What is Enterprise AI, and How Does It Power Process Automation at Scale?

“Enterprise AI is an umbrella term to refer to an enterprise’s AI strategy, policies governing AI use, its suite of artificial intelligence (AI) software solutions, and the overall AI infrastructure within the organization.”

Technically speaking, enterprise AI is an amalgamation of an organization’s data, data infrastructure, ML models, computing infrastructure, MLOps, automation, regulatory compliance, and AI strategy. Enterprise AI is an abstract phrase that refers to how all these components are put together to function as intelligent business growth drivers.

Enterprise AI Examples

From automotive and smart energy grids to retail, healthcare, education, and finance, enterprise AI is creating waves in every single industry. Here are some of the prominent enterprise AI examples- 

• Redmil, a finTech giant in India, implemented AryaAI’s artificial intelligence-driven identity verification solutions and reduced their fraudulent account creation activities by 98% within 6 weeks.

• Chobani, an American yogurt-making company, used Enterprise AI solutions from SAP Concur to automate expense management and saved 75% of the time and resources spent on expense entry, audit, and issue flagging.

• A large bank partnered with Arya.ai to deploy an AI-led cheque-clearing system to not just expedite its cheque verification and clearance process but also cut costs by 55%. Their enterprise AI solution used predefined rules to verify cheque details and automated the verification process for matching signature, MICR, and other cheque details.

• MABKargo, an air cargo arm of Malaysia Aviation Group (MAG) used enterprise AI for reinventing unit load device (ULD) build planning and loading processes. They improved ULD time by 83% per flight. That’s an annual uptick of $5.5 million.

AI alone is just intelligence. The true value of this intelligence is harnessed when it is deployed for process automation. “What’s process automation,” you ask.

Automation in Enterprises 

Enterprises rely on various process or workflow automation techniques. This is achieved by stitching together automation scripts, software, and system integrations. 

There are varied types of process automation. For instance,

• RPA (Robotic Process Automation) is used to automate time-consuming, repetitive UI interactions with rule-based bots. 
• BPM (Business Process Management) software goes beyond basic RPA and helps automate end-to-end enterprise workflows. 
• iPaaS platforms that help you interconnect multiple applications (CRMs, Cloud platforms, ERPs, On-premise apps, and even RPA tools) using APIs. Thus, enabling seamless data exchange between applications to streamline business processes while eliminating your reliance on manual tasks. 
• AI-led process automation is an approach in which you use AI solutions to improve the efficiency of process automation systems further. 

AI-Powered Automation in Enterprises

AI-powered automation transforms the traditional paradigm of process automation by embedding intelligence directly into the workflow. 

It unlocks fierce possibilities for enterprises—financial fraud detection, real-time app feature deployment/roll-back, digital-assistants-led telehealth, CX personalization at scale, 24/7 customer service, cost reduction across business verticals, predictive disease prevention/cure, and whatnot. 

You get better at everything if you can strategically implement AI-led process automation systems. Speed. Scale. Accuracy. Efficiency. Costs. Everything.

However, Enterprise AI process automation projects aren’t without challenges. There are many inherent limitations or frictions that CFOs and CEOs have to overcome for Enterprise AI projects to take off-

• High upfront costs of developing enterprise AI solutions and maintaining them. 
• Complex implementation & integration of different moving parts of AI process automation can quickly morph into technical debt.
• Limited adaptability to unstructured tasks, i.e., the AI process automation solution falls short when human-level judgment, creativity, and critical contextual awareness are expected (yup, despite 128K contextual window availability).
• The outcomes heavily rely on the quality of data used to train the models, so, there is a problematic dependency on data quality.

And that’s where ‘Agents’ enter the picture.

Agents are the next stage of enterprise AI process automation.

• AI Agents are self-managed i.e., autonomous with logical and analytical capabilities to automate business decision-making and maintenance. 
• Agents can collaborate without us having to predefine the automation or decision-making logic akin to coworkers collaborating on projects.

However, AI agents do not completely eliminate the security, privacy, and ethical (bias) concerns of enterprise AI. But they do offer significant advantages in addressing-

• Regulatory & compliance challenges
• Scalability challenges of legacy systems
• Handling exceptions & edge use cases
• Real-time decision-making by accurately sensing/comprehending context & emotions. 

Benefits of Enterprise AI

Facilitates Better Governance

Regulations and compliance have been a pain for small, medium, and large enterprises across the globe. More so in the USA and Europe. But that’s about to change now. 

With enterprise AI solutions, 

• You can ensure that your organization strictly complies with regulations and policies. Enterprise AI applications are designed to proactively detect policy violations or deviations in real time. 
• You can define governance rules within your enterprise AI software for policy enforcement, access control, data security, anomaly detection, and mitigation.

