If there is one application category where you can see the implication of AI at large, it has to be the Enterprise.
Every enterprise app is a sea of data waiting to be analyzed and used for making better-informed business decisions. In case you have missed the incorporation of AI in your enterprise app, this is where you should start.
Below are four AI Technologies that you should incorporate into your business processes to make better informed and intelligent decisions. Incorporation of these technologies would mean paving a way into an enterprise structure that takes real-time data and employ preventive analysis result once a predicted event occurs.
The technology is at the center of AI strategy of a number of businesses. A typical machine learning ecosystem includes algorithms, APIs, model deployment, and development tools, along with many other elements. In the Machine Learning platform, computers are enabled to learn without the need of some explicit programming support.
A number of innovative companies like Google, Amazon, and Microsoft are making use of the machine learning technology. Tesco, the multinational grocery retailer, uses machine learning algorithms throughout its business, both for its internal applications, for driver routing, and in the customer-facing apps by integrating them with Google home assistant hardware.
Natural Language Processing is involved with enabling interactions between human and computers, in a language that humans talk in. It makes use of computer programs, which can not just understand the written or spoken human speech, but also answer in the same language and context. Software and Devices such as Apple's Siri, Amazon's Alexa, Google Assistant, and Microsoft's Cortana make use of NLP for understanding and responding to users' queries.
NLP Technology has been used vastly in support, service, and transactions processes and comes with a massive potential for improving company's internal operations.
The second Mind, a voice activated platform has found its place in a number of board rooms. All you have to do is ask the platform last year's sales revenue and it displays the result on a screen.
Hardware Devices with AI
To propel deep learning at a much atomic level, companies have been developing AI-based hardware. Straight from chips to Graphics Processing Units (GPU), the AI based enterprise generation is moving on to integrating AI into their hardware as well.
Earlier the chips were designed in a way that information had to be fed into them and they used to process it. But now, new age chips are being developed that can learn and keep growing to match the changing user queries and processes.
The whole idea of integrating AI with hardware devices is based on the requirement that the users should not get restricted to AI based apps and software for making processes more efficient. the hardware that they are using should also be smart enough to support a disruptive technology like AI.
Working like Virtual assistants, Virtual Agents aim at offering the similar 24*7 support to the employees. The sales team no longer have to wait for the product team to send them the product portfolio, by asking the virtual agent to fetch the document, the process will become much faster.
By getting integrated with NLP technology, Virtual Agents can offer a series of real-time help to the employees.
While still majorly used in the customer service department, it is only a matter of time when Virtual agents will find incorporation in a series of different business processes, creating seamless conversations between the different enterprise apps and devices.
Now that you know the four AI technologies that you should incorporate in your enterprise application, here are the ways you can embed AI in your app if you are just starting with the technology -
Since you are a new entrant in the world where AI meets enterprise, the idea is not to get too aggressive and start small. These steps are layout according to their technical difficulty level. Move on to Step 2 only when you have mastered Step 1 and have seen the results. Likewise, look at advance level Step 3 once you are done with the intermediate level of Step 2.
Step 1: Start with APIs
The first step that you should take as a brand who is new to the world of AI, is integrated AI based APIs in your current mobile applications. Make your current enterprise app intelligent by integrating them with APIs that work around image pattern recognition, NLP, speech to text, language understanding, etc.
Step 2: Develop and Deploy Custom AI platform in Cloud
This step consists of acquiring the data from a number of present sources and then implementing them in a tailor-made machine learning platform. This requires the creation of data processing structures, identifying right algorithms, testing and training those machine learning prototypes, and lastly, deploying them for production.
ML as Service takes the data and reveal the end model as an API endpoint. The advantage of this lies in using cloud infrastructure for testing and training the models. Using this, users would spin up the infrastructure that is powered by the advanced hardware setup that is based on FPGAs and GPUs.
Step 3: Run AI Platforms On-Premises
Once your enterprise app reaches a stage that it now requires customization to a great extent and need to comply with policies related to data security, it is time to run open source AI platform with the help of your team, in-house.
As businesses, you will have to spend for acquiring modern hardware that is based on GPUs and SSDs that are designed for parallel data processing.
You would also need to employ data scientists who are skilled in building customized models that are based on a series of open source platforms. A major advantage of this methodology is - everything from the data acquisition to the real-time analytics functions in-house.
The steps, while easy to read are a lot difficult at their execution stage. If you need any help with incorporating AI to your existing Enterprise App, contact the team of AI experts, today.
About the Author: Tripti Rai is a content manager at Appventiv.