Rob Olver's 360-degree guide to enhancing employee engagement and productivity with AI

With nearly 50% of global companies set to integrate AI into their operations, and 35% already on board, the era of AI-driven efficiency is rapidly approaching. Yet, many businesses, find themselves at a crossroads: should they stick with traditional methods or embrace the transformative potential of AI?

Among the most promising innovations in this AI revolution are AI assistants. These digital helpers are designed to seamlessly blend into business operations, potentially unlocking new levels of productivity and efficiency. But how exactly do AI assistants make a difference? Do they genuinely enhance productivity, and what additional benefits can they offer?

To unravel these questions, we turned to Rob Olver, Director and Investor at Zeligate. In an exclusive interview with People Matters, Olver provides a deep dive into the world of AI assistants. 

He discusses their role in fostering collaboration between human employees and AI systems, their impact on reducing biases and disparities, and how they can significantly boost employee well-being and productivity.

Excerpts from the interview: 

How to balance the line between empowering employees with AI assistance and maintaining their autonomy?

As with the any new technology adoption in business, how we balance AI assistance with employee autonomy requires a clear and well considered strategy:

Clear guidelines: Develop comprehensive policies outlining when and how AI should be used. These guidelines should emphasise that AI is a tool to augment human capabilities, not replace them and provide examples on ways of working. This should be developed in collaboration with team members to gain valuable subject matter expert insight and buy-in to the process.

Task allocation: Work with the team to identify tasks or workloads that are best suited for AI assistance, such as data analysis, scheduling, or routine report generation. Reserve complex decision-making, creative problem-solving, and tasks requiring emotional intelligence for human employees. 

Customisation options: Allow employees to customise their AI assistants to suit their working style and preferences, giving them a sense of control and personalisation over the engagement layer between the team member and the AI assistant

Regular training: Provide ongoing training to help employees understand AI capabilities and limitations, enabling them to make informed decisions about when to rely on AI and when to use their own judgement. Training should be incorporated into their personal development and aligned with potential career pathways.

Feedback mechanisms: Implement regular surveys or feedback sessions to gauge employee sentiment towards AI tools and promptly address any concerns.

Performance metrics: Ensure that performance evaluations focus on the value employees bring beyond what AI can do, such as creativity, leadership, and interpersonal skills.

Opt-out options: For non-critical tasks, consider allowing employees to opt-out of using AI if they prefer to complete the task manually.

In what ways are AI assistants enhancing the collaborative dynamics between human employees and AI systems?

There are several ways in which AI assistants are changing our ways of working and how we collaborate:

• Intelligent meeting management: AI can transcribe meetings, extract action items, and even suggest follow-up tasks, allowing human participants to focus on the discussion rather than note-taking.
• Data-driven insights: AI can analyse vast amounts of data and present insights in real-time during collaborative sessions, enabling more informed decision-making.
• Project management: AI assistants can track project progress, predict potential delays, and suggest resource allocation, helping teams collaborate more efficiently and effectively.
• Language support: AI-powered translation and language processing can break down communication barriers in global teams, fostering better collaboration across different regions.
• Personalised learning: AI can identify skill gaps in teams and suggest personalised learning resources, promoting continuous improvement and better teamwork.
• Conflict resolution: AI assistants can analyse communication patterns and suggest ways to improve team dynamics or address potential conflicts before they escalate.
• Idea generation: AI can contribute to brainstorming sessions by suggesting ideas based on vast databases of information, sparking new thoughts among human team members with higher critical thinking.

What ethical considerations should be addressed in the deployment of AI assistants to ensure that they do not inadvertently reinforce biases or create disparities among staff?

To ensure ethical deployment of AI assistants we recommend:

Diverse development teams: Ensure that AI development teams are diverse in terms of gender, ethnicity, and background to minimise inherent biases in AI systems.

Transparent algorithms: Strive for transparency in AI algorithms, making them open for scrutiny and audit where possible, whilst protecting IP.

Regular bias audits: Conduct frequent audits of AI outputs to identify and correct any biases in decision-making or recommendations. These processes should be built into AI Product development roadmaps as best practice.

Equal access policies: Implement policies ensuring all employees, regardless of position or department, have equal access to AI tools and training.

Ethics training: Provide comprehensive ethics training for both AI developers and end-users, covering topics like data privacy, fairness, and potential societal impacts.

