Worldwide AI Webinar panel discussion on Challenges and Solutions for Adopting AI & ML in your Enterprise

4 tech giant executives solve four major barriers to AI/ML adoption in the enterprise

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

NEW YORK, October 10, 2022 /PRNewswire/ — Artificial intelligence (AI) no longer just appears in science textbooks, but is now an evolving reality. Businesses are starting to realize how important this cutting-edge technology is as 56% of businesses said there would be deployment of AI in at least one function, according to a 2021 study McKinsey survey.

Global roundtable on the challenges and solutions for adopting AI and ML in your business

Yet adopting AI is not straightforward. What are the main obstacles preventing companies from exploiting the enormous potential of this new technology?

Four AI leaders from top tech companies sat down to dissect the 4 major challenges to AI/ML adoption and offered solutions during a panel discussion during the Worldwide AI Webinar 2022.

  1. Lack of AI knowledge
    Andreas WelschVice President of AI at SAP highlighted the lack of organizational AI knowledge as the first challenge. Often, even senior executives confuse AI projects with IT or data projects that have a clear start and end date. On the contrary, adopting AI is an ongoing journey that involves research, testing, and trial.

    “I see when you’re building AI models and solutions from scratch with teams, often they don’t know what they don’t know. […]. And then there’s also this spectrum between this excitement of what you read in the press and the fear of uncertainty within the organization.” – Andreas WelschVice President of AI at SAP

    Nestor CamiloDirector of Cloud Adoption at Oracle agreed with Andreas’ perspective, adding that the journey from planning to implementation is complex and demanding, requiring in-depth understanding from the deployment team.

    “Creating models is very easy, anyone with Python skills and a few hours of training can copy code and run it on their machine,[…]but later when this needs to be put into production a lot of things need to be resolved, getting data from production systems, moving it safely, retraining the model and of course adapting it, maintaining a secure platform and highly available is a lot of effort.Nestor CamiloDirector Cloud Adoption Public Sector at Oracle

    To tackle this problem, Aamar HussainDirector of Azure Data at Microsoft, suggested not treating AI adoption like any other software development project and understanding the current maturity level of the business. Assessing your organization’s skills, budget, AI awareness, and data availability is highly recommended to ensure successful adoption.

    “Non-tech focus and focus is really important. Don’t jump right in and think you can just turn it on and off.” – Aamar HussainDirector of Azure Data at Microsoft

  2. Lack of strategic approach to AI adoption
    This second challenge was specified by Aamar. He stressed the need for a data-centric approach and an appropriate framework, methodology and strategy. Issue identification, data governance, ethical considerations, constant AI model monitoring and management are equally critical.

    Ali ArsandjaniDirector of Cloud Partner Engineering at Google Cloudalso weighed in on this question:

    “I think it’s important to understand what the real use case is and then choose the appropriate technology. I would say any traditional mechanism should be broken down into what you would do with software engineering and with machine learning So there’s a segment of the problem that’s easily solved by traditional software engineering practices and best practices, and there’s a segment that could potentially benefit from unlocking the data. data would be through machine learning so if you can divide the use case into those two categories what is traditional what you do for a scalable architecture versus what you have data for data entry and exit that you can then use to unlock abilities in AML scenario types.

  3. fear of uncertainty
    Fear is the third common problem. Andreas Welsch mentioned people’s fear of losing their jobs to AI as Ali Arsandjani addressed the uncertainty of return on investment. He suggested :

    “If a leader is faced with uncertain technology, unproven technology, they are likely to put off until the value of that technology is demonstrated.”

    Nestor built on his point, saying “risk of failure” is not an easy issue to overcome and offering a solution:

    “We need to make it easier for businesses to use ML. If you don’t have the experts or the domain knowledge, democratizing AI access to the majority is a must. […] First, we need to integrate ML models into our business applications […] Then we make it easy to use your data in your production systems to train and forecast without the need for complex ETL” – Nestor CamiloDirector Cloud Adoption Public Sector at Oracle

    In addition, Andreas Welsch stressed the importance of gaining the trust of business users. As Andreas said, explaining and educating business stakeholders about the tremendous benefits of the AI/ML system is paramount so that those involved can begin to capitalize on its potential.

    Ali Arsandjani had a different perspective. He believed that education should come first; with enough knowledge comes the ability to weigh in and make assessments based on knowledge and experience, which can be used to build trust.

    “To have a conversation about tech adoption, I would say we need to do more in terms of not just trying to persuade, but investing in education and upskilling; then we can have more aligned conversations with someone. If they’re in a completely different mindset and a completely different knowledge base, there’s almost no way to convince them.”

    Nestor Camilo supported Ali’s opinion, saying it’s crucial to provide formal AI training because “you can do a lot better if they know what you’re talking about.”

    Aamar Hussain discussed how education can have a positive impact on culture. He considered culture to play an essential 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 concerns cultural barriers, or Ali Arsandjani put, “resistance to change”. Aamar Hussain further noted that some executives tend to stick with the way things have been done for years. It often takes a bit of persuasion before you see that adopting new processes will be worth the overall gains they will bring.

    He also praised the fact that the executives are great role models for their followers. You can’t preach sustainability and be one of the main sources of CO2 emissions. The advocate for change and the adoption of new technologies must be the change itself.

    Ali Arsandjani offers a possible solution to this problem:

    “So start small, using projects that demonstrate KPIs that can be improved. So something that someone cares about. If I come to you with a proposal and you don’t really care about this proposal or that it is not really aligned with your business objectives, it will not be important. But if it is, you will listen. And if there is a project that will identify a breakthrough in this area, you will tend to listen to it more.And then the next phase of maturity, there would be education, adoption and documentation of practices, automation of practices, collection of data on tasks that have been performed , then continuous improvement.”

Overall, all four speakers agreed that the best ways to address some key AI/ML adoption challenges in enterprises are to understand business needs, identify organizational concerns, and see how new technologies can help, engage everyone in your journey, and reduce the risk of misinformation and misinformation. Most importantly, companies can achieve transformational development by anticipating barriers to adoption and adopting a strategic implementation of AI using the maturity model described by Ali.

“With a business-focused machine learning MVP, you can convince reluctant people that AI is mature for many business use cases and not try to do a filming project first. moon, but small steps that generate a lot of value and confidence” – Nestor CamiloDirector Cloud Adoption Public Sector at Oracle

Webinar details

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

Discover Wow AI website, Youtubeand LinkedIn channels to rewatch the 2022 Global AI Webinar and learn more from the industry’s top AI experts.

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