Trust in the data behind artificial intelligence and other data-driven initiatives is going downhill – fast. A recent survey finds fewer than half of business leaders say they have the data they need to pursue cutting-edge strategies – and their numbers have declined somewhat dramatically over the past two years.

The Current State of Data Trust

That’s the word from a recent Salesforce survey of 552 business leaders, which finds trust in the data that underpins data-driven decisions is falling. For example, only 40% trust the reliability of their companies’ data – down from 54% in a similar survey conducted in 2023. Likewise, 41% say their data is relevant – down from 50% two years ago. And, stunningly, a mere 36% believe their data is accurate – down from 49% just two years ago.

Challenges in Data Analysis

While 63% of leaders say finding, analyzing, and interpreting data on their own is key to their jobs, 54% of them aren’t fully confident in their ability to do this. This lack of confidence is a significant issue, as executives understand that subpar data collection, cleansing, and curation processes can lead to unreliable decision-making.

The Impact of Poor Data on AI Initiatives

Industry observers agree that data poses a problematic issue for AI initiatives of all kinds. Executives are aware that feeding low-quality data into AI models can result in poor decision-making. When it comes to automated processes, the stakes get even higher. “You better have data to back it up, and that’s a problem” said industry analyst Andy Thurai. “Executives are afraid their data is not complete, not wholesome, not timely, stale, not reliable, or not accurate.”

The Role of Synthetic Data

Complicating the situation is the fact that many enterprises now use synthetic data to train their AI models when not enough data is available, or to maintain security. The problem is that “executives’ confidence that the models are trained on real-world data is not there,” Thurai added. This lack of confidence can lead to hesitation in adopting AI solutions.

The Need for a New Approach

Part of the issue may also be the fact that applications designed for the 2020s are relying on technology designed in the 1990s. Enterprises “often struggle with a data infrastructure built over many years that utilizes many different technologies, and was built without a clear plan or direction,” said Dwaine Plauche, senior manager of product marketing for Aspen Technology. What is required is a shift in thinking for the handling and positioning of enterprise data.

Rethinking Data Infrastructure

Plauche advised that enterprises should rethink their data infrastructure as an internal customer service. “The goal should be providing internal customers or projects with needed data – a service-oriented mindset that is aligned with the organization’s strategy.” This approach can help to improve data quality and increase confidence in AI decision-making.

The Role of AI in Data Management

Of course, AI itself can help manage and overcome issues with data. “An adage among veteran data scientists is that good modeling is 80% data preparation and only 20% modeling and analysis,” said Richard Sonnenblick, chief data scientist at Planview. AI can help to identify and filter errors in data, lowering the chances of leading a team to false conclusions.

Using AI to Solve Business Problems

Sonnenblick advised that leaders should start with the business problem and work backwards. “AI for AI’s sake is unlikely to achieve useful results. Leaders should identify the desired outcome of a particular task that AI will support, such as decreased time to completion or a higher success rate. AI excels at identifying patterns in data that are too complex to be quickly recognized by humans, meaning it can provide powerful insights for reassigning tasks or reallocating resources to maximize margins or minimize downtime.”

Conclusion

In conclusion, trust in data is declining, and this is having a significant impact on AI initiatives. To overcome this issue, enterprises need to rethink their data infrastructure and approach to data management. By using AI to manage and overcome issues with data, and by starting with the business problem and working backwards, leaders can increase confidence in AI decision-making and achieve better outcomes.

FAQs

• Q: What is the current state of trust in data?
  A: Trust in data is declining, with only 40% of business leaders trusting the reliability of their companies’ data.
• Q: What is the impact of poor data on AI initiatives?
  A: Poor data can lead to unreliable decision-making and a lack of confidence in AI models.
• Q: How can AI help with data management?
  A: AI can help to identify and filter errors in data, and provide powerful insights for reassigning tasks or reallocating resources.
• Q: What approach should leaders take when using AI to solve business problems?
  A: Leaders should start with the business problem and work backwards, identifying the desired outcome of a particular task that AI will support.

Introduction to Emotional AI

AI is undoubtedly reshaping our lives, but there’s still a great deal of hype surrounding it. One of today’s most popular narratives is that machines are learning to understand human feelings and emotions. This is the domain of affective computing, a field of AI research and development concerned with interpreting, simulating and predicting feelings and emotions in an effort to navigate the complex, often unpredictable landscape of the human psyche. The idea is that emotion-aware AI will lead to more useful, accessible and safer applications.

