How to be Successful in Implementation of Artificial Intelligence or Machine Learning?

Amongst the emerging technologies of recent years, AI coined with just two vowels of the English Alphabet, has been the hottest topic for the tech world. This evolving technology in its infancy has immense potential to streamline business processes as well as enhance efficiency thereby improving productivity. While few of the elite technology leaders like Amazon, Google and Apple ardently believe in the invincible capabilities of Artificial or rather Adaptive Intelligence, the terminology I have been so fascinated with, those enterprises which are not so affluent technically, are still facing significant challenges in adopting AI.

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So what are the reasons pulling down average organizations from embracing artificial intelligence into their business practices? Is it insufficient data, poor algorithm or lack of strategy? Through this post, I am making an attempt to dissect and showcase a few simple steps that have led to successful AI implementation.

Clear Implementation Strategy - It's important to understand that Artificial Intelligence is like any other technology. It depends on the business line of the organization and also it depends on you to decide on the use cases. But you need to be grounded to the reality that you need to use AI as any other tool, be it for Interactive Voice Response, Chatbot Service, Financial Accounting, Resume Scanning or for Data Analysis. The real effort is in describing the problem statement, in creating the business case, defining implementation strategy, thinking about the edge cases and understanding the risk of any business decision. You think of implementing AI as similar to building an Excel model. The best way to build a model is to sketch it out on paper first. It will compel you to be thoughtful about the decisions you make. Deciding to implement AI as a core part of your business is a much more complex decision, but treat it similarly. The best way to overcome the barriers is to make a strong business case for why Artificial Intelligence solves your problems, and in fact, addresses it better than any other available solution.

Transparent Algorithms - Coding in any form, procedural or object-oriented, irrespective of the language, reflects the prejudices of the programmer developing the code. Particularly in AI, we're already seeing the use of the destruction closed systems can create. Since people don't know how a system works, they assume that system is unbiased. But whether you're talking about a job application system that automatically eliminates all applicants without a college degree or an adoption assessment tool that excludes everyone with a biological relative who had one or more police cases, assumptions can be incredibly destructive. To use machine learning or artificial intelligence responsibly, efficiently and effectively, you need transparent algorithms. You need to know how the system arrives at conclusions, any assumptions if used, you must know whether those are applicable to your business case and build a process for checking inaccurate results or deviations.

Understand the Limitations of the AI System - As it is true with any other technology, Artificial Intelligence too has its shortcomings. The intense extravagance created around AI has made it seem like the be-all and end-all of business future trends. You must be hearing it will displace human resources in certain skilled jobs, expose financial gaps and fraudulent activities, drive the economy forward and much more. However, AI is still in its nascent stages, and there are limitations to the technology that organizations can't currently overcome. For example, some healthcare practitioners are experimenting with AI, but few of the results were found to be extremely bizarre, horrific and disturbing. Even authors are toggling with AI tools to churn out impressive content. Organizations rely on quality content for sales and marketing, but AI cannot grasp or manipulate the rules of grammar. Right now, organizations are limited to simple responses, but even those need to be reviewed and verified. As AI continues to advance, organizations should start incorporating AI into their accounting, sales, marketing, talent acquisition and support strategies to optimize them for the future.

Effective Data Management - Artificial Intelligence and its subsets like machine learning, deep learning, neural networks and even robotics cannot function properly with algorithm alone. Each of these exponential technologies need data in correct form with quality, availability and accessibility at a good speed. While we work deeper into maturing our AI models, we still need to put in a lot of effort at managing data in an effective way. We need to get our data in a better position both quality wise and structurally speaking in order to access and process it at high speed. This will help us to benefit from the progress in AI and build applications for it that are specific to certain lines of business and solve specific problems. Much data is stored in outdated systems which are not compatible with AI or are too antiquated beyond possibility for an upgrade, and the movement of this data to more sophisticated smart systems is the stepping stone towards successful AI implementation.

Verification of AI Systems - It is vital for enterprises not to have cent percent blind faith when introducing AI systems into their mainstream business. It is not possible to comprehend and control every aspect of any tool or application at a single shot, but the verification mechanism needs to be chalked out and followed in order to avoid going berserk. From a developer’s perspective, testing AI components of a system has its own unique challenges. We cannot always control performance over time, definitions of consistency and success may be the only relative. For the end user, businesses may trust AI to deliver a superior quality experience. However, if AI delivers poor automated suggestions or a frustrating chatbot experience, it may be tough or impossible to detect, diagnose, replicate and solve the issue. An enterprise must construct effective mechanisms to constantly evaluate the performance and quality of the results delivered by AI components before trusting that AI system to be deployed as part of its business practice.

Leadership Drive - Artificial Intelligence is the happening technology of the current digital era and is penetrating various ecosystems at a good pace. As AI continues to become an integral part of your core business, you need to acknowledge the power and potential it holds in store. If you are part of the leadership team, you have a bigger responsibility to seek support from your entire organization, else the adoption will most likely fail. You need to hear out your employees with patience, listen to what their fears, premonitions and discomforts are with regards to using AI. It might be something as simple as the lack of knowledge or skills on the field. While you need to educate yourself on the latest technological developments, plan for implementing education and training to help the workforce in large scale. Invest in your employees to enhance their AI skills and it will most likely fetch outstanding results.

Ascertain Affordability - Its true that medium to large enterprises are adopting AI to make the most of the smart technology to improve productivity and enhance profitability, small businesses are still pretty far away from implementing this technology into their daily operations. Why? It is still expensive, takes a long time to fully execute and is still not well analyzed and understood for complete utilization. We need a few years to pass before the technology becomes more affordable and we definitely need to shorten the onboarding process for new clientele, as it can sometimes take up to six months to gather sufficient data for a simple AI customer service chat program to get fully functional and yield appropriate results.

Building AI Pool of Resources - The primary obstacle of implementing AI is building a knowledgeable talent pool of AI resources. The most important functions for AI implementation within the organization are technically inclined product managers who can identify a problem sufficiently massive to solve with AI, a data engineering team that will create the right structure of database and data warehouse as well as integrate your business systems into it, also AI-savvy software developers and data science teams that can execute the AI model having the right goal and the right database.

There are a few common issues with implementing AI. First, the technology is not that much matured as the hype makes it out to be. AI systems are only as smart as their inputs. In case AI is not learning the correct information, it is useless and expensive. Before implementing AI into your business, you need to compare your existing solutions to AI and ensure it can perform all of the same functions. Second, there needs to be widespread training about how to properly use AI. If people are not trained on how to use the AI system, they will feed the system with incorrect or bad data, lowering its effectiveness. Consider whether the time and effort it will take your team to master AI, the cost your organization will incur, the teething problems you will face at various stages of implementation are worth the added benefits before you take the big leap into the world of Artificial Intelligence.

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