customer support performance, helping teams optimize their support processes.
9. Runway AI
Runway AI is a creative assistant designed for teams working on video editing, graphic design, and more, with AI tools that automate many of these processes.
Key Features
- AI-powered video editing: Automatically edits videos, adds effects, and generates animations.
- Design automation: Simplifies the creation of graphics, designs, and animations through AI.
- Collaboration tools: Teams can collaborate easily within the platform, sharing projects and working together seamlessly.
10. Paradox AI
Paradox AI uses conversational AI to automate HR processes, from recruitment to onboarding, making the entire HR workflow more efficient.
Key Features
- AI-powered recruitment: Conducts initial job applicant screenings via chatbots, schedules interviews, and manages the early stages of the hiring process.
- Automated onboarding: Guides new hires through onboarding, ensuring they have all the necessary information.
- Workflow automation: Paradox AI automates repetitive HR tasks, allowing HR professionals to focus on strategic initiatives.
11. Synthesia
Synthesia was founded in 2017 by a team of AI researchers and entrepreneurs from:
- UCL
- Stanford
- TUM
- Cambridge
Its mission is to empower everyone to make video content - without cameras, microphones, or studios. Using AI, the company is radically changing the content creation process and unleashing human creativity for good. Its tool enables businesses to turn text into high-quality videos with AI avatars and voiceovers in over 120 languages to help make training, customer onboarding, sales, and more.
12. Generative Design
Autodesk’s Generative Design tool allows users across various industries, from automotive to construction, to generate the most optimal, high-performing design alternatives quickly. It enables designers or engineers to input design goals into the generative design software, along with parameters such as:
- Performance or spatial requirements
- Materials
- Manufacturing methods
- Cost constraints
It recently worked with NASA, which turned to AI-powered design tools to create cost-efficient, faster, and lighter structures for space exploration.
13. AlphaCode
A coding tool developed by Google DeepMind, AlphaCode is capable of computer programs at a competitive level. AlphaCode achieved an estimated rank within the top 54% of participants in programming competitions by solving new problems that require a combination of critical thinking, logic, algorithms, coding, and natural language understanding. Google DeepMind is a team of:
- Scientists
- Engineers
- Ethicist
- And more
Committed to solving intelligence, advancing science, and benefiting humanity.
14. Sensei
Sensei from Adobe brings the power of AI and machine learning to experiences, deepening insights, enhancing creative expression, accelerating tasks and workflows, and driving real-time decisions. Adobe has announced several generative AI innovations across Experience Cloud that redefine how businesses deliver customer experiences.
Adobe Sensei GenAI will leverage multiple large language models (LLMs) within the Adobe Experience Platform, depending on unique business needs. Adobe Firefly, Adobe’s new family of creative generative AI models, is focused on images and text effects and is designed to generate safe content for commercial use.
15. DALL-E 2
Beginning as a research project in January 2021, OpenAI introduced DALL-E, which can create images through natural language. One year later, it announced its improved DALL-E 2 system, which can combine concepts, attributes, and styles.
The tool can also expand images beyond the original canvas, creating expansive new compositions. By removing the most explicit content from the training data, OpenAI minimized DALL·E 2’s exposure to these concepts, using advanced techniques to prevent photorealistic generations of real individuals’ faces, including those of public figures.
16. ZBrain
ZBrain is a leading enterprise generative AI platform that aims to reshape operational workflows by leveraging businesses’ proprietary data. This comprehensive, full-stack solution empowers businesses to develop secure Large Language Model-based applications with diverse Natural Language Processing (NLP) capabilities.
Apps built on ZBrain simplify content creation, ensuring outputs align seamlessly with brand guidelines and business workflows. They excel in data analysis, extracting meaningful patterns and trends from data to provide actionable insights for informed decision-making. Offering versatility across various industries, ZBrain integrates seamlessly with over 80 data sources and supports multiple LLMs such as:
- GPT-4
- PaLM 2
- Llama 2
- BERT
Its risk governance feature ensures data safety by identifying and mitigating risks, while ZBrain Flow enables intuitive business logic creation for apps without coding. With competitive pricing plans catering to diverse needs, ZBrain emerges as a powerful ally for enterprises seeking to optimize operations and leverage generative AI for enhanced productivity and innovation.
