The award manager’s guide to using responsible, human-centred AI

AI is becoming ubiquitous these days—from our internet search engines to our email clients to our documents, spreadsheets and social media feeds. It’s fast becoming an everyday part of our work lives, as many of us scramble to figure out how to best use AI to work smarter while staying true to our mission and tasks at hand.

AI has also arrived in the awards management sector. We can now use it to manage high volumes of entries, streamline evaluation for judges and save time by providing quick summaries of entries and submissions.

The question is fast becoming not whether to use AI, but how generative AI can be used responsibly as a tool to enhance fairness and drive efficiency, without compromising integrity or judgment.

For awards programs built on credibility and recognition, responsible, human-centred adoption of AI truly matters.

The challenges: data privacy, program security, community trust

The rise of AI has raised important concerns, especially regarding data privacy and program security.

Awards programs are trust-based systems. When entrants feel that decisions are automated, opaque or biased, it can undermine years of credibility.

Uncontrolled AI can introduce risks into the awards lifecycle such as hidden bias, cloudy decision logic and an over-reliance on automation.

For awards managers, the challenge now lies in gaining efficiency without losing human judgement, accountability or explainability.

The opportunity: explainable, human-centred AI

When used responsibly, AI does not replace human expertise. It supports it. A human-centred approach to AI means technology that assists people in making better decisions, instead of making decisions for them.

In the awards management lifecycle, this could include:

• Helping administrators categorise entries more efficiently
• Supporting judges by summarising long submissions
• Flagging inconsistencies or missing information before judging begins

AI should never determine winners. Instead, it should act as a tool to reduce manual effort so judges can focus on what matters most: qualitative assessment, discussion and collaboration, and professional judgement.

Award Force is unique in its offering of optional AI tools because it applies this philosophy by embedding intelligent features within structured, human-led workflows. The outcome is scale with control. Judges remain decision-makers, administrators remain accountable and participants experience a fair and transparent process.

This is where explainable AI becomes essential. Any AI-supported insight should be understandable, reviewable and open to challenge. If an organiser cannot explain how a tool supports an outcome, it should not be present in the awards process.

Practical applications: responsible AI in action

Responsible AI does not require complex implementation. It starts with clear boundaries and purpose.

Just take this practical example:

An international awards program receives over 2,000 entries annually. Administrators use AI-assisted tools to pre-check submissions for completeness and format consistency before judging begins. The system highlights missing responses and unusually short answers but does not score or rank entries.

Judges receive clean, standardised submissions and optional AI-generated summaries clearly labelled as support material. Judges can ignore, edit or challenge these summaries and always refer to the original content. All scoring remains human-controlled.

The result:

• Reduced administrative workload
• Faster decision-making for judges
• Improved consistency without removing human judgment

This is responsible use of generative AI in practice: supportive, transparent and optional.

Get more ideas! See a list of practical AI prompts for common use cases in Award Force.

Tips for adopting responsible, explainable AI

Awards professionals do not need to be AI experts to adopt intelligent technology responsibly. A few basic principles go a long way.

1. Define what AI can and cannot do
Be explicit in your organisational guidelines. For example, AI should not score, rank or decide outcomes. Document these limits internally and communicate them clearly to judges and stakeholders.

2. Keep humans in the loop
Every AI-supported output should be reviewable, editable and dismissible by a person. Keep human oversight at the forefront.

3. Prioritise explainability
Choose tools that offer clear logic and visibility. If you cannot explain how an output was generated, you should not rely on it in a high-stakes environment.

4. Communicate openly with judges
Transparency builds trust. Let judges know when AI is used, how it supports them and how they remain in control. This aligns with best practice outlined in resources such as the UK Information Commissioner’s Office guidance on explaining decisions made with AI.

5. Start small and measure impact
Introduce AI in low-risk areas such as administrative checks or workflow support. Evaluate outcomes before expanding its role.

Powering awards excellence, responsibly

When done well, responsible AI does more than save time. It strengthens confidence. Entrants trust that their work is reviewed fairly. Judges feel supported rather than replaced. Administrators enjoy clarity and control.

Fairness, credibility and excellence will not be disrupted by AI if human-centred design comes first.

In Award Force, the AI tools are human-controlled. Awards managers have the option to turn them on, choose which AI agent they trust and decide when and how to use them, all within the secure Award Force virtual private cloud. Program data is never shared with third parties or connected to the internet.

Award Force AI tools help awards programs remain efficient, fair and trusted as they scale.

Responsible AI is not about technology for technology’s own sake. It is about empowering people to deliver recognition that truly matters.