The Future of AI: What to Expect in the Next 5 Years - TechTarget
In the near future, AI will make us feel that life is speeding up. Human behavior will change and industries will be radically transformed.
implement AI in the decision-making processes
Speed of life. The most obvious change that many people will feel across society is an increase in the tempo of engagements with large institutions. Any organization that engages regularly with large numbers of users -- businesses, government units, nonprofits -- will be compelled to <anchor>implement AI in the decision-making processes</anchor> and in their public- and consumer-facing activities. AI will allow these organizations to make most of the decisions much more quickly. As a result, we will all feel life speeding up.
Industry experts share top tactics for AI-powered analytics | TechTarget
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# Industry experts share top tactics for AI-powered analytics

## Unlock the power of AI in data analytics with expert guidance. Learn how to implement AI tools that drive strategic success and future-proof your business.

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Ana Salom-Boira

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Published: 20 Aug 2024

As AI adoption accelerates across industries, the quality and management of data have become paramount to business success. For executives and decision-makers navigating this rapidly evolving landscape, staying ahead of the curve is crucial to maintaining competitive advantage and driving sustainable growth in an increasingly data-driven economy.

BrightTALK's Data, Analytics and AI: Building a Foundation for Excellence Summit highlights the critical role of data in business success by showcasing four expert thought leaders sharing their insights. From embracing data-driven decision-making to harnessing the power of generative AI, read on to discover how top executives are navigating the complexities of data governance and predictive analytics to future-proof their organizations and unlock unprecedented business value.

## The role of data in decision-making in uncertain times

His key points included the following:

- Have a clear data philosophy. The assumptions we make about data will shape how we use and interpret it for decision-making.
- Question the assumption that more data is always better. An overreliance on data can be detrimental, meaning more data is not inherently superior.
- Removing bias from data could strip away valuable insights because bias is deeply human. We should rethink automatically trying to de-bias all data. "If we remove bias from data ... actually, what we're doing is removing the humanity from the data," Fish said.
- We need to consider whether AI will see and operate under the same paradigms and assumptions as humans, or if it can see solutions outside its training.
- Focus on unsaid questions. Teams need to address questions that are typically avoided. Facing uncomfortable queries that challenge mainstream assumptions will boost innovation.

Fish emphasized the need for flexible, adaptive thinking in data-driven decision-making processes. His presentation laid the foundation for a discussion on the importance of data management and governance for effective AI implementation.

## Building the foundation with a comprehensive approach to data management

In her presentation " Securing the Future: Data Governance in the Age of AI," Joanne Biggadike, deputy head of data at Dual U.K., stressed the pivotal role of data governance in successful AI implementation. Before adopting AI, businesses must ensure investments align with their specific needs.

Her main arguments included the following:

- Data governance establishes trust, traceability and accountability for data used in AI systems. It does this by defining clear ownership, stewardship, quality checks and documentation standards.
- Data governance practices reduce the risk of biased, unreliable or unethical AI outputs.
- AI systems are "data hungry" and could misuse or repurpose personal data without proper safeguards. This raises serious privacy concerns.
- Ethical considerations need to be embedded into data governance frameworks. Key considerations include transparency, consent and alignment with organizational values.
- AI literacy is key. Businesses can improve AI literacy through education and cross-functional collaboration. They should also involve all stakeholders in AI governance decisions.

Biggadike advocated for a holistic data management approach. This involves steps such as implementing a golden source for data and establishing data responsibility hierarchies. Her principles highlighted the significance of preparing data effectively for AI integration and future-proofing businesses in the evolving data landscape.

## Leveraging predictive analytics for optimized decision-making

For Farid Sheikhi, KFC's manager of business intelligence, data is the lifeblood of modern business. His presentation " Insights to Action: Leverage Predictive Analytics to Optimize Decision-Making" discussed the complex decisions data analytics can help inform. He stressed the importance of translating data insights into actionable strategies.

He argued that businesses need to address the following challenges:

- Turning data insights into actionable strategies. Sheikhi argued that businesses lack the ability to convert data insights into action.
- Ensuring data integrity. He referred to the "garbage in, garbage out" challenge, stressing that data needs to be clean and reliable in order for models to work.
- Integrating domain expertise with data science. Sheikhi argued that whoever builds the model needs to understand the underlying business problems before designing anything. Domain expertise and collaboration with key stakeholders are therefore critical for the model's success.
- Continuous model monitoring and updating. He outlined the importance of feedback and iteration for the long-term success of the model.

Looking to the future, Sheikhi outlined significant analytics trends. These include augmented analytics, data democratization and responsible AI. These trends empower businesses to make more informed decisions in today's data-rich environment.

## Unlocking advanced strategic insights with GenAI-enhanced predictive analytics

Efe Ogolo, director of data science and analytics at Sago Mini, closed out the event with his presentation " GenAI-Enhanced Predictive Analytics: Unlock Advanced Strategic Insights," discussing the future of predictive analytics in the era of generative AI. Predictive analytics plays a crucial role in understanding historical data patterns and anticipating future outcomes. It enables proactive decision-making, competitive advantage and risk management.

Ogolo explained how GenAI enhances predictive analytics through the following:

- Improved data quality and availability by generating high-quality synthetic training data.
- Better model performance by mitigating biases and using transfer learning.
- Advanced insights uncovered by identifying nuanced patterns traditional models have missed.

By integrating GenAI with predictive analytics, Ogolo argued, organizations can gain nuanced insights, overcome data limitations and adapt quickly to industry contexts, establishing a competitive edge in today's data-driven business landscape.

As data analytics and AI continue to evolve, their impact on business strategy and decision-making is only set to grow further. These thought leader insights underscore the critical importance of a robust data foundation, ethical governance and innovative approaches to leveraging AI technologies. By embracing these principles, organizations can better navigate the complexities of today's data-driven landscape and position themselves to capitalize on future opportunities.

Ana Salom-Boira is an editorial manager within TechTarget's Editorial Summits team. She also produces and hosts the podcast series Tech Beyond the Hype , which explores how emerging technologies and the latest business trends are shaping the future of work.

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