Automation, Artificial Intelligence, the Internet of Things, Blockchain, Robotics, and many other technological innovations have rapidly changed how people live, communicate, work and how industries collectively operate.

AI, in particular, is gaining global interest, drawing debate on its innovative adaptation in business, healthcare, education, and government (Stahl, 2021). The technology is based on machine learning principles; its genius is its ability to quickly provide and disseminate information in seconds and to change and rewrite algorithms (Stahl, 2021).

The discussion on tech and the changing dynamic of workplaces has been ongoing. However, most conversations before 2022 were fixed on robotics and its impact on the human-employment relationship.

A few years ago, we were bombarded with headlines like ‘Machines are taking our jobs’ or ‘Your colleague is a bot’ with every management consultancy firm tackling tailored skills programmes, preparing the human worker to adapt to changes in work and future skills requirements.

Notably, this was geared toward blue-collar workers in manufacturing, where automation and robotics directly threatened the job functions and security of millions.

The Impact of AI on Social Development Practitioners: New Challenges and Future Skillsets

AI, however, presents a new challenge to the human worker; besides its information processing speed, AI can also write, create pictures, videos, and speech, and identify sound (Marr, 2019). The entertainment industry has already been penetrated with AI technology, replacing actors and screenwriters and even creating songs for artists without them singing words (Davenport & Bean, 2023).

At this juncture, there is no denying that core job skills are threatened by technological innovation; however, unlike automation adoption, AI also impacts knowledge workers’ skills to a degree. These people’s job is to think for a living, such as development practitioners.

For a practitioner in social development, several skills are essential to effectively evaluate programmes and their impact on bringing or facilitating change.

Some of these skills include:

  • Analytical skills
  • Data collection and management
  • Programme evaluation techniques
  • Project management skills
  • Reporting and presenting results
  • Ethical understanding and sensitivity
  • Stakeholder engagement
  • Critical thinking & problem-solving
  • Continuous learning and adaptability
  • (Eval Community, 2024a; Transparency and Accountability Initiative, 2019)

Therefore, the fundamental questions for us as development practitioners are:

  • Can AI replace social development practitioners’ skills?
  • Can it be as impactful in the sector as in others, changing how it operates?
  • What are the future desired skill sets for practitioners?


The Potential of AI and Machine Learning in Monitoring and Evaluation

Let’s try to answer the questions posed above (with nuance):

AI & Machine Learning could play a definitive role in monitoring and evaluation in the future.

For instance, it can provide valuable tools and data for data collection, data entry, data cleaning and analysis, data visualization, enhancing data quality, identifying trends and insights, and providing real-time insights (Institute for Development, 2023; Eval Community, 2024b).

In particular, AI can be extremely useful for monitoring and evaluation processes that are time-consuming, resource-intensive and subject to human error (Institute for Development, 2023).

The Irreplaceable Human Element: Nuanced Skills in Evaluation Practice

It is, however, the nuanced understanding, ethical judgment, interpersonal skills, emotional intelligence, empathy and critical thinking that AI cannot replace in practitioners (Stahl, 2021; Khalaf, 2023). These skills are integral to the role; AI cannot make rational judgments — no moral or principle code or value system guides its decision-making (Khalaf, 2023).

Thus, it cannot replace the human capacity to understand complex social dynamics, make ethical decisions, engage effectively with people, and apply critical thinking to solve problems and adapt strategies. Thus, the degree to which the knowledge worker enhances and develops their soft and technical skills becomes the difference between their expertise and what AI can and cannot do.

AI Can Enhance Evaluators’ Skill Sets but Can’t Replace Them

Technology can enhance a process or service but cannot replace a skill. Instead of practitioners spending hours and even days on repetitive tasks such as data entry and cleaning, these tedious tasks can be processed by AI.

As a result, the time freed up could be better spent by evaluators to develop new approaches and tools or engage with stakeholders. Therefore, the value-added value of technology in the field is evident; however, it should not be regarded as a threat or even a replacement for the critical skills of knowledge workers.

Developing a Human-AI synergy

A synergistic relationship between human capabilities and computational power can exist. After all, both have inherited strengths, which can be beneficial and even complimentary. The human brain may not be able to compete with the processing speed of AI; however, the technology is limited in its ability to solve multiple problems and can only execute one task at a time (Stahl, 2021).

Furthermore, the AI is often rigid and can only perform ‘thinking’ from data it has been trained on (Stahl, 2021). Although AI’s ability to replicate some forms of ‘human intelligence’ was best displayed during an international chess championship.

IBM developed an impressive Deep Blue AI-powered computer to play chess, which requires strategy, foresight and logic (Greenemeier, 2017). Deep Blue beat the world chess player Garry Kasparov in a six-game match in 1997 (Greenemeier, 2017). But rather than highlight AI’s ability to outperform humans, this example actually highlights the limitations of AI:

Chess is a well-defined game with rules, moves and goals. As a game, chess’ rigidity provides the kind of environment in which a computer program can thrive (Greenemeier, 2017).

So, combining the strength of AI in rigid rule-based environments, such as data and numbers, with the nuanced requirements needed for stakeholder engagement and communications management that humans inherently possess is a synergy that can have widespread positive impacts.

A Word of Caution Against an Over-Reliance on AI

The over-reliance on AI should also be cautioned. The excitement around technology is warranted; however, practitioners should note that AI has limitations that can impact the results sought.

AI requires learning to be effective, which requires massive amounts of data, highlighting questions about how and where such data will be sourced and its impact on privacy and security (Stahl, 2021).

Furthermore, the areas of social development require ethical data collection adherence to privacy and consent as well as safeguarding sensitive information and participants’ rights (Eval Community, 2024b).

AI Can Be Trained on Biased Data

The most crucial factor for practitioners to note is that the data carries bias, which can reflect societal inequalities and the implicit bias of the designers who create and input data (Stahl, 2021; Eval Community, 2024b).

This bias can also influence the output of the results — it is essential to reflect on what this would mean in the social development sector.

In Summary

What will be more important is how the industry and practitioners shape and develop skills. Practitioners can prepare in several ways to ensure their skills remain practical and relevant.

This includes:

  • Staying informed on emerging technologies
  • Developing technical skills
  • Embracing digital data collection methods
  • Enhancing data analysis capabilities
  • Understanding the limitations of technology
  • Adopting a flexible and adaptive mindset
  • Strengthening soft skills
  • Networking with peers and experts and integrating technology in ethical and contextually relevant ways

(Khalaf 2023; Eval Community, 2024b)

By embracing technology, continuously learning, and balancing technical skills with essential soft skills, practitioners can effectively navigate the evolving landscape of their field.

AI Automation Learning And Development Monitoring And Evaluation