Ethics review board: Establish an independent ethics review board to oversee AI deployment and usage, ensuring alignment with company values and ethical standards. This is already under consideration in Europe.

Feedback channels: Create anonymous feedback channels for employees to report concerns about AI systems without fear of repercussion.

Data privacy measures: Implement robust data privacy measures to protect employee information used by AI systems.

Contextual decision-making: Ensure that AI recommendations are always contextualised, considering factors that may not be apparent in data alone.

What measures should be in place to ensure that the use of AI assistants positively impacts employee well-being, rather than contributing to job displacement or increased stress?

To promote employee well-being alongside AI integration:

1. Clear communication: Regularly communicate the company's vision for AI, emphasising its role in supporting rather than replacing human workers.
2. Skill development: Offer comprehensive training programs to help employees develop skills that complement AI capabilities, ensuring their continued relevance.
3. Stress monitoring: Use AI to monitor workplace stress indicators and suggest interventions or support when needed.
4. Workload management: Implement AI-powered workload management systems to ensure fair distribution of tasks and prevent burnout.
5. Career path planning: Utilise AI to help employees identify potential career paths and skill development opportunities both within the organisation and broader career pathways.
6. Personalised well-being programs: Use AI to tailor well-being initiatives to individual employee needs and preferences including how local external vendors could be utilised as part of these programs.
7. Human-AI collaboration metrics: Develop metrics that measure the effectiveness of human-AI collaboration, not just individual productivity.
8. Regular impact assessments: Conduct periodic assessments of AI's impact on job satisfaction, stress levels, and overall well-being, adjusting strategies as needed.

How to ensure transparency and accountability in decision-making processes where AI assistants play a significant role? 

To maintain transparency and accountability in AI-assisted decision-making:

Decision logs: Implement systems that log all significant decisions made with AI assistance, including the data and prompts used and the reasoning behind the decision.

Explainable AI: Prioritise the use of explainable AI models that can provide clear rationales for their recommendations or decisions.

Human oversight: Establish clear protocols for human oversight of AI-assisted decisions, especially for high-stakes or sensitive issues.

Appeal process: Create a well-defined process for employees or customers to repeal decisions made by AI systems.

Regular audits: Conduct regular audits of AI systems to ensure they are functioning as intended and not developing unexpected biases or hallucinations.

Performance dashboards: Develop dashboards that clearly show the performance and impact of AI systems across various metrics.

Stakeholder communication: Regularly communicate with all stakeholders about how AI is being used in decision-making processes.

Continuous education: Provide ongoing education to employees about how AI systems work and their role in decision-making processes.

You can also read: 

• How HR will shape the future of AI in Australia
• 5 best practices for AI in HR in 2024
• 20 cutting-edge roles born from the AI revolution in the workplace

Given the rapid advancements in AI technology, how do you foresee the role of AI assistants evolving over the next five years?

Given the pace of evolution in the AI space we should see significant changes in our ways of working and interaction with new tech including assistants:

• Hyper-personalisation: AI assistants will offer highly tailored experiences, adapting to individual work styles, preferences, and even moods.
• Contextual understanding: They will grasp nuanced context in conversations, leading to more accurate and relevant responses.
• Proactive assistance: AI assistants will anticipate needs, suggesting actions or providing information before being asked.
• Multimodal interaction: Integration of voice, text, and visual inputs will allow for more natural and comprehensive communication.
• Emotional intelligence: Advanced sentiment analysis will enable AI assistants to respond appropriately to users' emotional states.
• Autonomous task completion: AI assistants, as guided, will handle entire workflows independently, from scheduling to report generation.
• Collaborative problem-solving: They will actively participate in brainstorming and decision-making processes, offering data-driven insights.
• Continuous learning: AI assistants will learn from each interaction, constantly improving their performance and knowledge base.
• Cross-functional integration: Seamless operation across various business functions and platforms will become standard.
• Enhanced security measures: AI assistants will incorporate advanced security protocols to protect sensitive data and ensure compliance.

While these advancements will significantly enhance workplace productivity, the unique human qualities of creativity, empathy, and complex reasoning will remain irreplaceable. The future workplace will likely see a symbiotic relationship between humans and AI assistants, each complementing the other's strengths.