Businesses are undergoing more significant changes than ever due to the new wave of artificial intelligence. Organizations across all sectors heavily use AI, machine learning, and deep learning technologies to boost their output, profits, and business outcomes. Implementing artificial intelligence in an organization requires significant time, resources, and expertise. The success hinges on the availability of comprehensive and high-quality data and the establishment of robust and secure computing infrastructures, alongside the involvement of expert data scientists. Organizations have consciously incorporated AI 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 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 projects.


Barriers to AI adoption

1. Determining the right datasets

Most of us are aware that automated systems are driven and developed by quality data. This is why the 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 artificial intelligence’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.

2. Data security and storage

Most artificial intelligence applications rely heavily on data to learn and make intelligent decisions. Businesses can run into storage issues when utilizing large volumes of data. Additionally, data-driven automation may cause security issues in business operations. Consequently, businesses seeking to implement artificial intelligence must adopt an optimal data management environment. This environment should minimize errors and prevent simple mistakes, such as mistyped numbers, which could have severe and costly consequences. It will be easier for organizations to access siloed data for ML projects in such a data management environment, which will also increase the security of sensitive data.

3. Infrastructure

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

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

4. AI Integration into an existing system 

It might surprise many readers that most firms encounter significant challenges when seamlessly integrating artificial intelligence into their existing business systems. A key obstacle to successful implementation is the complexity of integrating AI into legacy processes. To navigate this challenge effectively, businesses aiming to incorporate artificial intelligence into their current systems require the expertise of AI solution providers.

5. Data and AI experts

Artificial Intelligence algorithms play an integral role in business intelligence operations. Businesses wanting to apply artificial intelligence effectively to their operations should have skilled experts pioneering the adoption. Even after necessary algorithms have been developed, maintaining and adapting the models to changing business environments is an ongoing and involved task that also requires a skilled workforce.  In general, deploying artificial intelligence in business can be a difficult process that is both time and resource intensive. However, many firms find that investing in artificial intelligence 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 artificial intelligence 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 artificial intelligence 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 artificial intelligence 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.

2. 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.

3. 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.

4. 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 artificial intelligence 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 efficient models and make businesses more intelligent. Here, we will discuss three common challenges Akaike faces when reducing project implementation time and providing solutions.

1. Talent Shortage 

Artificial Intelligence 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 these projects. To alleviate the dependency on 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.

2. Data shortage

Data scarcity is a major bottleneck to Artificial Intelligence production. 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.

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

There is no one-size-fits-all in artificial intelligence. Different 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 artificial intelligence. Akaike follows this strategy. Rather than build models from scratch every single time, Akaike first investigates if one or more existing 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 artificial intelligence, 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.  

BYOB: Empowering On-Time Data-Driven Decision Implementation for Businesses

In contrast to the previously mentioned custom-based solutions, Akaike’s BYOB (Build Your Own Brain) is an AI analyst that operates as a plug-and-play product. It helps to empower organizations and enterprises, as BYOB provides swift analysis and insights across diverse data sources. As a versatile ally across finance, marketing, HR, operations, legal, and more, BYOB seamlessly connects and organizes data, accelerating decision-making processes.

BYOB goes beyond mere aggregation, automatically sorting and delivering real-time insights based on key metrics and patterns. Adopting BYOB provides a competitive edge, representing a significant leap in AI implementation. The advanced features of BYOB make it a valuable asset for businesses actively integrating artificial intelligence into their operations.

Additionally, it effortlessly integrates into existing workflows and tools, facilitating a quick and smooth implementation process. For instance, in projects requiring data-driven decisions, BYOB anticipates trends before they unfold. Companies gain valuable insights, allowing them to foresee changes, enhance performance, and make swift, accurate decisions. 

BYOB is not just a tool; it’s a strategic partner that enhances organizational agility and decision-making prowess through its advanced AI capabilities.

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 this technology moderately and have aggressive plans for the future. The use of artificial intelligence 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