Building AI for enterprise: A checklist
Learn essential steps for successful enterprise AI adoption, from identifying use cases to prioritizing security and data management.
Key Takeaways
Identify high-impact use cases within your organization where AI technologies can deliver significant ROI, such as customer service chatbots, automated report generation, contract analysis, and process automation.
Ensure data quality, accessibility, and structure, as they directly impact the effectiveness of LLM-powered AI systems. Leverage Retrieval Augmented Generation (RAG) to enhance the context and accuracy of AI-generated responses.
Carefully select the right LLM based on your specific requirements and fine-tune it on your enterprise data to improve its understanding of your domain-specific language and context.
Prioritize security and privacy in AI deployments by adopting a "security-first" approach and considering on-premises deployment or Virtual Private Clouds (VPCs).
Embrace an API-first approach to ensure seamless integration and scalability of your AI solutions, allowing them to work harmoniously with your existing systems.
Last year, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
I have been building and working with enterprise software for about a decade now. As I started Multimodal, my one goal was to implement AI and machine learning in large organizations with complex workflows so they could get ROI fast and scale across the board.
Building efficient and scalable enterprise AI software comes with many challenges. Most business leaders don’t know exactly what they need for successful deployment and implementation. In this checklist, I’ll dive into some things that are essential for building and deploying a great enterprise AI solution.
AI implementation checklist: 5 key points to ensure maximum success
Before we begin the checklist, I want to emphasize that the decision to build a solution in-house vs outsourcing is crucial. However, regardless of what you choose, you should consider the following before investing any dollars in a solution.
1. Identify high-impact use cases
The first step is pinpointing areas within your organization where AI technologies can deliver the most impactful results. Think about repetitive tasks, mundane workflows, knowledge-based and document-heavy work.
Anything that involves substantial amounts of natural language processing, knowledge retrieval, or content generation will generally be a good choice. These are the sweet spots where generative AI truly excels.
Prioritize use cases that align with your strategic business goals and promise a high return on investment. In industries grappling with complex data and intricate workflows, you’ll usually see plenty of opportunities:
Customer service chatbots: AI-powered chatbots can engage in natural, human-like conversations with customers, providing instant responses to queries, troubleshooting issues, and even offering personalized product recommendations.
Automated report generation: LLMs can analyze vast datasets and generate insightful reports in a fraction of the time it takes humans, freeing up your team for more strategic work. For example, BloombergGPT, a recent LLM specifically trained in financial data, can generate financial reports and news articles with impressive accuracy.
Contract analysis: LLMs can sift through lengthy legal documents, extract key information, identify potential risks, and summarize complex clauses, significantly speeding up contract review processes. One startup that is successful in the legal AI space is Harvey. Contract analysis is one of the key things they do.
Process automation: Repetitive, rule-based tasks, such as data entry, form filling, and invoice processing, can be automated with generative AI, reducing errors and boosting efficiency. We’ve worked with several banks and financial institutions to automate tasks like loan origination, document analysis and extraction, and application processing. It’s a huge area of interest for most clients we work with, and efficient process automation is achievable with Gen AI.
These are just a few examples, and the possibilities are constantly expanding as LLMs become more sophisticated. The key is to identify those use cases that not only leverage the strengths of AI but also directly contribute to your business goals.
By targeting high-impact areas, you can ensure that your AI investments yield tangible benefits in a short time period. This also prevents disillusionment with AI and helps you test what works best for your enterprise.
2. Data: The foundation of AI success
Data serves as the lifeblood of any LLM-powered AI system. Its quality, accessibility, and structure directly impact the model's ability to generate meaningful insights, accurate predictions, and valuable actions. Ensuring data is clean, well-structured, and readily accessible is not just a best practice; it's a fundamental necessity for AI success.
Why data quality matters: LLMs, while powerful, are only as good as the data they're trained on. Inaccurate, incomplete, or biased data can lead to flawed outputs, perpetuating errors, misinformation, or even discriminatory outcomes.
Retrieval Augmented Generation (RAG): Traditional LLMs have a knowledge cut-off date based on their training data. This means they are unaware of any information or developments that occurred after that date.
They also lack access to proprietary enterprise data, limiting their ability to generate contextually relevant and accurate responses within a specific business environment.
RAG addresses these limitations by acting as a bridge between the LLM and the vast universe of enterprise data. By automating knowledge retrieval and integration, RAG streamlines the process of generating insights from enterprise data, saving time and resources.
In essence, RAG empowers LLMs to tap into the wealth of knowledge residing within an organization's data ecosystem. This allows them to go beyond their pre-trained knowledge and generate responses that are not only informative but also contextually relevant, accurate, and personalized.
Overcoming data challenges: Data silos, where information is scattered across disparate systems, can hinder access and integration. Unstructured data, such as text documents, emails, and social media feeds, can be difficult for LLMs to interpret and utilize effectively.
Every organization has unique data characteristics and workflow requirements. A one-size-fits-all approach rarely yields optimal outcomes. Tailoring AI solutions to the specific needs of each organization, including data formats, integration points, and user experience, is crucial for maximizing the value of Gen AI-powered applications.
For your enterprise AI platforms to thrive and deliver full value, overcoming these data challenges is crucial. The best thing is that AI can help with cleaning the data too.