17. Scale
Scale is a leading force in the enterprise generative AI landscape with its all-encompassing platform, offering the complete toolkit for businesses looking for AI integration. Trusted by prominent AI teams globally, this platform facilitates generative AI applications' seamless development and deployment. Tackling challenges such as:
- Customization
- Performance
- Security
Scale collaborates with premier model providers, boasts a robust data engine for continuous improvement, and ensures independence from specific clouds or tools. Leveraging a tailored approach, Scale fine-tunes models with enterprise data and employs expert prompt engineering for efficient application development. With its high-end features, Scale provides tangible solutions to critical business needs, promising a secure, scalable, and transformative journey into generative AI for enterprises.
18. C3
C3 generative AI stands at the forefront of enterprise generative AI applications, offering a unified knowledge source that transforms how businesses access, retrieve, and leverage critical insights. This cutting-edge solution combines:
- Natural language understanding
- Generative AI
- Reinforcement learning
- Retrieval AI models within C3 AI’s patented model-driven architecture
Ensuring accurate responses traceable to ground truth, C3 generative AI provides rapid access to high-value insights while maintaining stringent enterprise-grade data security and access controls. Its domain-specific responses cater to various industries, including:
- Aerospace
- Defense
- Financial services
- Healthcare
- Manufacturing
- Oil & gas
- Telecommunications
- Utilities
With tailored solutions for business processes and enterprise systems, C3 generative AI marks a significant milestone in deploying domain-specific generative AI models, offering unprecedented efficiency and insights across diverse sectors.
19. DataRobot
DataRobot stands as a pivotal player in bridging the generative AI confidence gap, delivering tangible real-world value in the ever-evolving landscape of AI. With over a decade of expertise, DataRobot offers an open, end-to-end AI lifecycle platform, empowering teams to confidently navigate the intricacies of the AI landscape. Unifying predictive and generative AI workflows, DataRobot eliminates silos and simplifies the deployment of high-quality generative AI applications.
The platform ensures adaptability by allowing innovation with the best-of-breed components across cloud environments while maintaining security and cost control. With a proven track record across multiple domains, DataRobot is committed to supporting organizations in making informed decisions today that shape tomorrow's opportunities, showcasing how generative AI can drive real-world value.
20. Xebia GenAI Platform
Acknowledging the challenges in deploying GenAI models, especially in terms of data privacy and security, Xebia offers an innovative GenAI Platform. This Machine Learning Operations (MLOps) solution, an extension of the Xebia Base, is meticulously designed to facilitate the integration of GenAI-powered applications into existing infrastructures. The platform covers crucial components such as:
- Models
- Prompts
- Monitoring
- Interfaces
- Cloud and data foundations
This approach not only addresses the challenges of deploying GenAI in production but also provides benefits such as:
- Model standardization
- Version control
- Model governance
- Scalability
- Cost optimization
Leveraging transfer learning, Xebia’s GenAI Platform empowers businesses to unlock the potential of generative AI, driving innovation and efficiency while ensuring the utmost security and control.
21. Prophecy
Prophecy is a generative AI platform focusing on the unique challenges posed by private enterprise data. In a landscape where building GenAI applications often encounters hurdles related to data complexities, Prophecy stands out by simplifying the integration of generative AI into existing infrastructures. Offering a two-step solution for data engineers, Prophecy facilitates the creation of GenAI apps on any enterprise data within a week.
