{"id":1950,"date":"2019-09-13T03:56:01","date_gmt":"2019-09-13T03:56:01","guid":{"rendered":"http:\/\/kusuaks7\/?p=1555"},"modified":"2024-04-08T12:48:25","modified_gmt":"2024-04-08T12:48:25","slug":"four-challenges-to-ai-adoption-and-their-solutions","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/four-challenges-to-ai-adoption-and-their-solutions\/","title":{"rendered":"Four challenges to AI adoption and their solutions"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhile AI gains traction as a core business enabler for organizations across different industries, a few barriers to AI adoption have slowed down the mainstream adoption and full-fledged applications of this technology. With time, investment, and continued experimentation, these obstacles will eventually be overcome, giving rise to a whole new generation of advanced AI applications.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhile the concept and its applications have been around for a while now, the field of AI still hasn\u2019t stopped surprising us with new innovations and milestones. In recent years we\u2019ve been treated to numerous stunning applications of the technology, from\u00a0AI machines that can engage in logical debates with humans<\/a>\u00a0to those that can detect cancer and other diseases better than human physicians. Businesses worldwide have lined up even more ambitious and revolutionary applications of AI to improve their products as well as their processes. However, adopting this technology, regardless of how beneficial they might have been, have also raised a few challenges. These barriers to AI adoption, mostly stemming from the rapid and exponential growth of AI capabilities combined with the lack of preparedness of businesses as well as governments, must be addressed before we transition to an AI-driven future.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Barriers to AI adoption and their solutions<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

1.\u00a0\u00a0\u00a0Gaining organization-wide buy-in<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe foremost and the earliest challenge to AI adoption for any business, especially if it\u2019s a traditional, established organization, is to get buy-in from stakeholders. These stakeholders include everyone involved in operating the business — from the directors and investors all the way down to the last employee. It is vital to convince each of these stakeholders of the value of using AI applications not only to the organization but also to them individually.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhile the buzz surrounding the technology in the mainstream and the numerous\u00a0cases of successful AI implementation<\/a>\u00a0may influence most investors and leaders to commit to AI, the same may not necessarily work for everyone at the organization. Even then many among the leadership and management may see AI as an extravagance. They can adopt the \u201cIf it ain\u2019t broke, don\u2019t fix it\u201d attitude towards driving organizational change. Most employees may feel threatened by the idea of incorporating AI into different business processes. They may feel control slipping out of their hands and may fear becoming redundant due to AI.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tCIOs and technology leaders must recognize these resistances as natural human responses and therefore, commit to educating their employees and the C-suite leaders about the need for AI. More importantly, they must focus on making their employees aware of the fact that AI will only make life easier for them by assisting them instead of replacing them altogether. They must also implement appropriate change management strategies, as people\u2019s inherent resistance to change may also make them averse to any transformative efforts, especially something as disruptive as AI.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

2.\u00a0\u00a0\u00a0Developing a clear AI strategy<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tAny major investment in technology by enterprises should follow a thorough strategy phase where the expectations, objectives, and plans are clearly laid out. However, aiming to exploit the early adopter advantage, businesses may want to rush into implementation by choosing the seemingly most obvious applications areas. And although such implementations may deliver desirable results, they may not align well with the organization\u2019s broader, long-term directives. Thus, considering how new AI initiatives align with organizational goals is key to ensuring that they are in perfect alignment with each other, enabling a smoother transition towards AI-driven processes. More importantly, having a clear AI strategy can enable businesses to avoid the \u201canalysis paralysis\u201d trap, where, due to the numerous benefits and newly emerging applications of AI, businesses struggle to prioritize what they want to achieve with AI. They then end up investing in initiatives that may not necessarily align with their long-term objectives. While this may not necessarily be disadvantageous, but can lead to hefty opportunity costs which may, at least temporarily, derail them from their desired direction. However, smaller organizations that are constrained for resources may feel the impact of such initiatives in the worst possible way, leaving them with no fallback options and resources.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tTo\u00a0create a clear AI implementation plan<\/a>, businesses must draw up all the possible use cases of AI that can benefit their organization. Then they can assess the feasibility of individual cases and their expected impact on the business\u2019s mission and long-term strategy. They can prioritize the different use cases by starting with simpler applications and pilot projects and incrementally transitioning to larger, overarching ones. Doing so will ensure that businesses can accelerate AI adoption and minimize the time to value.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