Speeds up Innovation & Value Delivery

For the past few years, “Do more with less” has been one of the most popular phrases doing rounds in corporate chambers across the world. Today, enterprises are expected to innovate at a breakneck speed. But innovation demands yielding awesome ideas, experimenting, and then hatching those ideas into products/services to deliver value to the end users. By its very nature, it’s a costly affair. 

Very few CFOs have an aggressive appetite for funding innovation projects. Most CFOs fret R&D propositions. But to the delight of CFOs, enterprise AI software can significantly reduce the costs associated with R&D projects with AI-driven simulations, predictive modeling, market testing, demand sensing, pivoting solutions, and even assisting humans with creative ideas.

Increased Operational Efficiency with Automation

• Intelligent process automation helps with enterprise operational efficiency by automating complex, resource-intensive repetitive manual tasks. This frees human talent to focus on more creative and critical tasks. 

• Enterprise AI solutions are self-learning systems that improve over time, continuously refine their approach, and, accordingly, optimize resource allocation and utilization. 

Cost Reduction Across Business Functions with Resource Optimization

The direct benefit of enhanced operational efficiency is the reduced costs, i.e., costs associated with talent, resources, and business operational risks. With process automation, predictive risk mitigation, optimized workflows, automated compliance, and governance, enterprises save a significant chunk of money. Tons of money. Enterprise AI solutions fix your money leakages, and this directly helps with cost reductions.

AI-enabled Scalability

Scalability of operational & engineering processes with high predictability, accuracy, and visibility is a core benefit of enterprise AI solutions. Real-time data insights help automate decision-making, which gets leveraged to scale your AI process automation software. Besides, enterprise AI software is usually architected not to operate in a siloed manner but rather with a holistic approach i.e., access to enterprise-wide resources, processes, and data. This is to ensure that various dependent business processes can be effectively carried out simultaneously or in a particular fashion as per the business needs.

Use Cases of Enterprise AI

Automated customer identity verification, transaction fraud detection, logistics route optimization, predictive machine maintenance, personalized L&D for employees, enterprise energy optimization, and intelligent engineering infrastructure observability are all use cases of Enterprise AI. There can be 100s of more use-cases of enterprise AI applications, depending on your industry & business function. 

All industries could equally utilize artificial intelligence software capabilities and translate them into business benefits. Here are some of the most prominent industry-specific enterprise AI software use cases-

Challenges in implementing Enterprise AI

Here are the top 5 monumental challenges that enterprise leaders need to solve-

1. Legacy systems in enterprises have siloed data across functions. Not to mention, the data could be unstructured and fragmented. The challenge for leaders envisioning implementing AI in enterprises is to consolidate data from these systems into their AI infrastructure. Data cleaning, mining, ETL pipelines, and data governance processes need to be established.

2. Your legacy infrastructure may not be capable of supporting your AI dreams. You need massive computational resources, such as GPUs and TPUs, that are capable of handling parallel processing and tensor operations at scale. You need not just huge budgets for this, but also a great team to do things right.

3. You have to overcome ML model drift, underfitting, and overfitting challenges. You need to fine-tune AI models and hyperparameters for best performance. Then evaluate everything on production-grade data. This process is time-consuming and resource-intensive as a lot of R&D is involved.

4. AI bias mitigation and establishing transparency-infused trust is another key concern. It is important for regulatory compliance and ethical confidence. AI models can be vulnerable to adversarial attacks, giving rise to security issues. Enterprises need to solve this.

5. Scarce talent availability and subject matter expertise shortage in deep learning and ML infrastructure management can slow down AI projects, and the stakes are quite high in such projects to leave any room for talent churn to hurt the business.

It’s an uphill battle, but you have the necessary support in the form of plug-and-play enterprise AI solutions. The AI services ecosystem is also flourishing. You can add the right enterprise AI platforms to your software stack, and that can help with your organizational AI strategy.

Arya.ai: Production Ready AI for Enterprises 

AryaAI’s suite of AI applications for BFSI helps you expedite AI adoption in your enterprise. You get out-of-the-box solutions with simple API integration interfaces to start leveraging AI-led document processing, AI cashflow forecasting, AI customer onboarding, and AI-driven financial fraud detection in banking and insurance. 

You also have-

• AryaAI’s Apex Platform- AI API library that seamlessly implements AI in an enterprise at lightning speed. 
• AryaAI’s Nexus Platform- AI API gateway to orchestrate and manage your enterprise AI APIs at scale.
• AryaxAI- The most accurate explainability and alignment stack to deliver accurate, true-to-model explainability, monitoring, risk management, and alignment techniques essential for highly mission-critical or regulated AI solutions. 

Next steps?

For Enterprise AI implementation you can start from scratch if you can afford the resources and talent needed for the same, or you can use enterprise AI platforms like Arya AI. If you are running a financial organization, we highly recommend you check out our suite of AI solutions and our finance clientele’s AI success stories to get a good grasp of the possibilities with Arya AI.