Overview

This report outlines the findings from the recent AI readiness survey conducted at Saxton Bampfylde and provides actionable recommendations for integrating AI into executive search and leadership advisory. The survey revealed varying levels of AI understanding across roles, with concerns around technical expertise, ethical implications, and data privacy. Based on these insights, this plan proposes targeted AI training, strategic pilot programmes, and ethical governance frameworks to enhance AI adoption. By addressing the identified barriers and emphasising AI's potential in streamlining processes and broadening candidate searches, Saxton Bampfylde can maintain a competitive edge while ensuring that AI complements its core values of human-centred leadership and decision-making.

1. Role Distribution

2. AI Understanding Levels

• Insight: A majority of respondents have an "Intermediate" level of understanding of AI (42.86%), while 25.71% rate their understanding as "Basic." Only 22.86% have an "Advanced" understanding, and a notable 8.57% admit to "No understanding."

• Implication: While there is some AI knowledge, there is a clear need for further education and training, especially for those at the "Basic" and "No understanding" levels. Investing in training programmes will be crucial for improving overall AI literacy, allowing the company to leverage AI tools effectively across all roles.

3. Frequency of AI Tool Use

• Weekly: The most common response, with 22 individuals using AI tools on a weekly basis. This suggests that a majority of users have integrated AI tools into their workflow regularly.

• Never: Nine respondents indicated they never use AI tools, highlighting that there is still a significant portion of individuals not incorporating AI into their daily work.

• Rarely: Eight individuals use AI tools infrequently, possibly when specific tasks or projects require them.

• Daily: Seven people use AI tools on a daily basis, showing that a smaller but important group relies heavily on AI tools in their daily routine.

• Monthly: Six respondents reported using AI tools on a monthly basis, indicating occasional use.

This distribution suggests that while many individuals use AI tools regularly, there is still a notable portion of users who either rarely or never use these tools. The data indicates room for growth in daily AI tool adoption across the board.

4. Degree of understanding of AI by Role

1. Associate Consultants:
   • There is a fairly even split between those with a "Moderate" and "Low" understanding of AI, with no "High" or "Very low" responses.
   • This suggests that while associate consultants are not extremely knowledgeable about AI, a significant number have a working understanding.

2. Business Support (e.g., Finance, Events, HR):
   • The majority of responses indicate a "Moderate" understanding of AI, but there are also "Low" and "Very low" levels represented.
   • This suggests that while some members of business support roles have grasped AI concepts to some extent, there remains a group with very basic or no understanding.

3. Consultants:
   • Consultants show a strong tendency toward "Moderate" understanding, with very few "Low" and no "Very low" responses. No one in this group reports a "High" level of understanding.
   • This indicates that consultants have a relatively balanced but moderate knowledge of AI's application, with a few lagging behind.

4. Project Coordinators:
   • Project coordinators show the widest range of understanding, from "Very low" to "Moderate". There are multiple "Low" and "Very low" responses, indicating a gap in knowledge.
   • This suggests that while some project coordinators are catching up, a significant portion still struggles to understand AI's relevance or application.

5. Researchers:
   • The researcher group has a significant number of respondents with a "Low" understanding of AI. However, a notable number also report "Moderate" understanding, and there is one response indicating "High" understanding.
   • This implies that researchers, while still largely in the lower to moderate range of understanding, include individuals with higher levels of knowledge about AI.

General Insights:

• Moderate understanding dominates across most roles, indicating that employees at Saxton Bampfylde generally have a basic to moderate grasp of AI concepts, but there is room for growth.

• Low understanding is particularly notable among project coordinators and researchers, signalling a need for targeted AI training in these groups.

• High understanding is almost absent across all roles except for one researcher, suggesting that advanced AI literacy is rare in the company.

• Very low understanding is seen mostly in project coordinators and business support roles, which may indicate that these positions have less exposure or need for AI-related knowledge in their daily functions.

