4 Giant Tech CEOs Overcome Four Major Hurdles to Adopt AI/Machine Learning in the Enterprise

AI executives from Google Cloud, Microsoft, Oracle, and SAP sat down to expose recurring challenges when companies try to integrate AI/ML

New YorkAnd the October 10 2022 /PRNewswire/ – Artificial intelligence (AI) is no longer just appearing in science textbooks but is now an evolving reality. Companies are beginning to realize how important this cutting edge technology is with 56% of companies reporting that there will be AI deployment in at least one job, according to 2021. McKinsey Survey.

A webinar around the world for AI on challenges and solutions for adopting AI and machine learning in your organization
A webinar around the world for AI on challenges and solutions for adopting AI and machine learning in your organization

However, adopting AI is not easy. What are some of the main obstacles preventing companies from tapping into the huge potential of this new technology?

Four AI executives from large tech companies sat down to dissect the four main challenges facing AI/Machine Learning adoption and presented solutions in a panel discussion during the 2022 Global Artificial Intelligence Symposium.

  1. The lack of literacy in artificial intelligence
    Andreas WelchVP of AI at succulents He noted that the lack of organizational literacy for AI was the number one challenge. Oftentimes, even senior executives mistake AI projects for an IT or data project that has a clear start and end date. Instead, AI adoption is an ongoing journey that includes research, testing, and experimentation.

    “I see that when you build AI models and solutions from scratch with teams, a lot of the time, they don’t know what they don’t know. […]. Then there’s also this spectrum between the excitement of what you read in the press and the fear of uncertainty within the organization. “- Andreas WelchSAP Vice President of AI

    Nestor CamiloCloud Certification Manager at inspiration He agreed with Andreas’ view, adding that the journey from planning to execution is complex and demanding, and thus requires a thorough understanding from the publishing team.

    Creating forms is very easy, anyone with some Python skills and a few hours of training can copy some code and run it in their machine,[…]but later when this is necessary for production there is a lot of things to solve, get data from production systems, transfer it securely, retrain the model and scaling of course, and maintain a secure and high platform, and that’s a lot of effort.Nestor CamiloDirector of Public Sector Cloud Certification, Oracle

    To address this problem, Ammar HusseinAzure Data Manager at Microsoft, suggested not to treat the adoption of AI as any software development project and to understand the current maturity level of the business. It is highly recommended that you assess your organization’s skills, budget, AI awareness, and data availability to ensure successful adoption.

    “Non-tech focus and focus are really important. Don’t jump into it right away and think you can turn it on and off.” – Ammar HusseinDirector of Azure Data, Microsoft

  2. Lack of a strategic approach to the adoption of artificial intelligence
    This second challenge was identified by Amar. He stressed the need for a data-centric approach and an appropriate framework, methodology and strategy. Problem identification, data governance, ethical considerations, continuous AI model monitoring, and management are equally vital.

    Ali ArsanjaniDirector of Cloud Partners Engineering at Google CloudAs they saw in this matter:

    “I think it is important to understand the actual use case and then choose the right technology. I would say that any traditional mechanism has to be broken down into what it will do with software engineering and machine learning. So there is part of the problem that can be easily addressed by traditional software engineering practices and best practices, and there is A chip that could benefit from unlocking the data. And it would be unlocking and activating the data through machine learning. So if you can break the use case down into these two categories, what is traditional, what you do for scalable architecture versus what you have data for input and output data that you can then use to launch Unleash the potential within types of AML scenarios..”

  3. Fear of the unknown
    Fear is the third common problem. Andreas Welch Mention people’s fear of losing their jobs to AI in the meantime Ali Arsanjani Addressing the uncertainty of return on investment. He suggested:

    “If a CEO is met with some unproven technology, unproven technology, they are likely to hold back until the value of that technology is proven.”

    Nestor builds on his point, asserting that the “risk of failure” is not easy to overcome and offers a solution:

    “We have to make it easy for companies to use ML. If you don’t have the experts or knowledge of the field, democratizing majority access is a must. […] First, ML models must be included in our business applications […] Then we make it easy to use your data in your production systems for training and forecasting without the need for a complex ETL”- Nestor CamiloDirector of Public Sector Cloud Certification, Oracle

    in addition to, Andreas Welch Highlight the importance of building the trust of business users. Explaining and informing business stakeholders of the enormous benefits of an AI/machine learning system so that interested parties can begin to benefit from its potential is crucial, Andreas declared.

    Ali Arsanjani A different perspective. He believed that education should come first; With sufficient knowledge comes the ability to evaluate and conduct both knowledge-based and experience-based assessments, which can be used Building confidence.

    “To have a conversation about embracing technology, I would say we need to do more not just in terms of trying to persuade, but invest in education and upskilling; then we can have more in-line conversations with someone. If they are in a completely different frame of mind and knowledge base. So different, there’s almost no way we can convince them.”

    Nestor Camilo He endorsed Ali’s opinion, saying that providing formal AI training is critical because “you can do a much better job if they know what you’re talking about.”

    Ammar Hussein Discuss how education can affect culture in a positive way. He considered that culture had a crucial role in the conversation. You can either have a dialogue where leaders force people to follow or you can have a culture where the leader brings people on board with education and reinforcement.

  4. cultural barriers
    The last major challenge is cultural barriers, or as Ali Arsanjani Put it, “Resist Change.” Ammar Hussein He further noted that some executives tend to stick with the way things have been done for years. It often takes some persuasion before they see that building new operations is worth the overall gains you will make.

    He also spoke extensively about CEOs being prime examples of their followers. You can’t preach about sustainability and being a major source of carbon dioxide emissions. The advocate for change and the adoption of new technology must be the change themselves.

    Ali Arsanjani Suggest a possible solution to this problem:

    “Start small, with projects that demonstrate KPIs that can be improved. So, something someone cares about. If I come to you with some suggestions and you don’t really care about that suggestion or it doesn’t really align with your business goals, it won’t matter. But if it’s Also, you will listen. And if there is a project that will identify some progress in this area, you will tend to listen to it more. And then the maturity stage will be education, adoption and documentation of practices, automation of practices, data collection on tasks that have been accomplished and then continuous improvement.”

Overall, the four speakers agreed that the best ways to tackle some of the major AI/ML adoption challenges in organizations is to understand business needs, identify organizational concerns and learn how new technologies can help, engage everyone in your journey, and reduce the risk of misinformation and information erroneous. Most importantly, companies can achieve transformational development by anticipating adoption barriers and adopting a strategic application of AI using the maturity model described by Ali.

“By using a business-based machine learning MVP, you can convince hesitant people that AI is mature in many commercial use cases and don’t try to do a moon-shooting project first, but small steps that generate a lot of value and trust” – Nestor CamiloDirector of Public Sector Cloud Certification, Oracle

Webinar details

Worldwide AI Webinar is a global AI education conference hosted by Wow AI. The 2022 edition of this event hosted more than 20 top AI experts and thought leaders from international tech giants and global corporations, as well as government agencies.

Check out Wow AI’s websiteAnd the YoutubeAnd the LinkedIn Channels to rewatch the World AI Webinar 2022 and learn more AI experts in the industry.

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