Prophecy ensures a streamlined and efficient process by running Extract, Transform, Load (ETL) on unstructured data to build a knowledge warehouse and implementing streaming ETL pipelines for inference. The platform’s key features include:
- ETL pipelines on Apache Spark
- Unstructured data transformations
- Support for multiple large language models
- Vector database storage
- At-scale execution on Spark
Prophecy’s emphasis on simplicity, scalability, and orchestration makes it a pivotal player in unleashing the full potential of generative AI for enterprise applications.
22. Aisera
Aisera emerges as a frontrunner in the generative AI landscape, offering a comprehensive suite of solutions tailored to enterprise needs. The platform’s key offerings include AiseraGPT, an intuitive turnkey solution featuring action bots powered by domain-specific LLMs, and AI Copilot, a concierge bot with proactive notifications and customizable prompts.
Aisera’s Enterprise AI Search ensures personalized and permission-aware results, while AiseraLLM allows users to build and operationalize their LLMs in weeks. Recognized as a leader in GPT and generative AI, Aisera stands out for its commitment to responsible AI, privacy, and security, making it a top choice for organizations seeking customizable yet out-of-the-box virtual agents.
23. Coveo
Coveo stands at the forefront of the generative AI transformation with its Relevance AI Platform, offering a robust solution tailored for enterprise readiness. The platform’s generative answering feature, designed for e-commerce, website, and workplace applications, delivers accurate and trustworthy answers that enhance user experience. Coveo utilizes mature large language models to identify relevant snippets in diverse documents, generating factual, up-to-date responses with citations.
The platform prioritizes enterprise security with stringent data security protocols, automatic data freshness, and personalized, human-like tone responses. With an illustrious history of driving customer growth for over 18 years, Coveo is a pioneer in the generative AI landscape, empowering enterprises with cutting-edge AI capabilities.
24. Addepto
Addepto’s enterprise generative AI platform is at the forefront of transformative innovation, offering organizations a comprehensive solution to build, deploy, and manage AI models. This cutting-edge platform utilizes advanced generative AI techniques to:
- Develop synthetic data
- Generate content
- Automate tasks
- Optimize processes
- Enhance overall efficiency
Addepto empowers businesses to unlock limitless possibilities with a focus on:
- Improved productivity
- Enhanced security
- Cost reduction
- Faster decision-making
- Scalability
- Enhanced creativity
The platform seamlessly incorporates with existing systems, ensuring data privacy, security, and regulatory compliance. Whether in healthcare, finance, manufacturing, or retail, any industry can benefit from Addepto’s enterprise-generative AI platform.
25. Pega GenAI
Pega GenAI is a groundbreaking generative AI platform for enterprises that propels productivity and creativity to new heights. This transformative technology allows organizations to innovate rapidly, leveraging AI and automation to handle complex tasks, enabling teams to concentrate on high-value activities.
Pega GenAI facilitates responsible adaptation, offering enterprise-ready governance to instill confidence in AI-driven endeavors. From accelerating low-code app development and strategy optimization to streamlining customer touchpoints and unlocking insights in the back office, Pega GenAI delivers a suite of 20 generative AI-powered boosters seamlessly integrated into the Pega Infinity platform.
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6 Hard Truths of Generative AI in the Enterprise
1. Tech Talent Remains the No. 1 Barrier to Adoption
Generative AI offers promising advances for enterprise operations, but organizations need skilled employees to get the most out of these innovative tools. Unfortunately, there aren’t enough of them. A recent Deloitte report states that lack of AI expertise remains the top barrier to adoption. Organizations that reported high AI expertise (33%) tended to feel more positive about generative AI. Yet, they too feel more pressure to adopt the technology, seeing genAI as more of a threat to their business model, according to the report.
“This reveals that even organizations who consider themselves well-versed in AI are preparing for potential headwinds,” says Jim Rowan, a principal at Deloitte Consulting. As a result, IT leaders, even those who report high levels of AI expertise on their teams, are rethinking talent strategies around AI, with upskilling playing a vital role in attempting to bridge AI skills gaps.