This distribution suggests that while there is a growing understanding of AI in most roles, additional training or resources could help raise the baseline, particularly in groups where "Low" and "Very low" responses are frequent.

5. Correlation between AI understanding and comfort with AI in decision-making

Analysis:

• High AI Understanding: Surprisingly, individuals with a "High" understanding of AI were "100% Uncomfortable" with AI-assisted decision-making. This could indicate a deeper awareness of potential challenges or risks associated with AI in decision-making.

• Moderate AI Understanding: The majority (39.3%) were "Comfortable," while 35.7% were "Neutral," and 25% were "Uncomfortable." This suggests a more balanced view among those with moderate AI knowledge, potentially indicating openness but with caution.

• Low AI Understanding: Some respondents (42.1%) were "Neutral," with a significant portion (47.4%) feeling "Uncomfortable." Only 10.5% were "Comfortable," showing that lower understanding often corresponds with hesitancy towards AI decision-making.

• Very Low AI Understanding: Half of the respondents (50%) were "Uncomfortable," with 25% choosing "Neutral" and 25% "Very Uncomfortable." This demonstrates a clear trend of discomfort in those with minimal AI knowledge.

Key Insight:

• The discomfort among those with high understanding may point to a need for more discussions on how AI can be used ethically and effectively in decision-making. On the other hand, the trend of discomfort in lower AI understanding suggests a need for more education and exposure to AI capabilities and limitations.

6. In which areas of executive search and leadership advisory could AI have the most impact?

Analysis

1. Top Areas for AI Impact:
   • Administration/paperwork/back office support stands out as the most frequently selected area, indicating that many employees believe AI can significantly streamline administrative tasks.
   • Market intelligence and trend analysis also ranks high, suggesting a belief in AI's capability to gather and analyse market data more efficiently.
   • Candidate sourcing and CV screening are next in line, indicating a strong belief in AI's potential to enhance the talent acquisition process.

2. Less Frequently Mentioned Areas:
   • Areas such as succession planning, drafting complex documents, and specific tasks like mapping (e.g., 'Name 10 charities that have a membership body') are mentioned far less frequently, suggesting that they are seen as less likely to benefit from AI adoption.

The overall trend suggests that employees view AI as a tool primarily for handling repetitive, data-heavy tasks, freeing up time for more strategic work. This insight can help prioritize AI integration in areas where the highest impact is anticipated.

7. To what extent do you agree with the statement: "AI will significantly change executive search and leadership advisory in the next 5 years?” Analysis of data.

Analysis

The responses to the statement "AI will significantly change executive search and leadership advisory in the next 5 years" show a predominantly positive sentiment towards the impact of AI in the industry:

• Agree: This is the most frequently selected response, indicating that a significant portion of respondents believe AI will have a notable impact on executive search and leadership advisory. This suggests a general consensus that AI will play a transformative role in the industry over the next five years.

• Strongly Agree: A considerable number of respondents also strongly agree with the statement, reinforcing the perception that AI is expected to bring about significant changes. This level of conviction suggests that many individuals anticipate AI to be a driving force in reshaping processes and approaches within the field.

• Neither Agree Nor Disagree: A moderate proportion of respondents chose this neutral option, indicating some uncertainty or ambivalence about the extent of AI's impact. This could reflect a need for more information or understanding about how AI might influence the industry or a belief that while changes will occur, they may not be as profound as others expect.

Key Takeaways

• There is a clear expectation among the majority of respondents that AI will significantly influence executive search and leadership advisory within the next five years.

• The strong representation of agreement and strong agreement responses suggests that the organisation is aware of AI's potential and is likely open to integrating AI solutions into their processes.

• The presence of neutral responses highlights a segment of the workforce that might need further education or exposure to AI's potential benefits and applications.

This data indicates a generally positive attitude towards AI's future role in the industry, with an opportunity to address any uncertainties through targeted awareness and training initiatives.

