Building Enterprise-Grade AI Applications
As the C-suite continues to marvel at AI, many companies are settling into the early stages of undertaking enterprise AI adoption in targeted areas now that the “winner takes all” rush to apply AI technologies to outpace the competition is mostly gone.
Instead of diving headfirst into new technology, AI deployment benefits from an open, experimental mindset, backed by rigorous evaluations and safety guardrails, according to OpenAI.
The companies seeing success aren’t rushing headlong to inject AI models into every workflow. They’re aligning around high-return, low-effort use cases, learning as they iterate, then taking what they learned into new areas.
The results are clear and measurable: faster, more accurate processes; more personalized customer experiences; and more rewarding work with less of a focus on mundane tasks.
WHAT AN ENTERPRISE AI SOLUTION IS
An enterprise AI solution is an AI-based technology that is designed and implemented to solve specific business challenges or streamline business processes within an enterprise or organization.
It involves the application of machine learning (ML), natural language processing, computer vision, and other AI techniques to develop intelligent systems that can automate tasks, analyze data, and provide insights.
Before we tackle the best practices involved in building an enterprise AI platform, let’s define what enterprise AI is not.
WHAT AN ENTERPRISE AI PLATFORM IS NOT
As per a report on the Publicis Sapient website, there are many tools that can act as the building blocks for implementing organization-wide enterprise AI. However, the following are examples of what enterprise AI is not.
- AI chatbots and copilots
For example, ChatGPT Pro or Microsoft Copilot are impressive tools on their own but they don’t natively connect with ERP systems, proprietary databases, or business logic workflows. These tools can generate insights on demand but can’t retain institutional or contextual knowledge over time to make AI-driven decisions more effective. They also process data through external servers, creating security and compliance risks for enterprises dealing with GDPR, HIPAA, or SOC 2 regulations.
- SaaS AI add-on platforms
Many tools work within their own suites yet require custom connectors or manual exports to sync with financial models, proprietary ML services, or bespoke applications.
Function-specific AI (such as ticket summarization or sales forecasting) delivers point value, but coordinating models across sales, operations, engineering, and compliance—where exponential value often lives—usually demands an extra layer of workflow management. SaaS AI comes with best practice capabilities out of the box, yet enterprises that want to train, fine-tune, and deploy specialized models will eventually outgrow the default settings.
- Generic infrastructure providers
Enterprise AI needs to work with SAP, Oracle, and many other large internal tools. Putting all your data in the cloud might pose a security risk, even for analytical purposes.
According to the Publicis Sapient report, “An enterprise AI platform, in contrast, runs within a company’s infrastructure—whether on-prem, private cloud, or hybrid environments—and enforces strict access controls, encryption, and auditability.”
HOW TO BUILD AN ENTERPRISE AI SOLUTION
The first step in building an enterprise AI solution is to identify the business problem that the AI solution will solve.
Aligning the AI tool with the organization’s goals and objectives can ensure the platform is topical, impact-driven, and in sync with the overall business strategy, as outlined in an article written by Akash Takyar, founder and CEO at LeewayHertz.
Takyar asserted that gathering and assessing company data is the next critical step in building an effective enterprise AI solution, indicating: “The quality, quantity, relevance, structure, and the process of cleaning and preprocessing the data are key considerations.”
An array of AI algorithms and technologies are available, and the next step is selecting the appropriate ones for the business problem. The different types of AI algorithms and technologies include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Each learning type is suited to solving a specific business problem.
“The design and implementation of the data pipeline involves a series of decisions regarding data sources, storage options, and the processing steps required. Scalability, security, and efficiency are important considerations when it comes to designing the pipeline,” Takyar explained. “The pipeline must meet the requirements of the AI models and the business problem that is being solved.”
Then, the data from the pipeline trains the models, and the algorithms generate predictions. The training phase is an iterative process that involves adjusting the parameters of the models to optimize their performance.
Integrating the AI solution with existing enterprise systems and processes involves connecting the AI solution to databases, APIs, or other enterprise systems to exchange data and information. The integration phase is important for the organization’s existing systems and processes, offering the opportunity to align with the AI solutions.
“[C]ertain measures, such as tracking key metrics including accuracy, speed, and reliability, can be taken. Performance monitoring can help identify potential concerns with the AI solution, such as data quality problems or algorithmic inefficiencies, and [the organization can] make improvements as needed,” Takyar recommended.
Monitoring and evaluation involve continuous reviewing of the performance of the AI solution, assessing its impact on the business, and making improvements and refinements.
Finally, making ongoing improvements to the AI solution includes updating and tweaking the data pipeline, algorithms, and existing enterprise systems and processes. The AI solution must continue to improve over time to meet the diverse needs of the business.
Organizations should routinely stay up-to-date with new technologies and train their employees in them. Fostering a culture where staff can attend conferences and workshops, conduct research, or engage with experts in the field will ensure the business grows alongside its employees.