Understanding Artificial Emotional Intelligence

First, what do emotions even mean in relation to machines? Well, the simple answer is that emotions are just another form of data for machines. Affective computing focuses on detecting, interpreting and responding to data on human emotional states. This can be gathered from voice recordings, image recognition algorithms trained on facial data, analyzing written text or even the way we move our mouse and click when shopping online. It can also include biometric data like heart rate, skin temperature and the body’s electrical activity. Emotional AI tools analyze patterns in this data and use it to interpret or simulate emotional interaction with us. This could include customer service bots detecting frustration or vehicle systems that detect and react to a driver’s state of mind.

The Complexity of Human Emotions

But emotions are complicated things that are highly open to interpretation (including across different geographies and cultures), and it’s often critically important that they aren’t misread. The more data an affective or emotional AI app has, the more closely it will simulate human emotion, and the more likely it will be to accurately predict and respond to our emotional needs. Data alone isn’t enough for a machine to be able to truly “feel.” In fact, research suggests that machines already process data much more quickly than our brains do. Instead, it’s the far greater complexity of our brains, when compared to even the most sophisticated artificial neural networks and machine learning models, that makes us capable of truly feeling and empathizing.

The Ethics Of Emotional AI

This raises some important ethical questions: Is it right to allow machines to make decisions that could affect our lives when we don’t fully comprehend their ability to understand us? For example, we might allow a machine to make us feel cautious or even scared in order to warn us against doing something dangerous. But will it know not to scare us too much, in proportion to the threat, in a way that could cause us trauma or distress? And will chatbots and AIs designed to act as virtual girlfriends, partners or lovers understand the implications of provoking or manipulating human emotions like love, jealousy or sexual attraction? Overstating the ability of machines to understand our emotions poses particular risks that will have to be given serious thought.

Risks And Rewards

Developing emotional AI is big business, as it’s seen as a way to deliver more personalized and engaging experiences, as well as to predict or even influence our behavior. Tools like Imentiv are used in recruitment and training to get a better understanding of how candidates will react to stressful situations, and cameras were used on the Sao Paulo subway to detect the emotional response of passengers to advertising. In one controversial use case, U.K. rail operator Network Rail reportedly sent video data of passengers to Amazon’s emotional analytics service without gathering their consent. The increasing prevalence and potential for invasion of privacy (of our thoughts, no less) has prompted lawmakers in some jurisdictions to take action. The European Union AI Act, for example, bans the use of AI that detects emotions in workplaces and schools.

Challenges and Limitations

One reason for this is the risk of bias — it’s already been shown that the ability of machines to accurately detect emotional responses varies according to race, age and gender. In Japan, for example, a smile is more frequently used to disguise negative emotions than in other parts of the world. This opens the possibility of AI driving new forms of discrimination — clearly, a threat that has to be understood and prevented.

Conclusion

While it’s clear that AI can’t truly "feel," dismissing the implications of its ability to understand our feelings would be a serious mistake. The very idea of letting machines read our minds by understanding our emotional responses will rightly set alarm bells ringing for many. It clearly creates dangerous opportunities that will be jumped on by the ill-intentioned. At the same time, affective computing may hold the key to unlocking therapies that can help people, as well as improving efficiency, convenience and safety in the services we use. It will be up to us, as developers, regulators or simply users of AI, to ensure that these new technological capabilities are integrated with society in a responsible way.

FAQs

• Q: Can machines truly understand human emotions?
  A: No, machines can only analyze and simulate emotions based on data, but they cannot truly feel emotions like humans do.
• Q: What is affective computing?
  A: Affective computing is a field of AI research and development focused on detecting, interpreting, and responding to human emotional states.
• Q: What are the risks associated with emotional AI?
  A: The risks include invasion of privacy, manipulation of emotions, and potential bias in detecting emotional responses, which could lead to discrimination.
• Q: Are there any laws regulating the use of emotional AI?
  A: Yes, laws like the European Union AI Act ban the use of AI that detects emotions in workplaces and schools to protect privacy and prevent misuse.
• Q: Can emotional AI be beneficial?
  A: Yes, it can be used to improve therapies, enhance user experiences, and increase safety and efficiency in various services, but it must be developed and used responsibly.