I’ll continue this discussion In my next post two weeks later where I’ll dive deeper into data, RAG, and its relevance for enterprise AI while also addressing how data can be made AI-ready.
3. Choose the right LLM and fine-tune for your needs
Gen AI is where the action is when it comes to enterprise AI applications. Selecting the right LLM for your enterprise AI solution is crucial. It's essential to evaluate different options based on factors like:
Size and performance: LLMs vary significantly in size, ranging from compact models suitable for resource-constrained environments to massive models with exceptional language understanding capabilities. Larger models generally offer better performance but demand more computational power.
Cost: LLM providers typically offer various pricing tiers based on usage, features, and support levels. Carefully assess your budget and projected usage to ensure a cost-effective solution.
Industry-specific needs: Certain LLMs may be pre-trained or fine-tuned on data relevant to specific industries, such as finance or healthcare. Consider whether a model with domain-specific knowledge would be advantageous for your use case.
Open-source vs. proprietary: Open-source LLMs offer flexibility and customization but may require more technical expertise to implement and maintain. Proprietary models often come with user-friendly interfaces and support but may have limitations on customization.
Once you've selected an LLM, fine-tuning it is crucial to unlock its full potential. At Multimodal, we focus on fine-tuning the LLM on company-specific data. This process enhances the LLM's ability to generate contextually relevant and accurate responses.
By exposing it to your internal documents, customer interactions, and industry-specific data, the LLM becomes more adept at understanding the context and nuances of your business.
4. Prioritize security and privacy
From a risk management perspective in the enterprise, security, and privacy must be at the forefront of any AI deployment. Adopting a "security-first" approach ensures that private deployments are the default option, minimizing data breaches and unauthorized access.
Data security in the age of AI is not just a matter of compliance; it's a business imperative. In March 2024, a HiddenLayer study revealed that a staggering 77% of businesses reported a breach to their AI systems in the past year, highlighting the vulnerability of AI infrastructure.
A single breach can lead to devastating financial losses, reputational damage, and erosion of customer trust. In highly regulated industries like finance, the stakes are even higher, with stringent data protection laws and penalties for non-compliance.
On-premises deployment: For organizations with particularly sensitive data or stringent regulatory requirements, on-premises deployment offers the highest level of control and security.
Virtual private clouds (VPCs): VPCs provide a secure and isolated environment within the cloud, offering a balance between control and scalability. With VPCs, you can leverage the benefits of cloud computing while maintaining strict access controls and data segregation.
Key security considerations:
Data encryption: Encrypting data at rest and in transit safeguards it from unauthorized access, even in the event of a breach.
Access controls: Implement strict access controls and authentication mechanisms to ensure that only authorized personnel can access sensitive AI systems and data.
Vendor due diligence: When partnering with third-party AI providers, thoroughly assess their security protocols and ensure they align with your organization's standards.
While working with different clients at Multimodal, I hear questions about security all the time. This is why, since the beginning, we’ve ensured full data privacy, network monitoring, and explainability. For our finance clients especially, explainability is crucial, and so is compliance.
By prioritizing security and privacy from the outset, and learning from the missteps of others, you too can build trust with partners and stakeholders, demonstrating your commitment to safeguarding their valuable information.
5. Adopt an API-first approach for seamless integration
You want your new AI tools to work effortlessly with your existing systems, not clash with them. An API-first approach ensures your AI solutions seamlessly integrate into your workflows, enhancing existing capabilities without requiring disruptive changes.
APIs, or Application Programming Interfaces, are the communication channels of the software world. They enable different applications to exchange data and functionality, even if they were built with different technologies. This communication happens through standardized requests and responses, making it easy for developers to connect disparate systems.
Seamless integration: Eliminate disruptive overhauls and complex migrations. With APIs, your AI solutions directly plug into your existing workbenches, enhancing capabilities without requiring you to start from scratch.
Scalability: As your business grows and your needs change, your AI should keep pace. An API-first architecture provides the flexibility to scale your solutions, handling increased data volumes and user demands. This decoupling of AI components from the underlying infrastructure allows you to add more resources or distribute the workload as needed.
Flexibility: APIs empower you to swap out components or add new functionalities without disrupting the entire system. This modularity allows you to iterate and improve your AI solutions over time, keeping them up-to-date with technological advancements.
Collaboration: Well-defined APIs enable different teams to work independently on various aspects of the AI solution, accelerating development and fostering innovation.
I also host an AI podcast and content series called “Pioneers.” This series takes you on an enthralling journey into the minds of AI visionaries, founders, and CEOs who are at the forefront of innovation through AI in their organizations.
To learn more, please visit Pioneers on Beehiiv.
Final tips for successful AI implementation
This checklist is a starter. For successful AI adoption in the enterprise, you need to ensure several things:
Choose the right vendor. Evaluate based on security, level of support offered, ease of integration, and other metrics.
Look for workflows that are simple when you’re first starting off. You ideally want to get to ROI within a few months so you and the other stakeholders do not feel like their investment is not paying off.
Making data AI-ready is always a challenge. You ideally want an AI partner who will clean and process the data for you. Otherwise, the implementation will be a failure.
I’ll come back in two weeks with a deeper dive into managing data and making it ready for enterprise AI applications.
See you in two weeks,
Ankur.