3.\u00a0\u00a0\u00a0Finding the right talent<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tAmong the biggest barriers to AI adoption for most organizations is finding the right AI talent.\u00a0Being an AI researcher requires a specific set of skills, knowledge, and attributes<\/a>\u00a0that is currently in rare supply across the world. Most of the existing global AI talent pool is currently engaged with the top international corporations, as they are the only ones who can financially afford them. Thus, most of the remaining organizations are not able to attract AI talent as easily.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\nHowever, this problem will be alleviated with time, as more and more people are pursuing an education and a career in AI research, driven by the abundance of opportunity and the high valuation in the job market. This will lead to a greater influx of new AI talent, filling the current talent void. However, businesses should look for ways to identify potential AI talent within their existing talent pool. They can identify those with the aptitude and the willingness to pursue AI research and development and build their own internal AI teams. These teams can be augmented by hiring AI expertise from established AI research organizations. These businesses should also look to partner with AI development firms and educational institutions to not just gain implementation ideas in the short term but also to build a deep AI knowledge base for the long run. Partnering with educational institutions can also help businesses acquire AI talent easily.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

4.\u00a0\u00a0\u00a0Overhauling existing systems<\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tMany businesses, especially the established enterprises that have built their business processes, technological systems, and the overall enterprise architecture over multiple decades, will find that AI may not be as easy to incorporate with these systems. Advanced AI applications require newer hardware and supporting platforms, making legacy systems redundant. Using AI may mean completely overhauling their existing systems, procedures, and even strategies and policies. This may come with a lot of associated costs both in terms of money and time, which can potentially cause them to lose their footing in the market and fall behind their competitors. Thus, AI, although necessary for their sustainability, may seem like too big a step for these enterprises.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tSuch organizations must adopt a multi-phased approach toward adopting AI. They must start small on projects that are highly likely to succeed. They must incrementally scale up their AI implementation by capitalizing on the small wins and later perform exploratory research to eventually achieve disruptive transformation. In addition to these barriers to AI adoption, there are numerous other challenges that must be addressed before embarking on the ambitious AI projects envisioned for the future. These barriers include those involving the quality of data used with AI, safety concerns, regulatory uncertainty, ethical implications, and a host of other related issues. Overcoming these challenges will require the collective effort of the global tech and AI community, regulators, and the enterprises adopting the technology, which can be motivated by the potentially groundbreaking\u00a0applications and benefits of AI<\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

While AI gains traction as a core business enabler for organizations across different industries, a few barriers to AI adoption have slowed down the mainstream adoption and full-fledged applications of this technology. With time, investment, and continued experimentation, these obstacles will eventually be overcome, giving rise to a whole new generation of advanced AI applications.While<\/p>\n","protected":false},"author":44,"featured_media":3936,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1914],"class_list":["post-1950","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1914,"user_id":44,"is_guest":0,"slug":"naveen-joshi","display_name":"Naveen Joshi","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/07\/l9qFe5Pt_400x400-150x150.jpg","user_url":"https:\/\/www.allerin.com\/","last_name":"Joshi","first_name":"Naveen","job_title":"","description":"Naveen Joshi is the Founder and CEO of Allerin Tech Pvt Ltd. A seasoned professional, he has more than 20 years extensive experience in customizing open source products for cost optimizations of large-scale IT deployment. Currently working on IoT solutions with Big Data Analytics. He specializes in Solution Design and consultancy, Data Science, Machine Learning, Deep Learning Enterprise Application Planning, Cost Optimization."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1950"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1950\/revisions"}],"predecessor-version":[{"id":36597,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1950\/revisions\/36597"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3936"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1950"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}