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The world is growing at a burgeoning rate owing to the latest technologies and booming competition rate. Therefore, in this, ever-changing digital landscape, it has incredibly imperative for businesses to maintain a competitive edge to stay ahead of the curve.

They must evolve with the growing automation and streamline every minute of business processes for strengthening decision-making. That’s where the role of **enterprise AI applications** comes into the picture. By leveraging the power of enterprise AI solutions, you can boost your productivity, profitability, and efficiency on a larger scale.

But what is Enterprise AI Solution?

How does it work?

**Understanding the Enterprise AI Solutions**

Enterprise AI Solutions are swiftly transforming how enterprises used to function. From manual processes and traditional methodologies, it shifts the business towards automated processes.

Such AI solutions have the capability to manage, and process voluminous data and automate operational tasks with just one click.

AI-based enterprise-level solutions are leveraging enterprises in enhancing their operational capabilities, optimizing costs, strengthening decision-making processes, and improving the overall functioning of the organization.

For an instance, AI-powered chatbots are working significantly in communicating with users at any point in time and helping them resolve their queries instantly. Such an AI solution is improving the customer experience and through a predictive maintenance system, it is reducing maintenance costs and downtime.

**Enterprise AI Solutions at RadicalLoop**

Being a powerful framework, Enterprise AI solutions are accelerating the full life cycle of enterprise at scale. It gives organizations a structured yet flexible way for developing AI-driven solutions today and for the future.

From scaling proofs of concepts to production scale systems, AI services are all here to deliver scalable results by incorporating specific guidelines drawn from the world of service-oriented and event-driven architectures.

At RadicalLoop, our team of engineers and AI scientists works significantly in improving the efficiency of business and avoiding duplication of efforts at the helm.

We are driving manual operations toward automated low-value tasks by improving the reproducibility and reusability of all work. Through AI solutions, we are eliminating the cost-consuming and time taking activities like copying, extracting, and managing the data quality with the strong support of automated processes.

**Can Enterprise AI solutions tackle the Skill Gaps?**

According to [Accenture](https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments), companies that deploy AI Solutions at scale tend to achieve 3 times higher returns on AI investments than enterprises that are in the AI proof-of-concept phase. However, AI deployment can be challenging for enterprises as it involves much more than just implementing pre-built AI models for arbitrary business procedures.

Today, AI Solutions not only serve as a focal point in improving the efficiency of enterprises but also in tackling the skill gaps from onboarding new talents. With a professional team of AI scientists and ML Engineers, it helps enterprises in developing and supporting the best practices at the helm.

**Layers of AI Enterprise Platform**

The layers of an enterprise AI platform that collaboratively help businesses in leveraging AI capabilities are -:

**1. Data & integration layer**

The Data & Integration Layer gives access to the enterprise data. It includes governance elements, and data transformation and helps further in managing data repositories and sources of data.

 **2. Experimentation layer**

Here, AI scientists develop, test, and iterate their hypotheses. It brings out automation in model selection, optimization, and interpretability.

**3. Operations & deployment layer**

This layer is integral for model governance and deployment. Here the risk assessment is conducted for validating and interpreting the proof of model, fairness and transparency, and model bias. It includes the results of experiments conducted by the AI DevOps engineers and system admin.

 **4. Intelligence layer**

The Intelligence Layer is the product of technical solutions and it works in conjunction with cognitive experience experts. It uses dynamic service discovery in intent with identification and provides a flexible response platform in orchestration with many APIs.

**5. Experience layer**

The Experience Layer interacts with users through technologies like gesture control, conversational UI, and AR. It is typically owned by a cognitive experience team that includes traditional user experience workers, creative individuals, conversational experienced workers, and visual designers who craft impressive and meaningful experiences through AI technology.

**How to get started with Enterprise AI Solutions for your business?**

As discussed, every business must formulate an enterprise AI strategy for accelerating and successfully scaling AI adoption. Let’s discuss how you can incorporate such AI solutions for your business -:

**1. Map out your business strategy**

Artificial Intelligence is a powerful technology but implementation of AI isn’t the only goal. The main goal is a strategic formulation that will support your business processes. All you need to do is -:

- Review your current and future developed business strategy and check its relevance
- Align your business strategy with the existing and potential AI opportunities for your operations
- Determine where AI can add value to your business process and which alternatives you must incorporate
- Optimize unnecessary costs and eliminate the cost of failed AI projects that were executed without any concrete strategic roadmap

 **2. Prepare a data strategy**

Data collection, management, and scrutinization can make or break the AI system. As a result, for scaling AI solutions in your organization, you must formulate an AI strategy that strongly supports your data strategy. It includes -:

- Managing all components of the data lifecycle right from data collection and storage to integration, management, and retrieval.
- Make sure that your data strategy feeds your AI systems with realistic data and exact labels.

And, in the case of voluminous data, you must focus on the quality of your datasets. In case, if you do not have large datasets, then that shouldn’t stop you from implementing AI solutions into your business.

However, industry leaders often [argue](https://spectrum.ieee.org/andrew-ng-data-centric-ai) that small but high-quality and concrete datasets often deliver significant performing models in comparison with large but low-quality datasets.

**3. Selecting reliable technology infrastructure**

The important function of AI systems is computing power. According to [OpenAI](https://openai.com/blog/ai-and-compute/), the strength of AI lies in computing power. It is imperatively used for training popular AI models that have doubled every 3.5 years since 2012.

Thus, it is indispensable to have a strong and powerful tech infrastructure for developing and deploying AI models. However, cloud computations could be a cost-effective model to get started with AI initiatives. With on-premise infrastructure, specialized hardware could be a cost-saving option in the future.

**4. Prepare a cross-functional center of excellence**

Every organization requires a successful enterprise AI strategy. To prepare such a strategy, you need a dedicated business unit that coordinates and simultaneously oversees all AI initiatives and processes in your organization. Such a business unit must evaluate the AI use cases and prepare a roadmap for the organization.

And, according to a [report](https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence), almost 37% of the large companies in the US have already prepared a cross-functional center of Excellence.

It is said that organizations must invest in building AI CoEs with a broad spectrum of skills, that consist of AI and IT experts, business executives, and domain experts for specific use cases. This would help organizations to -:

- Resolve the gap between executive decision-making and AI implementation
- Develop a unified vision for AI across the enterprise
- Standardize common practices and simplify communication.

**5. Develop AI responsibly**

As the world is moving towards AI systems and their integrations, people are increasingly concerned about the ethical implications.

Therefore, every organization must take the responsibility to make fair and unbiased decisions that don’t impact the public. Any non-legitimate decision can put your company at risk of lawsuits or reputational damage.

Every business leader and AI professional must introduce this law in their workplace and familiarize themselves with [AI ethics](https://research.aimultiple.com/ai-ethics/) and principles including fairness, transparency, privacy, and security.

**The Final Words**

Building an Enterprise AI solution comes with a multitude of benefits for organizations. It helps them in automating the processes and streamlining the business operations thus providing a competitive edge at the helm.

In today’s landscape where data is the king, Enterprise AI solutions help organizations in harnessing the power of data for making informed decisions, optimizing the cost structure, and devising strategies based on predictive maintenance.

Furthermore, it offers numerous business opportunities for enhancing the operations of your organization.

Want to bring automation into your enterprise? Call [RadicalLoop](https://www.radicalloop.com/) to discuss the application of Enterprise AI solutions for your business.

Bring **AI into the enterprise** and take your business to the next best level.

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Discover the benefits of building an Enterprise AI solution for your organization. Streamline operations, optimize costs, and make informed decisions with the power of data.

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