Businesses are undergoing larger changes than ever due to the new wave of artificial intelligence (AI). AI, machine learning, and deep learning technologies are heavily used by organizations across all sectors to boost their output, profits, and business outcomes. Effective adoption of AI in an organization is a time, resource, and skill-intensive process. Comprehensive and high-quality data, powerful and secure computing infrastructures, and expert data scientists are critical to the success of any AI project. BCG reports that only 23% of organizations have consciously incorporated AI-powered solutions into their businesses, knowing the benefits they can reap.

In addition, with the intense competition and the rapid pace of technological progress in this field, a shortened time to implement becomes a necessity. Quick adoption of artificial intelligence in business especially for starters facilitates rapid innovation, faster time-to-market, enhances agility, and improves revenue growth potential

This article investigates the barriers to quick and meaningful AI adoption and analyzes the benefits that can be achieved through faster implementation. Using Akaike as an example, the article outlines strategies that can be applied to shorten the time-to-implement AI projects.

Barriers to AI adoption

  1. Determining the right data sets

Most of us are aware that AI systems are driven and developed by quality data. This is why AI implementation should begin with the right set of data. The fact that different types of data will be flowing across organizations makes it hard to determine which ones to use.

To improve AI’s decision-making and learning process, we must identify and use the right data set. To accomplish this, businesses may need to contact experts in the field of Artificial Intelligence who can guide them through the correct pathway and approach to enable transformative digital experiences.

  1. Data Security and Storage

To learn and make intelligent decisions, most AI applications rely heavily on data. Businesses can run into storage issues when utilizing large volumes of data. Additionally, data-driven automation may cause security issues in business operations. As a result, businesses that want to implement AI must embrace the best and right data management environment. It will be easier for organizations to access siloed data for AI and ML projects in such a data management environment, which will also increase the security of sensitive data. 

  1. Infrastructure

For most organizations, replacing obsolete infrastructure and conventional legacy systems remains a significant task. The majority of AI-based systems operate at high computational speeds. If your company has a sizable infrastructure and top-tier processors, AI-based solutions will be able to operate more quickly.

According to a recent McKinsey analysis, companies that utilize AI are the ones that are prepared to expand their operations beyond the digital frontier. Businesses that intend to use AI should think about developing a stable and adaptable infrastructure fully compatible with AI-based solutions or apps.

  1. AI Integration into an existing system 

Many readers would be surprised to learn that most firms find it difficult to integrate AI into their current business systems. Difficulty in integration into legacy processes is a primary roadblock to implementing AI in many firms.

Businesses seeking to integrate artificial intelligence (AI) properly into their current systems will need the assistance of AI solution providers with in-depth knowledge and experience in the field of AI, who can aid in the adoption process from conception to deployment.

  1. Skilled Data Scientists and AI experts

AI algorithms play an integral role in business intelligence operations. Businesses wanting to apply AI effectively to their operations should have skilled AI experts pioneering AI adoption. Even after necessary AI algorithms have been developed, maintaining and adapting the AI and ML models to changing business environments is an ongoing and involved task that also requires a skilled workforce.  In general, deploying AI in business can be a difficult process that is both time and resource intensive. However, many firms find that investing in AI is justified due to its potential advantages, which include increased productivity, cost savings, and better decision-making. In the last four years, the number of organizations requesting AI technology has increased by 270%, and it has tripled in the prior year, according to a Gartner poll. 

Now, let us discuss the competitive edge that the rapid adoption of AI can bring to a business. 

  1. Rapid Innovation

Front runners of AI adoption have the edge of being ahead of the innovation funnel. Given the brisk pace of technological change in the field of artificial intelligence, machine learning, and deep learning and the ever-growing applications based on them, a business that understands and incorporates AI in its operation is at the front of the line of innovation and can quickly differentiate its services and products from its competitors and stand out. These innovative services and products can enable it to capture and even at times, create new markets and gain market share from its competitors who are behind in AI adoption.

  1. Enhanced revenue growth potential

A rapidly innovating business as mentioned before, can capture and create new markets and gain market share from competitors, thereby showing an enhanced revenue growth potential.

  1. Enhances Agility

Businesses struggling with operational inefficiencies have found that artificial intelligence can be a viable alternative to manual work. A key component of company solvency has been artificial intelligence. How? AI tools identify areas for improvement and provide secure remote workspace solutions, which have been critical to business success. Using artificial intelligence tools allows businesses to test new scenarios before committing to a product or solution, allowing them to respond to unfamiliar situations more flexibly.

  1. Faster time-to-market of products and solutions

Several repetitive and time-consuming tasks can be automated through artificial intelligence, including data analysis, testing, and prototyping, thereby reducing the time to market products and services. Businesses can quickly analyze large volumes of data with AI and uncover patterns and insights that are otherwise impossible to uncover with humans. AI-driven processes and methods have been shown to be less error-prone and hence enable businesses to deliver high-quality goods, faster, giving businesses a competitive advantage. 

Akaike’s Strategy to drive rapid AI Adoption

Akaike aims to build AI models that are efficient, provide maximum human impact, and make businesses smarter. Here we will discuss three common challenges that Akaike faces when reducing time-to-implement in AI projects, and provide solutions to overcome them. 

  1. Talent Shortage 

AI is driven by data and hence requires skilled data scientists for effective adoption and implementation. A talent shortage, therefore, implies delays in the execution of AI projects. To alleviate the dependency on AI and data experts to the extent possible, Akaike designed and created a platform, called no-code low code platform. People who don’t know how to code or don’t have the time to code can use low-code and no-code development platforms. It is a UI-based AI tool, which has UI elements to create models, which create and visualize data sets.  No-code and low-code frameworks are based on coding languages like PHP, Python, and Java, but end users are unconcerned with the details. 

For example, low-code AI search allows developers to integrate data sources, create employee and customer-facing search apps, and leverage AI and machine learning.

  1. Data shortage

Data scarcity is a major bottleneck to Artificial Intelligence (AI) production. AI/Natural Language Understanding (NLU) projects can fail for several reasons, lack of meaningful data being a primary one. To address this issue, Akaike focuses on generating synthetic data that captures the semantics and characteristics of real data.  Synthetic data can be used to identify inherent patterns, interactions, correlations, and hidden relationships. The data is generated algorithmically and is used to validate mathematical models, train machine learning models, and stand in for test data sets of production or operational data.

  1. AI Models: What’s Possible, what’s Not

There is no one-size-fits-all in AI. Different AI models work better in different scenarios depending on the kind of data, artifacts, and other factors. However, the good news is that pre-existing models developed for other projects can be applied to new problem domains and their efficacy studied quickly. In other words, a lot of re-use is possible in the world of AI. Akaike follows this strategy. Rather than build AI models from scratch every single time, Akaike first investigates if one or more existing AI models can be fine-tuned and applied to solve the problem at hand. This approach also helps in showing a client what is feasible using AI, pretty quickly and helps Akaike get the customer buy-in.  If needed, the models could be extended, tailored, or even built-from-scratch, once the customer is on the same page.  

Wrapping Up

In today’s world, artificial intelligence is reshaping business. It is clear from the above information that adopting AI can scale your business and give you a competitive advantage. Currently, many organizations are implementing AI technology moderately and have aggressive plans for the future. The use of AI technology can eliminate human errors, improve decision-making, speed up data processing, and eliminate repetitive tasks.

Looking to add artificial intelligence to your business? Then you’ve come to the right place, Akaike combines business intelligence with technology expertise to provide your business with the best results and will guide you through implementing artificial intelligence for your business requirements with a cost-effective approach.

Edited By: Naga Vydyanathan