On the way to overcoming AI/ML roadblocks:
From augmenting human capabilities to automating repetitive tasks and streamlining customer services to improving business efficiencies, AI has made its way into business processes, be they simple or complex. In a nutshell, AI has opportunities in every enterprise and through every business process. AI is something that generally every business leader is aware of. Then why is there hesitation when it comes to implementing AI? To demystify the roadblocks discouraging our leaders from adopting pervasive intelligent tech can be enumerated as
Hurdle 1: Culture.
As the saying goes, “The measure of intelligence is the ability to change.” It’s a part of human nature to resist change as it seems to be well adept at doing things a certain way, and they are comfortable with it as it is harvesting them with efficient and effective results. They altogether ignore the benefits one can reap while adopting a change.
When business leaders are not ready to value data-led decisions, how can an organization rely on AI-based decision-making? It usually happens when AI is treated as unknown.
Amendments:
It’s all about readying your organization’s culture towards
- A better understanding of AI advantages and what it can offer.
- Comprehension of how and what data is essential for the success of AI will help to develop a culture of cross-function in the organization.
Education in AI is necessary to overcome the fear of the unknown as the knowledge will make them receptive and engage them with real potential. Leaders cannot assemble AI benefits in small and medium-sized businesses. They are unaware of how predictive capabilities can help them prepare for seasonal variations or events and trends that trigger surges or troughs in supply and demand.
Hurdle 2: Skills
A grave concern for organizations to get on board is the shortage of skilled personnel, choking their capability to capitalize on AI opportunities.
Amendments:
- Hiring a data scientist can be costly when there is a dearth of talent, especially for small businesses. However, upskilling the in-house expertise can bridge the gap between different departments and AI. Allowing organizations to identify problems that can be solved with AI.
- Empowering domain experts at organizations can help them gauge and map a problem to AI solution. Upskilling doesn’t require knowing the technicalities; non-technical individuals need to learn AI to understand the processes involved to tailor their understanding for the better performance of AI models.
When leaders know the importance of data, and how to leverage it in upbringing, an organization helps to use skills better.
Hurdle 3:Infrastructure
Success never comes overnight. The same is the case with AI/ML. It won’t change the business in a snap of a finger. This tech gets intelligent after learning and processing a large volume of data. Improved compute, and storage systems are required; therefore, an infrastructure is necessary.
Every business is defined to have its budget and ROI. AI/ML implementation will take a reasonable sum from its resources, but is it a worthy investment?
Amendments:
- Get over with age-old technology; though they are functional, they create a financial burden and hinder innovation. To leverage a data-driven business model, an organization must switch to modernized systems to achieve significant performance, agility, and innovation leaps.
Hurdle 4: Trust
Gaining trust in AI/ML is more about building a relationship; the stronger a deeper bond is the trust. The current state-of-the-art in AI relies on algorithms based on the black box concept. Hence, making it difficult to interpret the outcomes. Interpretability plays a vital role in trust-building. However, other parameters also have a part to play in strengthening trust, including performance, privacy, and security, unbiased data.
Amendments:
- Defined objectives will help understand why you need AI/ML in the first place and, therefore, the first step in fostering trust.
- There are indirect methods that help in understanding the interpretability of algorithms.
- Start using AI/ML at a small level to start building trust. The more you use it, the more you will trust it.
Hurdle 5: Strategic Approach
A realistic path for implementing AI requires a sound understanding of goals, knowing the organization’s priorities ( making business functions more intelligent or developing smarter products and services, etc.), and the challenges that must be addressed.
Amendments:
- The Discovery of use cases and aligning them with problems at hand gives a broader view of potential solutions of AI as it might help you understand your strategic goals achievable.
- Smaller steps towards AI by taking smaller projects aids in laying the groundwork that will help achieve bigger goals tomorrow. Start small, i.e., begin from any of your business functions, and after success, scale up and utilize it in other business processes.
A proper AI strategy paves the way to achieve business value and continued growth and investment.
Hurdle 6: Cost
The cost of implementing AI depends on the complexity of the problem and the data resources required. The development of an AI solution requires specialized talent, such as data engineers, data scientists, and data analysts, and data, leading to increased costs. This cost includes the resources needed and the time taken for development. Organizations typically build internal teams to meet their AI needs, but this can be costly as hiring a team of AI and data experts is expensive.
Amendments:
- One solution to this is hiring a third party with specialist AI capabilities that can speed up development processes.
- However, the most conducive solution is we make our domain experts the drivers of AI by upskilling them with AI knowledge, where they can leverage off-the-shelf AI to tailor their deep knowledge towards AI success.
A Road Ahead:
The path to AI adoption opens up by overcoming obstacles. Businesses should assess the potential benefits of this data-driven technology and explore its value. The in-house experts can drive innovation by leveraging their knowledge, and their skills can be upgraded to create AI models with accessible tools intuitively. By simplifying technicalities, AI can be adopted at cost-effective prices.
The soon-to-be-launched EMLY. Labs is one such product that aims to overcome key barriers. Its collaboration feature promotes transparency and a seamless experience, enhancing the flexibility of AI projects and facilitating the integration of new use cases for faster AI implementation.