عرض العناصر حسب علامة : إنترنت الأشياء

معلومات إضافية

• المحتوى بالإنجليزية Three myths about AI
  In brief
  Myth 1: AI should be implemented away from the business units. In reality, the ultimate value of AI is realized when it is at the heart of the business.
  Myth 2: AI and IoT should be considered separately. In reality, companies that are capturing benefits from AI have coupled the capability with IOT to extract greater value.
  Myth 3: When it comes to data, quantity over quality is the goal. In reality, well managed and reliable data drives more impactful insights.
  As companies emerge from the pandemic and look to reframe their future, artificial intelligence (AI) will be a cornerstone of their technology ecosystem. This is a moment of profound significance, and there is an opportunity not only to rethink how to create value, but the very definition of value — for example, agility versus predictability; innovation versus strategic planning; operating model versus systems thinking.
  
  It’s impossible to overstate the impact of AI and data in this reframing, yet there are some prevailing myths that could impede the adoption of AI. In this article, we explore findings from EY Tech Horizon survey, leadership perspectives on technology and transformation, across 570 global businesses, to identify and address some of these misconceptions.
  
  Myth 1: It’s better to delay investment and keep AI away from core operations and data
  Our survey found that 55% of organizations are reluctant to invest in AI in the next two years. Many companies believe they should approach AI with caution, and they pilot AI in a separate business unit away from the core operations and data. Of course, AI does carry some risks to core operations and data – like any new technology, it needs to be secured against potential privacy breaches and cyber threats, and implementations must not compromise resiliency in any way.
  
  But this cautious approach, while understandable, may prevent organizations from gaining any value from AI. At the other end of the spectrum, our survey also shows that transformative leaders are heavily investing in AI: 45% of corporates say AI technologies will account for the largest share of investment in the next two years. And this group is more likely to extract value from the technology – because of their data strategy.
  
  If companies want to solve their complex business problems using AI, it’s imperative that they put data at the heart of their businesses. This will not be easy though. Our data shows that only a few companies have a truly integrated approach to using data across the business. In fact, just 4% of corporate companies say their approach to using data is highly sophisticated because it’s at the very core of their business model. Worryingly, a significant proportion of corporates (15%) do not use data in any way at all.
  
  This is the main challenge that organizations need to overcome. Beatriz Sanz Saiz, EY Global Consulting Data and Analytics Leader, says companies need to go the other way and embed AI and data deep into their core business. “The future is about a platform play, using AI to give meaning to data,” she says. “There’s no need to move data around. AI should be built into core processing.”
  
  So what is causing 55% of organizations to delay their uptake of AI? They report that the biggest barriers to embracing such innovations are lack of transformation culture (32%), legacy technology (32%), lack of collaboration across departments (30%) and lack of skills internally to compete in the digital economy (27%). Skills are particularly a problem in the health sector, as 78% of companies focused on AI in the health sector agree that skills shortage is a major issue.
  
  As companies address and adapt their tech frameworks and modernize their legacy technology infrastructure, they should consider the long-term benefits of enabling and accelerating AI strategy, whether that’s to deliver radical customer centricity, agility, insights and prediction, efficiency or growth. And where skills are concerned, laggards should take their lead from organizations who are extracting value and investing heavily in AI – they are also introducing new incentives to encourage workforce to upskill (58%) as well as enforcing mandatory training programs (57%).
  
  The future is about a platform play, using AI to give meaning to data. There’s no need to move data around. AI should be built into core processing.
  Beatriz Sanz Sáiz
  EY Global Consulting Data and Analytics Leader
  How EY can help
  Artificial intelligence consulting services
  Our Consulting approach to the adoption of AI and intelligent automation is human-centered, pragmatic, outcomes-focused and ethical.
  
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  Recommendations
  Develop an enterprise data to leverage AI in core business operations
  Build the AI strategy into the modernization of legacy applications and infrastructure to start realizing long-term benefits sooner
  Offer incentives to future proof the workforce and deploy robust training programs in AI competencies to build skills in-house
  Myth 2: AI and Internet of Things (IoT) do not overlap
  AI and IoT technologies are evolving rapidly, and our survey found that the two are correlated: 54% of AI-focused companies also invested in IoT in the last two years, compared to 50% of overall corporates. Although only a small uplift, it still shows that those with an AI focus are also clued-up on IoT.
  
  The investments AI-focused companies are making in IoT are paying off – 48% of AI-focused companies said IoT is having a very positive impact on their ability to innovate, compared to just 41% of corporates overall.
  
