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Challenges with AI Deloitte Insights

Posted by Test on June 30, 2022

To solve this challenge, ensure that datasets are vetted for potential biases and cleaned before using them to develop new AI tools. All these businesses produce enormous volumes of data every single day, but AI is rarely used to analyze massive datasets and draw conclusions from these patterns and features of that data. We need to learn what can bridge the gap between those AI problems and the profitability of a business.

As machines learn from data sets they’re fed, chances are “pretty high” they may replicate many of the banking industry’s past failings that resulted in systematic disparate treatment of African Americans and other marginalized consumers. There’s another researcher who has a famous TED Talk, Joy Buolamwini at MIT Media Lab. And she says, “Look, a lot of the other researchers are more male and more pale than I am. We tend to look for opportunity under the streetlight of technical novelty—in the lower-right quadrant of our two-by-two—trying to find those big new technologies that will change the world. A more productive approach might be to acknowledge that a significant factor in AI’s recent successes has been the development of our digital infrastructure. Rather than looking for opportunities to apply particular AI technologies that have been successful in the past, look for problems in our business environment where the operating context has changed significantly in the past five to ten years.

Risks of Artificial Intelligence

Our past research has found majorities of Americans express concern about online privacy generally and a lack of control over their own personal information. Other uses of AI where opinions tilt more positive than negative include helping companies make safe cars and trucks and helping people take care of their health. Unfortunately, several recent studies have shown that human-AI teams often do not currently outperform AI-only teams.1 Still, there is a growing body of work on methods to create more effective human-AI collaboration in both the AI and human-computer-interaction communities. As this work matures, we see several near-term opportunities for AI to improve human capabilities and vica versa. ITU maintains an AI Repository where anyone working in the field of artificial intelligence can contribute key information about how to leverage AI for good. This is the only global repository that identifies AI-related projects, research initiatives, think tanks and organizations that aim to accelerate progress on the 17 United Nations Sustainable Development Goals (SDGs).

Innovating new applications for AI and advancing the field requires even more knowledge. Generative AI offers many positive opportunities to bring about efficiencies in ESG data management workflow, but it also raises new questions. To answer these questions, solutions providers need to prioritize the concerns around accuracy, bias, and trust by revealing proactively what they are doing to address them. For example, Friedman described how someone asked him his view of the ethics of using generative AI to write a company’s sustainability report. In response, he asked how using ChatGPT is any different ethically from using a professional outside writer to write a company’s sustainability report.


Similarly, Alejo said generative AI will remove some types of jobs but also “open up brand new types of jobs that those same people can take advantage of.” The mainstreaming of generative AI offers here-and-now-capabilities, the promise of future advances — and more than a few pitfalls. The unemployed fared better in Massachusetts with 65 percent receiving benefits, as opposed to 8 percent in Florida. While the European Union already has rigorous data-privacy laws and the European Commission is considering a formal regulatory framework for ethical use of AI, the U.S. government has historically been late when it comes to tech regulation. “There’s no businessperson on the planet at an enterprise of any size that isn’t concerned about this and trying to reflect on what’s going to be politically, legally, regulatorily, [or] ethically acceptable,” said Fuller.

Challenges When Using AI

In some cases, further challenges to the integration of AI systems come in the form of legal or economic incentives; for example, malpractice and compliance concerns have limited the penetration of AI in the health sector. Regulatory frameworks for safe, responsible innovation will be needed to achieve these possible near-term beneficial impacts. Automated systems are already in use for blood-glucose control and providing ways to monitor and coordinate care at home. AI-based tools can allow people with various disabilities—such as limitations in vision, hearing, fine and gross mobility, and memory—to live more independently and participate in more activities. Many of these programs can run on smartphones, further improving accessibility.

Ranking your organization’s approach to managing performance

Google is using AI to help people facing disease and natural disasters, and to provide new opportunities for underserved communities. You can do it internally, within your company, or outsource the work, you can use synthetic labeling or data programming. All of these approaches have their pros and cons, as presented in the table below. The rise of prerecorded interviews, however, introduces a new format to the recruitment process—one that might not appeal to every applicant. Some candidates may find it uncomfortable or unnerving when they know they’re being recorded, which could negatively affect their performance and cause the company to miss out on a qualified candidate. One of the most common complaints about AI screening tools for resumes and job applications is the frequency with which they reject documents with unfamiliar structure or formatting.