2. GenAI’s Bottom-Line Impact is a Ways Away
While noting that “the honeymoon phase is over,” there are likely few genAI projects generating bottom-line impact right now, according to Aamer Baig, a senior partner and global leader at McKinsey, who spoke about the hard truths of GenAI at the MIT Sloan CIO Symposium in May. Only 15% of the companies McKinsey surveyed have a line of sight into earning improvements from genAI initiatives, Baig said. “Not all use cases are equal. Not a lot add value.” He suggested that organizations focus on “initiatives that solve real business problems that are technologically feasible and have little risk.”
3. Legal Questions Put IT in a State of Limbo
The two factors Higginson believes are slowing down the delivery of platforms at scale are the legal and regulatory unknowns of relying on the technology and the high cost of being an early adopter. “While the legal jeopardy of using this technology remains murky, the vendor costs and the media-fed paranoia about ‘getting something wrong’ remains high,” he says. “As a result, many administrators are electing against being the first test case for this brave new frontier of technology — especially when they have an unclear notion of how the technology functions and cannot provide attorneys any guarantees on how it actually works.”
The Deloitte study found that compliance (28%) and governance issues (27%) are considered barriers to AI adoption. According to the report, less than half (42%) of respondents agreed they have done enough to govern generative AI adoption and mitigate its potential risks.
4. Managing Costs is a Huge Issue
Echoing Higginson’s concern about the cost of implementing genAI, McKinsey’s Baig stressed that organizations must manage costs before costs are managed them. This is due to the high compute intensity and high change management genAI requires. He recommended that organizations invest as much in these areas as they do to bring about digital transformation because genAI will require changes in workflows, business processes, and new KPIs.
Organizations also have to factor in risk and hallucination training and budget for ongoing maintenance, Baig said. Higginson says the fundamental barriers to adopting genAI are the scarcity and cost of the hardware, power, and data needed to train models. “With such scarcity comes the need to prioritize which solutions have the broadest appeal to the population and can generate the most long-term revenue,” he says.
5. Getting the Right Data is Hard
Right now, many organizations are challenged by the need to use high-quality data because genAI models require vast amounts of accurate, relevant data to perform effectively. Organizations should not focus on finding the perfect data because that is a “huge, daunting challenge,” said McKinsey’s Baig. “Many organizations are taking a top-down look at data, and ‘that ends up looking like a massive spreadsheet.”
“Focus on data that helps you with multiple use cases.” While research and development continue to push the needle on what genAI can do, “we know that data is a critical aspect to enabling AI solutions. We also recognize that many organizations are uncovering the work it will take to build the right data foundations to support scaled AI deployments,” says Deloitte’s Rowan.
6. It’s Here to Stay
Despite the growing pains, IT leaders recognize that genAI isn’t going anywhere. “One thing is for sure, it’s going to be a bumpy ride with some spectacular successes and failures, and I cannot wait to see how the industry changes over the next five to 10 years,” Phoenix Hospital’s Higginson says. Michael Corrigan, CIO of World Insurance, says that while genAI is powerful and evolving very rapidly, it is maturing very slowly. He says there is also a lot of hype and misnomers about it. “It definitely requires a strategy and roadmap to be implemented properly for it to have a positive impact on your business, to be able to enhance your capabilities, and reach your business goals,” Corrigan says.
It also requires organizations to establish use cases and the tools they want to use because shadow genAI is creeping in. “Even if a company hasn’t rolled out a particular AI tool, employees are out there using ChatGPT and all sorts of third-party AI tools because it is making them more efficient,” says Briggs & Stratton’s Olsson. “The hard truth is, if you don’t start giving them tools, they’re going to find them; … even if they’re not doing anything with AI, the data risk is there. It’s a new information security risk.”
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack. Our solution provides:
- Managed GenAI middleware
- Custom GenAI APIs
- Low code agent builders
- Automated GenAI workflows
- GenOps
- Edge deployment
- Integrated vector databases
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