  “Today’s technology is about measuring things,” says Dr. Aleksander Poniewierski, EY Global Digital and Emerging Technology Leader. “The way you measure processes or people’s behavior or an asset’s condition – that’s IoT. Then when you send this data to the cloud you need to have algorithms and methods to process this information – that’s AI.”
  
  A connected vehicle is a good example of IoT and AI working together: IoT collects data such as speed, location and proximity to objects, and AI interprets this data to make recommendations such as slow down, turn left or stop.
  
  Starbucks is also combining the technologies to great effect. The coffee giant has invested in connected espresso machines that can alert the company to when they need tuning or maintaining, reducing downtime and improving the customer experience.1
  
  The companies that are the most advanced when it comes to AI are also developing expertise around IoT in tandem – an important strategic move. Organizations who want to drive a comprehensive transformation should embrace these crossovers and explore opportunities to integrate multiple capabilities. In doing so, they can develop new business solutions that deliver a holistic response to customer needs.
  
  Recommendations
  Explore AI and IoT integration opportunities across business processes to yield wider benefits simultaneously
  Build PoCs and MVPs to test the solution with a “fail fast” mentality to explore more avenues for value creation
  Myth 3: All data is good data
  Data can come from a huge range of sources – business applications, website analytics, industrial equipment, wearables, social media – the list is endless. But quantity does not equal quality.
  
  “To build a trusted AI system, companies need to have trusted data – data that is from a reliable source, is compliant, is accurate, is clean, is relevant and is transformed for intended purpose,” says Beatriz Sanz Saiz.
  
  Poor data can stem from poor data-collection practices, which have to be stopped. “Traditional companies capture data from multiple applications such as CRM and ERP, from call centers and so on,” says Sanz Saiz. “People collecting this data don’t necessarily care about data quality. What companies need is to have a strong data governance structure in place to control and monitor the data they use to train AI models.”
  
  Unfortunately, most companies still lack a strong data governance model. Our research shows that only 8% of corporate companies have a governance function for emerging technology that is well established and active. But AI-focused corporates are taking the lead in this regard, with 11% of them saying their governance function is well established. “AI could be part of the solution to fix data quality. For example, using algorithms to extract information from handwritten forms, fix data entry errors and match information across systems”, says Gavin Seewooruttun, EY Asia-Pacific Artificial Intelligence and Analytics Consulting Leader. Although only a small uplift, data governance is one of the most difficult and time-consuming elements of establishing a robust AI strategy, so even small gains can be significant.
  
  Recommendations
  Implement a strong and robust data governance structure to ensure trusted data informs business decisions
  Define a clear strategy to capture, cleanse, stage and consume data so that it is secure and provides AI applications with the best quality sources
  Introduce validation checks to ensure AI applications leverage only trusted data
  Don’t let myths get between AI and your success
  AI's potential to benefit companies, customers and society is enormous. But with hype comes confusion, and businesses must work hard to question misconceptions and look at AI with clarity –and define how AI investment can best deliver long-term value.
  
  They can start by investing in robust, well-governed AI solutions that are deeply integrated not only into the core of their business operations, but also with other emerging technologies.
  
  Greater adoption in turn leads to new mindsets that benefit the core business: for example, a well-established governance function for emerging technologies could help mitigate some of the perceived risks of AI and encourage a transformation culture.
  
  And there are already success stories: 85% of the surveyed leaders who have seen financial gains are leveraging data and analytics insights to increase speed to innovate. To start realizing the benefits of AI, the best approach is “future-back”: organizations should ask themselves whether their business will still be relevant in two, five or ten years’ time. Then by working through future-back scenarios that incorporate AI into the strategy, organizations can flex and adapt to ensure they’re following a path to maintain relevance today — and 15 years from now.
  
  By adopting redefined value levers –putting humans@center, enabling technology@speed and driving innovation@scale – and using their purpose to guide them, organizations can build adaptability into their businesses and enable bigger, better transformations, with AI prioritized as a long-term driver of value.
  
  About the research
  The analysis in this report draws on an extensive program of quantitative and qualitative research.
  
  A survey of 570 C-suite and senior business leaders was conducted across 12 countries (US, Canada, Brazil, UK, Germany, France, Italy, Spain, Australia, Japan, China and India) and nine sectors (consumer products and retail; energy; health and life sciences; tech, media and telco; industrial; financial services; education; transportation and logistics; and hospitality). The 570 companies were split into two categories: 500 corporates and 70 start-ups. The data in this report refers to the 500 corporates only, unless otherwise stated. In addition, a number of in-depth interviews were conducted with leading digital transformation leaders.