.svg)
Who would have thought that we will play the role of god someday and eventually bring life to the metals? And guess what, we are doing it now! AI is still evolving and there is no proper linear guide on how an AI can be deployed. It’s newness and the absence of relevant industry standards makes it difficult for a pre-defined implementation roadmap.
If the reports of a survey conducted by a McKinsey consulting firm are to be believed, hardly 20% of all the AI-enabled companies could deploy it in multiple parts of the company. So what are the hurdles of AI that enterprises need to overcome? Let's find out!
AI is a tremendously powerful instrument, and we are only scratching the surface of its potential worth. From operations to customer engagement, AI can aid enterprises in many ways. The majority of businesses are still learning how artificial intelligence can strengthen their growth in the coming years.
Since huge volumes of data are still stored in silos, they offer an untapped potential and it is difficult to access. Once they are tapped, the preparation of data and appropriate application models takes time as well.
The majority of businesses are unsure where to begin. There are numerous advantages of utilizing AI. Enterprises should begin by determining the worth of each AI initiative to their organization.
For instance, picking AI projects that look promising and deliver the most value to the business operations and customer engagement. Experimentation also plays a big role. Is the first project going to be a huge hit? Most likely not! However, that will be a learning experience, and consistent efforts in AI come with an exponential learning curve.
The sooner an organization initiates AI, the sooner it shall realize the benefits. Projects that hugely impact the primary business objectives should be prioritized first.
Here are the top hurdles that enterprises need to overcome in order to successfully adopt & operationalize AI.
Top Hurdles That Enterprises Need To Overcome To Operationalize AI
- Making AI Models Production-Friendly
The fact that AI's dynamics do not work in the same manner that traditional systems do is one of the biggest barriers to adoption. Most modern AI has machine learning at its core. Also, it is built on models that are distinctive from simple process-based activities with legacy codes used within outdated systems.
The workflow and data management of these legacy systems are mostly out of sync when it comes to machine learning(ML) requirements. The team developing the AI and the IT people in charge of deployment don't always comprehend that integrating machine learning models with legacy systems is not a cakewalk. It can be overcome with new workflows and applications for the better optimization of AI's results.
- Measuring & Proving Business Value
Going by a Deloitte survey, it's often difficult for a business to prove the worth of AI. 30% of the respondents in the survey had said that they consider this as one of the top three hurdles in adopting AI. Most companies try to implement AI and judge its efficacy in terms of the problems it can solve. Instead, AI should be integrated at the base level.
The common practice is that organizations hire data scientists and expect them to formulate an AI strategy by letting them loose on data stockpiles. It should change. Organizations need to measure the business value based on the intrinsic nature of the project.
- Scalability
Scalability is very important for any AI to completely spread its wings after successful implementation. If an AI model has been successfully deployed, that would imply basic established scalability.
But, can it expand and manage a greater throughput? If yes,how much? Has the AI been designed to cater to future requirements?
Mostly, deploying an AI is deemed as a big success and it's scaling isn't planned. The model's performance requirements in terms of scalability must be set concurrently with its development. Or else, the deployment will not be robust, even if the AI model is deployed at all!
- Cybersecurity
The Deloitte survey points at cybersecurity as the single most significant risk of deploying Artificial Intelligence. There have been several data breaches involving information gathered by companies to back their AI ambitions.
In a majority of these cases, however, the breaches did not occur due to the flaws in the AI application.
In reality, AI is being looked upon as a viable option to shield businesses against cyber-attacks.
However, it should be kept in mind that a new technology or platform always brings with it new security challenges. Many of them are not immediately evident and only surfaces after some time. Overall, cyber-threats remain a concern that can be mitigated with proper measures.
- Legal & Regulatory Risks
Legal & regulatory issues are a major concern for businesses considering AI, particularly those in the regulated sectors. A major problem is the absence of transparency standards in AI algorithms since the field is still in an evolutionary mode.
The AI models remain a black box where the algorithms have advanced but the transparency & explainability of the model poses a question. This makes explaining a company's decision-making procedure to consumers, regulators, board members, and other stakeholders a bit difficult.
Importance Of AI In 2021 & Beyond In The Technology Roadmap
A majority of the business executives are searching for ways to realize their data potential in meaningful ways. Getting a grasp of the ever-changing customer mindset is essential for success and everyone knows it. The AI models must engage the precise data, correctly.
Businesses should come together to invest in Mission-driven AI laboratories. They can target areas of high potential and develop solutions. Here is a glimpse of what these labs can facilitate:
- Novel collaborations between domain experts and AI researchers, to mitigate challenges & cater to requirements for AI technologies,
- Exposing AI researchers to real-time problems, enabling them to acquire expertise on business problems,
- Immersive scenarios for multidisciplinary teams to study challenging problems,
- Acquisition of unprecedented research-grade data,
- Trial of AI prototypes for developmental evaluation,
- Examining the security & dependability of AI systems prior to mass adoption, and
- Training of the next-generation AI researchers & engineers.
Regulatory & privacy requirements, on the other hand, necessitate greater effort to mitigate. Beyond the obvious intention of customer engagement, businesses are turning to data & artificial intelligence (AI) to gain a deeper understanding of how their business operates and how to manage it more efficiently.
Data coupled with automation and intelligence is a potent combination. AI is a critical component in the companies' efforts to shift processes, boost customer engagement, and eventually reinvent their businesses.
Closing Thoughts!
To end with, the business leaders should pick an important business goal that's data-intensive and commence with AI there. Finding a miniature project is always better to start with as it will allow the AI development team the leeway for experimentation and gain a better understanding.
While venturing out with AI, it is better not to expect a positive outcome. It's a learning curve that will help you figure out what to do next. There are numerous ways for AI to transform a business. The organizations just need to trust it and take a leap of faith!