Challenges When Using AI

AI systems may also help predict the effects of different climate-change-mitigation or pandemic-management strategies and search among possible options to highlight those that are most promising. These forecasting systems typically have limits and biases based on the data they were trained on, and there is also potential for misuse if people overtrust their predictions or if the decisions impact people directly. And it is important to remember, by the way, as we think about all the exciting stuff that’s going on in AI and machine learning, that the vast majority—whether it’s the techniques or even the applications—are mostly solving very specific things.

The future of work challenge

When applied thoughtfully, AI-driven tools can unlock transformative intelligence at speeds and scales impossible just a few years ago. Generative AI will likely restructure how work gets done in many fields, a prospect that raises job-loss concerns. An article on the Chinese video game industry states job opportunities for artists are vanishing as companies employ AI-based image generators. Legacy systems “have a very specific way of doing that, and now you’ve got generative AI that’s leveraging AI Customer Service way different types of thinking,” Alejo explained. “Organizations have to find new ways to either create integrations or adopt new capabilities, with new technologies, that enable them to reach the same outputs, or outcomes, faster and more effectively.” Thus far, companies that develop or use AI systems largely self-police, relying on existing laws and market forces, like negative reactions from consumers and shareholders or the demands of highly-prized AI technical talent to keep them in line.

  • • Educate teams early on AI best practices, ethics, limitations and pitfalls to instill critical thinking and discernment.
  • Build your confidence by learning essential soft skills to help you become an Industry ready professional.
  • • Predictive analytics techniques can continuously analyze structured and unstructured customer data to uncover non-obvious correlations between attributes and behaviors indicating high-value segments.
  • Hidden insights emerge that would be impossible to manually detect across siloed datasets.
  • Health care experts see many possible uses for AI, including with billing and processing necessary paperwork.
  • Refine the AI screening models regularly until outcomes consistently meet company needs.
  • Certain services may not be available to attest clients under the rules and regulations of public accounting.

Despite all this promise, adoption of AI is not where many expected (or hoped) that it might be. Autonomous cars are a prime example, where commercial, mass market, versions constantly seem to be a decade away, despite early success and significant investment. We hear a similar story from AI practitioners working in firms attempting to leverage AI, with carefully developed models and solutions left on the bench as they are either not compelling enough or too fragile to replace existing solutions. There are notable successes, such as machine language translation, however there appears to have been more misses.

The promise and challenges of AI

These might well represent problems whose time has come, as we can now bring a range of digital tools to create innovative new solutions to old problems. In the past, solutions that depended on large packages of technologies were at a disadvantage. It was quite possible, for example, to create impressive AI-powered legal analysis solutions in the 80s and 90s, though these solutions were hamstrung by the need to manually enter information from paper documents before the solution could do its work.

Challenges When Using AI

If you’re a company where marketing and sales is what drives the value, that’s actually where AI can create the most value. If you’re a company where operational excellence matters the most to you, that’s where you can create the most value with AI. If you’re an insurance company, or if you’re a bank, then risk is really important to you, and that’s another place where AI can add value. It goes through everything from managing human capital and analyzing your people’s performance and recruitment, et cetera, all through the entire business system. We see the potential for trillions of dollars of value to be created annually across the entire economy [Exhibit 1]. Without a robust, repeatable, and scalable data value chain, AI can’t scale.

The Need For Complementary Capabilities

Change your Analytics and performance cookie settings to access this feature. Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action. Some AI-based programs—including EndeavorRx, a video game designed to treat attention-deficit/hyperactivity disorder—have even received clearance from the U.S. Food and Drug Administration for use under medical supervision (Kollins, S. H., et al., npj Digital Medicine, Vol. 4, 2021). If political rivalries and warmongering tendencies are not kept in check, artificial intelligence could end up being applied with the worst intentions. By automating routine or unsafe activities and those prone to human error, AI could allow humans to be more productive and to work and live more safely.

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