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14 November 2023

human & AI

Top data trends 2024: Human & AI integration

Artificial intelligence is undoubtedly one of the hottest topics in the big data space today, and its importance will only increase in the coming years. With the growing use of AI, new risks are emerging related to bias, discrimination, trust, transparency, privacy, ethics, safety, security, finance, and corporate reputation. Each year, additional risks, laws, and policies to regulate the use of AI have led organizations to begin adopting guidelines, practices, roles, and tooling for AI governance and responsible AI.

AI, smart items, and machine learning technologies are all designed with an underlying expectation that things would work much better – or at least more cheaply – without humans and human interaction. However, a number of factors are driving a more balanced view of data and analytics that accepts that, although AI tends to outperform humans in some use cases, AI tools plus human insight tend to deliver better results than AI alone.

The importance of retaining the human element

AI systems increasingly have the capability to replace human decision-makers but are often subject to strategic, ethical, or logical blind spots that make their use dangerous. The power to deploy advanced analytics “because we can” requires a balancing human consideration of “but should we?”

It’s important to recognize human involvement across a large range of decision-making processes from decision support to decision automation. Even the most automated decision-making process needs human involvement in its initial design and formation and in its subsequent monitoring and evaluation to avoid unintended consequences and repercussions.

Future implications of AI & human integration

The integration of AI and humans has already begun, and it has the potential to continue revolutionizing various aspects of our lives. This focus on retaining the human dimension in the field of data and analytics has three key implications:

  • Driving adoption of data and analytics initiatives: Giving workers the ability to control their own environment has repeatedly proven to increase performance and satisfaction. Giving data and analytics users the power to influence or even control the way analysis is created and used increases the likelihood of its use in their decision-making.
  • Ensuring data and analytics deployments consider multiple aspects of risk: The need for data and analytics leaders to guard against the worst possible implications of trends in the big data space while ensuring the best potential outcomes requires careful attention and diligent governance.
  • Efforts to drive decision automation without considering the human role in decisions could result in the creation of a data-driven organization without conscience or consistent purpose.

The integration of AI and humans holds immense promise, but careful consideration and ethical guidelines are essential to navigate the potential challenges and ensure a positive impact on society. As AI becomes more integrated into daily life, ethical concerns related to privacy, bias, and decision-making will arise. Striking a balance between innovation and ethical considerations will be essential for a sustainable and responsible future.

Key steps to ensure optimal human & AI integration

Amidst the ever-growing rise of AI, it’s important to remember the human element in decision-making, collaboration, and information development. To do this, it will become increasingly important over time to establish and uphold ethical frameworks that prioritize human well-being, privacy, and fairness in AI development. 

Emphasizing user-centric design, transparency, and inclusive development with diverse teams fosters technological advancements that enhance human capabilities and maintain user trust, and education and training programs should continue to inform the public about AI, promoting responsible interaction. 

The following are a few key steps organizations can take to ensure balanced human collaboration with AI elements:

  • Data literacy: Data literacy programs must include education about the appropriate way to combine data and analytics with human decision-making skills. Data literacy programs that solely focus on enabling “data-driven” decisions only will not achieve their change management potential if they forget to include the human element.
  • Agility: (Re)assess opportunities for using AI within and across business areas.
  • Cross-functional collaboration: Begin or continue adopting good AI engineering practices including a stronger collaboration between data engineers, AI experts, and software developers.
  • Change management: As AI “joins the team” and impacts business roles, activities, and processes, make sure that change management and HR aspects are fully addressed in AI initiatives.


The future of working with AI will change organizational life as we know it! Encouraging human/AI collaboration as a tool for augmentation rather than replacement, along with continuous feedback mechanisms, ensures systems are adaptive and responsive to user needs. Legal and regulatory frameworks must safeguard human rights, privacy, and safety, while responsible data practices and human oversight in critical decision-making processes further prioritize the human element in the AI landscape.

Looking for more insights like these? Find similar insights from Gartner in our latest report “Innovation Insight: How Generative AI Is Transforming Data Management Solutions.” Access your complimentary copy of the report to discover the Gartner research guide for genAI adoption, showing what to expect from vendors and how to identify potential risks.

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