What is enterprise AI? The fundamentals
Enterprise artificial intelligence (AI) broadly refers to any AI system, platform, application, or other iteration of AI technology that's specifically designed to benefit business operations for enterprise-level organizations.
By necessity, enterprise AI solutions function at a large scale and scope appropriate to their use cases. This means that an enterprise AI deployment often encompasses hundreds of different models, apps, and other tools. The resources that facilitate AI development and management— such as MLOps platforms that oversee machine learning (ML) models—also fall within the greater enterprise AI ecosystem. The same is true of the data infrastructure and storage solutions that allow AI to function in the first place.
In other words, no single AI software can be the one-stop shop for an enterprise's intelligent automation needs—though certain types of solutions have particularly pivotal roles in the ongoing success of AI services.
How did enterprise AI become so important?
AI—and its associated technologies, like machine learning and deep learning—have been present in enterprises to varying degrees for at least a decade. But over the course of the past several years, "present" started to look more and more like "pervasive." And most projections of the technology's prospects make it seem entirely possible that "ubiquitous" might be the best term to describe the scope of enterprises' usage of AI models and other solutions.
Consider the following findings from a Dataiku-sponsored 2023 IDC InfoBrief report on the current state and immediate future of AI:
- As of 2023, AI adoption is approximately three times greater than in 2019.
- About 50% of organizations surveyed by IDC's analysts stated that they actively planned to utilize AI in the next 12 months across a variety of business functions.
- Global AI investment could reach or exceed $301 billion by 2026, based on a 26.5% compound annual growth rate (CAGR) between 2021 and 2026. That would be about three times the funding allocated toward AI models and projects in 2020 and more than double the $150 billion expected by the end of 2023.
Overall, it's clear that AI/ML has become increasingly intertwined within the operations and service offerings of numerous enterprise-scale organizations.
Given the scope at which today's enterprises operate their AI/ML initiatives—and, more specifically, the volume of data required to support them in a multitude of different formats—seamless integration is a must. Facilitating this requires the strategic use of data management best practices alongside a reliable tech stack that includes cutting-edge analytics solutions.
Major enterprise AI use cases
After successful AI implementation, enterprises have countless potential uses for the technology. The following are among the most common—and effective—applications of AI/ML:
Virtually every enterprise that operates an ecommerce website uses a recommendation engine with an ML algorithm at its core. Before the customer makes a purchase, the model gathers data on everything from average time spent on product pages to the most-browsed item categories and even the times of day users most frequently shop.
Gradually, enough data will have been gathered to generate actionable insights that turn into the most relevant product recommendations and maximize the possibility of conversions. By making shopping more personalized, AI-powered recommendations also significantly improve the customer experience.
Virtually every enterprise expects some degree of periodic customer churn. But with AI-driven predictive analytics, large organizations enable themselves to develop reasonable projections of how mild, severe, or in between that churn will be. If necessary, they can then make contingency plans for mitigating the effects of losing significant business.
Additionally, AI data analytics empower companies to understand the factors that create churn, both before and after it occurs. Organizations will thus be informed enough to work to repeat—or improve upon—positive decisions and make strategic changes to avoid repeating mistakes.
Enterprises that face significant fraud risks—financial institutions, insurers, utility companies, and healthcare providers, to name a few—can't afford to let fraudulent activity go undetected or unmitigated.
By deploying AI at scale in conjunction with real-time data analytics, all of these organizations can respond to fraud before it does irreversible damage to the enterprise, its customers, or both. For instance, banks and credit card providers using AI tools can detect anomalous transactions and preemptively freeze cards. Similarly, electric and gas providers can use AI to detect potential meter tampering and dispatch a technician to verify the problem and proceed accordingly—or, if there are an alarming number of fraud indicators, remotely pause service.
Supply chain management
Global and multinational corporations with complex supply chains know just how easy it is for one link—be it a supplier, freight carrier, or order fulfillment center—to disrupt everything. Leveraging AI in supply chain management greatly reduces the likelihood of such butterfly-effect issues.
AI/ML capabilities enable intelligent demand forecasting that triggers production based on short- and long-term needs, automated inventory control to replenish raw materials, and predictive maintenance to minimize equipment or fleet downtime. In conjunction with advanced analytics, AI-driven supply chain management also allows enterprises to predict potential transport disruptions and create more efficient routes.
Obstacles to AI initiatives—and how to overcome them
A Gartner survey from 2022 reveals a notable complexity of enterprise AI adoption: While 80% of queried executives are bullish on AI's potential, the research also demonstrates a gap between belief in AI and making it a fundamental aspect of day-to-day enterprise operations.
About 54% of AI/ML models make it into production from their pilot phases. This is a notable increase from the technology's early days, but still less than what one might expect from enterprises with enterprise-scale resources.
The biggest reason for this, per Gartner, is difficulty with connecting AI models to tangible business value—despite the technology's aforementioned use cases, which are far from its only applications. There are also security concerns, as 41% of survey respondents claimed to have experienced an AI-related data breach.
There are several clear and tangible ways for enterprises to mitigate the risk of developing AI projects and not following through with them.
- Begin AI initiatives with concrete goals. For example, if you want to automate processes within the marketing department, ensure this has tangible business value. That could mean increasing productivity, creating more effective collateral, or integrating messaging across channels. If AI will simply make aspects of the job slightly more convenient, that's not necessarily worthwhile. To ensure AI projects remain purpose-driven, consider appointing experts, including data scientists and AI engineers, to carefully review all proposed model use cases.
- Train and deploy models at scale. You must think of model training, deployment, and ultimately operationalization in terms of thousands and possibly even millions of models. This will require building and maintaining dedicated AI/ML data pipelines, creating a feature store to facilitate data preparation for model training and retraining, and using MLOps or ModelOps frameworks to ensure that model operationalization remains agile.
- Practice careful change management. It's all but guaranteed that some in your enterprise will be skeptical of large-scale AI implementation. This is understandable, particularly given the ongoing controversies regarding generative AI. Counter these attitudes nonconfrontationally by consistently citing the long-term value of various AI projects. Also, in the previously mentioned Gartner report, in-house AI training is considered just as important as external hiring: Perhaps you can overcome doubts about AI by pitching it as a new skill set to be learned.
- Maintain responsibility and security. AI/ML models remain susceptible to bias and can, in the wrong hands, become shortcuts rather than true solutions. Additionally, AI is generally more vulnerable to human compromise than external breaches. As such, you must implement firm governance over training data and those tasked with inputting it—and institute zero-tolerance security policies.
- Use analytics to assess AI project performance. An analytics platform that can integrate AI/ML model scoring with business data will be particularly useful for this purpose, especially at the enterprise scale.
Optimize the value of enterprise AI with Teradata solutions
Teradata VantageCloud is the most complete cloud analytics and data platform for maximizing the effectiveness of enterprise AI models.
In addition to VantageCloud's seamless data integration and warehousing capabilities, the solution's powerful ClearScape AnalyticsTM engine is effectively tailor-made to facilitate accelerated model training and deployment. You can scale AI operations with ease to match your enterprise's needs.
VantageCloud also includes numerous integrations with partner AI solutions to make it easier than ever to operationalize AI and let it reach its full potential. To learn more, read about our collaborations with Google Cloud's Vertex AI and Dataiku. Also, read our white paper and discover Teradata's versatile Analytics 123 strategy for enterprise AI.