Introduction to the Impact of Tariffs on the Global Services Market

A lot of our clients, especially CIOs and Global Business Services (GBS) executives, are asking about the effects of the new tariffs on the global services market. The most important impact of tariffs is their effect on the broader macroeconomy. Currently, we are seeing many firms postpone projects and move to a more defensive posture. As the uncertainty continues, we anticipate that enterprises will further curtail their discretionary spending. It is likely that a slowing economy will shift focus away from spending to support growth to cost-cutting initiatives.

Regarding the Potential for Tariffs Levied on Services

As of today, it does not look like there will be tariffs on services. And it’s highly unlikely that even if the tariff wars intensify that there will be tariffs on services. There are numerous reasons for this conviction. However, the primary reasoning is the level of difficulty in executing a tariff strategy on services. Simply put, there is no clean way for any country to put tariffs on invisible exports, such as services, without creating tremendous unintended consequences.

However, there has been some talk that the EU and India will put tariffs/taxes on US firms. These new taxes could be levied on the US firms’ IP and cloud services. That said, it seems that this talk is primarily aimed at creating bargaining leverage for the upcoming trade negotiations and likely will not materialize in actual fact. If the move to tax US IP and cloud services does move forward, we think they will have a modest to negligible effect. Taxes on US cloud services will be easy to evade. There is already substantial processing power in the EU and other untariffed countries. Firms such as AWS, Microsoft, and Google, as well as their clients, will quickly transfer workloads to these centers.

The Modi Administration Positions to Increase Taxes on Services Delivered in India

Modi has signaled that he and his administration are considering increasing the tax levied on services exported to the US and other countries. At this time, this appears to be talk aimed at creating bargaining leverage for future trade talks. However, if they do move forward with these taxes, they will modestly increase the cost of firms’ buying services originating in India. As these taxes have yet to materialize, it is hard to know if they will affect third-party services and GCCs/captives in the same way.

Other Real Dangers to Worry About

Other potential moves by the Trump administration may impact the global business services market. Clearly, this administration has a restrictive view of immigration. At the outset, their early moves have largely focused on the undocumented at our southern and northern borders. These immigrants have very little impact on the services industry. But, if the administration moves to tighten eligibility for H1B and L1 visas, this could adversely affect the services industry modestly. This is a play we have seen before. This time, the industry is well prepared to cope with these potential changes and will lean on the learnings from their experience with the last Trump administration. For that reason, we think this will have a de minimis impact on the global services industry if it happens.

Even the Most Aggressive Tariffs Will Have a Modest Impact Compared with the Growing Impact of AI

Here is the biggest worry for global services executives: the emerging impact of AI on services. This development seems to be much more profound than the threatened tariffs. Companies are becoming more efficient in using AI to do their own development. We believe the impact of AI is likely to be far more significant both in the short run and long-term than the potential impact of tariffs to restructure global trade that the Trump administration is attempting.

We are already seeing firms deploying AI in their IT development and at the same time shifting work in house from third-party vendors. This phenomenon has been at the heart of the slow recovery of IT services spend. Additionally, we are starting to see new system-of-action software eat into both IT and BPO workloads. As this AI revolution unfolds, it is likely to threaten the core assumption around labor arbitrage on which the modern services industry is built on. Hyper-productive AI first delivery may well need to be delivered in more proximate time zones, with the hyper-productive AI delivery making the offshore model far less compelling.

Conclusion

The impact of tariffs on the global services market is a complex issue, but it seems that the tariffs will have a modest effect on the industry. The bigger challenge is the emerging impact of AI on services, which is likely to be far more significant in the short and long term. The industry needs to prepare for this change and transform its operating models to remain competitive.

FAQs

• Q: Will there be tariffs on services?
  A: It is unlikely that there will be tariffs on services due to the difficulty in executing a tariff strategy on invisible exports.
• Q: How will the Modi administration’s potential tax increase on services delivered in India affect the global services market?
  A: The tax increase will modestly increase the cost of firms buying services originating in India, but its impact is unclear as it has yet to materialize.
• Q: What is the biggest worry for global services executives?
  A: The emerging impact of AI on services, which is likely to be far more significant than the threatened tariffs.
• Q: How will the AI revolution affect the global services industry?
  A: The AI revolution will threaten the core assumption around labor arbitrage, making the offshore model less compelling, and will require the industry to transform its operating models to remain competitive.