Top 3 challenges to AI adoption and how the barriers are overcome
Over the past few years, AI has made its way to every boardroom discussion. Be it giants like Google, Netflix, or Amazon or small and medium businesses; everyone has benefitted from AI. While many companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production, there are still some challenges in adopting AI.
Accenture’s report reveals that 75% of executives believe they risk going out of business in 5 years if they don’t scale AI. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are using AI at scale. While they are making efforts to deploy and scale AI, awareness about the challenges of this journey is also necessary.
Challenge #1: Data quality and quantity
Technologies such as Artificial Intelligence (AI) and Machine Learning (ML) have the potential to help businesses make better use of the massive volumes of data. Still, these techniques depend on the computing power, quality, and quantity of the data provided. Getting consistent and accurate quality and quantity of the data is a challenge, since, there are no commonly accepted and widely adopted standards of data definitions and governance in enterprises.
Many enterprises are pursuing a range of AI initiatives and modernizing data infrastructure. But current data practices are an issue, as several companies haven’t attained a high level of sophistication with crucial data-related aspects. In fact, many organizations have stopped mid-way when pursuing AI initiatives because the data is not good enough; hence predictions and insights would also be unreliable. Therefore, many companies tend to postpone their AI journey in favor of a data journey before starting the AI leg.
In order to overcome this challenge, a robust data management strategy, data quality, and governance framework should be in place to ensure that all the data generated in the organization is captured, processed, and stored effectively. Also, the right blend of cloud and traditional data warehouse setup will help organizations achieve optimal performance. There should also be a focus on a forward-looking approach, i.e., on future integrating and scaling of data. It ensures that integrating data from various new sources is not a challenge later.
Challenge #2: Hiring the talents with AI skills
One of the major challenges while AI adoption is finding or hiring the right team with AI skills to work with. The right talent is the key to success for any initiative, and the same is true with AI as well. As per a Juniper research survey, 41% of respondents are worried about the training of current employees to operate the AI systems. Also, 32% concentrate on recruiting the already trained talents to cope with it. AI is a far more complex skill to build, and therefore there is certainly a demand and supply gap in the marketplace.
AI comprises a range of technologies that covers advanced analytics with the ability to predict outcomes, Conversational AI, Natural Language Processing (NLP), Robotic Process Automation (RPA), Deep Learning, etc. The sheer vastness of the technology makes it difficult to find the right talent for both the creation and implementation of an end-to-end AI journey across an organization. Also, AI takes time to evolve and requires constant creative and material investment till it starts maturing and providing a level of acceptable accuracy. Therefore, we need someone with creative brains who can also innovate the use-cases for the technology.
To address this challenge, an organization needs to build a culture where business teams can think about the use of AI in day-to-day operations. Once this culture is built, the organizations have more champions beyond the innovation group to motivate the rest of the people in the organization to walk the same path.
Companies will have to invest in the right talent, train internal resources with the right aptitude and the know-how in related technologies, add people to the creative team, and think unconventionally while solving business problems. Also, low-code/no-code AI technologies empower technical and non-technical programmers to become citizen data scientists and build AI applications with little to no coding knowledge.
Challenge #3: Eliminating Bias and AI Governance
AI governance means monitoring and evaluating ROI, risk, bias, and effectiveness algorithms. However, while hugely interested in AI adoption, companies are reluctant to build their AI governance strategy. Also, bias in any AI model impairs the possibility of making the right decisions. After seeing positive gains using biased models, businesses might get a false assurance, but the models with biases are not solving the problem they are supposed to solve.
This usually happens due to the use of datasets that tend to be discriminative against arbitrary groups. So, the datasets used for training, especially evaluating the models, should be balanced so that they don’t reflect real-world biases. Unknown biases still sneak into the systems. To overcome this challenge, strong Quality Assurance (QA) processes are important to have in place. These QA processes should be extended post-integration, too, since data drift and feedback loops after deployment can still bring biases to the system. Also, Explainable AI and Ethical AI capabilities ensure transparency and interpretability in the models with the fair usage of AI. With these capabilities, it helps eliminate biases in the model.
Getting consistent and accurate data, filling the AI skills gaps, and AI governance might be challenging. So, get ready for a long game, try pilots of your projects before the final run, and set the metrics to measure the progress of AI adoption over time. Simultaneously, it would help businesses monitor and evaluate algorithms that impact the business daily. This is an opportune time to climb this mountain towards AI-powered decisions, step-by-step, with balanced, courageous actions.
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Payal is a Product Marketing Specialist at Subex, who covers Augmented Analytics. In her current role, she focuses on CIO challenges with data management, and potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.