Business Redefined By Enterprise AI Adoption

When Alan Turing published his paper titled Computing Machinery and Intelligence in 1950, he was trying to answer a simple question - can machines think? He introduced the Turing Test in the paper, where a human had to converse with a bunch of people. Among the people, there was also a machine disguised as a human. The goal was to check whether the human would identify the machine or not.

Though the test was not definitive, it opened doors to Artificial Intelligence that we see around us now. Since 1950, AI has evolved to be much smarter and intelligent, taking up a plethora of roles. But, how exactly has it evolved? What improvements has it brought to people and businesses? 

In this blog post, we will answer all these questions. We will also discuss how companies are adopting AI in their business and the challenges in AI adoption. Lastly, we will end the blog with solutions for a smooth AI adoption in the enterprise and a short note on the way forward.

The Rise Of Enterprise AI Adoption

From recognizing patterns in images to identifying objects to enabling self-driving cars, AI has come a long way. Enterprises are utilizing AI in various ways to augment their business.

Teradata conducted a study titled State of Artificial Intelligence for Enterprises, which spans across America, Europe, and the APAC region. Out of the 260 decision-makers interviewed, 80% of respondents revealed that they were using AI in one form or another.

The study also revealed the major business areas where AI is adopted - product innovation, customer service, supply chain, security, sales, marketing, and finance.

In another survey conducted by McKinsey to gauge AI adoption by industry, 50% of the companies reported that they have adopted AI in one business function at least.

Such a growing adoption of AI gave rise to what we call Enterprise AI.

Enterprise AI refers to making AI the core of your business strategy. It drives every area of your business to make decisions based on data. It is not a one-off use case but a strategy deeply imbibed in your business decisions. The main idea of using enterprise AI is not just for monetary gains but for creating an efficient and sustainable future.

Enterprise AI enables organizations to create a culture where humans and AI co-exist. It democratizes AI by involving every employee in the processes governed by AI. Such a culture would help enterprises leverage both technology and people to iterate processes rapidly, with much-reduced efforts.

How Can AI Be Adopted In Business?

For adopting Enterprise AI, companies need to build AI systems at an organizational level. These systems include your data, your algorithms, and all other technologies that are required to build a sustainable AI system.

The bare requirements you would need to have such systems in place are:

  • Large data sets formed by collecting relevant data from every area of your business
  • A reliable infrastructure to store these data sets, iterate AI models and deploy them
  • Qualified talents like data scientists, data engineers, ML engineers, and analysts who can drive the model building process

Being equipped with these resources solves one part of the problem. The other problem is utilizing these resources to successfully build and deploy models.

Enterprise AI Adoption Challenges

When it comes to building models, you need to ensure that your enterprise AI strategy holds access to all the data collected from various business areas. Having a centralized system to store such data ensures smooth workflow between different models. It will also provide a clear understanding of what data is used where.

Once all the data is in place, the next challenge is to use it to build AI models for specific use cases. The major challenge here is the inflexibility of choosing the best tools to build high-quality models. Most vendors are good with one particular tool, while the rest of the tools offered are substandard. Another issue is the model lock-in that is thrust upon by the vendors.

The last major issue of enterprise AI is monitoring all the models deployed in various areas of your business. Monitoring is crucial to understand every process and address the challenges of AI adoption. Your models process large chunks of data and make decisions based on the learnings from that data. Ensuring the decisions are fair, unbiased, and accurate should be of utmost importance to your enterprise.

The Ideal Enterprise AI

Having the tools to build an AI model and building the model are two different things

Considering all the requirements and challenges of adopting Enterprise AI, an ideal solution would be as follows.

  • Data: Since AI is driven by data, collecting large data sets is imperative to ensure accuracy. Using APIs to collect data at every touchpoint is the ideal solution. The collected data needs to be stored in a centralized database that is easy to access.
  • Infrastructure: As discussed in the previous point, storing the collected data is a big challenge. If your enterprise intends to dive deep into AI, you will probably be deploying many models. You will need ample infrastructure in terms of machines and servers to process, store, and replicate the models. To ensure infrastructure never poses a challenge in AI adoption, enterprises will have to be ready with their preferred infrastructure choice.
  • Monitoring: Since AI is used across various business areas, it becomes crucial to monitor every aspect involved with it. Tracking the data, the people using it, understanding the decision-making of the AI - all these things are imperative to know.
  • People and Time: Having the tools to build an AI model and building the model are two different things. This is a realization of 87% of companies that have failed to deploy ML models. After data, the lack of the right talent to carry out the model building process is the biggest challenge in AI adoption. The onus falls on the leadership as well. Spending large amounts of money on people and tools does not get you the results. AI adoption needs to be carried out by people who are experts in that domain. They need to be assisted by industry experts who understand their industry. Ideally, companies need to hire the right and diverse talent. They have to ensure that the hired talents are trained regularly with the constantly changing technological and industrial demands.

The points mentioned above would be part of an ideal solution. Companies need the right strategies to pull off such an ideal solution. Most companies fail at this, which affects their enterprise AI adoption. But, chasing perfection is an ongoing process. Companies have to ensure that they take the necessary steps in their capacity and make the best use of their resources.

Boosting Enterprise AI Adoption

To ease the adoption of enterprise AI, a group of AI experts and industry influencers have come together to form Attri. Our goal is to simplify enterprise AI adoption where AI is leveraged to make progress. We are working towards a future where AI is accepted as a catalyst that enhances human capacities to solve problems that were not solved before.

We believe in technology and want to leverage it to change the way enterprises do business. Concerning this goal, we help organizations in two distinct ways.

  • We provide an interoperable platform to build, integrate and scale AI models
  • We build AI models that address major business problems so that enterprises can readily implement them into their infrastructure

We are constantly working towards improving the human-technology relationship. With this goal in mind, we are just getting started on our journey to the future.

Amogh Balikai

Amogh Balikai

Amogh is a technical content specialist at Attri. With a background in computer science, he strives to understand and simplify ML for diverse readers.