8. To what extent do you agree with the statement: "AI will significantly change executive search and leadership advisory in the next 5 years?” Analysis of comments.

Positive Sentiments:

• Efficiency and Automation: A significant portion of respondents believe AI will improve efficiency, especially in administrative tasks, data processing, and candidate sourcing. This is seen as a way to free up time for high-value activities, like relationship building and strategic thinking.

• Supportive Role: Many view AI as a supportive tool that will complement human judgement, particularly in tasks like screening candidates, generating paperwork, and streamlining workflows.

• Innovation and Evolution: There’s optimism about AI being an enabler of change, making processes faster and expanding the pool of potential candidates. Several respondents likened this shift to previous technological evolutions, such as moving from telephone-based research to internet research.

Neutral Sentiments:

• Uncertainty: A few respondents expressed uncertainty about the full extent of AI's impact due to a lack of understanding or knowledge. They acknowledged potential benefits but were unsure about how AI would integrate effectively into the existing workflows.

• Balanced Perspective: Some respondents recognised that AI would bring changes but emphasised the importance of human elements, such as emotional intelligence and relationship building, which AI cannot replace.

Negative Sentiments:

• Concerns About Redundancy: There were concerns about job security, with some respondents expressing fear that AI could replace roles, particularly for researchers and PCTs.

• Data Accuracy and Bias: Worries were raised about the accuracy and reliability of AI, as well as the potential for bias in decision-making, especially when AI tools are used for candidate screening or psychometric assessments.

• Dependence and Over-reliance: Some expressed caution about over-relying on AI, emphasising that human judgement, experience, and personal interactions remain crucial in the executive search process.

Conclusion

The overall sentiment is cautiously optimistic, with a majority acknowledging the potential for AI to significantly change the industry. However, there is a strong emphasis on the need to balance AI capabilities with the irreplaceable human elements of executive search and leadership advisory, ensuring that AI serves as a tool that enhances, rather than replaces, human expertise. The visualisations will effectively capture the nuanced perspectives and provide actionable insights into how the organisation can navigate the adoption of AI in a way that aligns with these sentiments.

9. What benefits do you see in incorporating AI into your role in the future? 

Analysis:

The responses suggest that the participants perceive several key benefits of incorporating AI into their roles, which can be grouped into the following themes:

1. Time Efficiency and Automation:
   • Many responses emphasise the role of AI in saving time by automating repetitive or manual tasks such as candidate screening, data input, drafting reports, and scheduling.
   • AI is seen as a tool that can handle basic administrative tasks, freeing up time for more strategic, value-added activities.

2. Improved Productivity and Focus on Strategic Tasks:
   • Respondents believe AI will enable them to focus on more impactful tasks like business development (BD), strategic planning, and relationship building.
   • This will shift their role towards higher-level activities that require human judgment and expertise.

3. Enhanced Research and Candidate Identification:
   • AI is viewed as a powerful tool for identifying potential candidates more effectively, searching databases, and providing more accurate matching.
   • Respondents suggest that AI can help scan CVs and provide summaries, enhancing the accuracy of candidate targeting and identification.

4. Quality and Accuracy:
   • Several respondents mentioned AI's potential to improve the accuracy of their work, such as generating better-quality reports, marketing materials, and reducing biases in candidate selection.

5. Data Utilisation and Insights:
   • AI is seen as a means to leverage existing data more effectively, enabling the extraction of valuable insights from client and candidate information.

6. Overcoming Bias and Enhancing Diversity:
   • A few responses highlight AI's role in reducing personal biases, which could improve diversity in candidate selection and assessment.

Overall, the responses highlight a positive sentiment towards AI's role in making work more efficient, saving time, and focusing on higher-value tasks. This suggests that many employees see AI as a means to enhance their roles, particularly in reducing administrative burdens and improving candidate identification processes.

10. What concerns do you have about the increased use of AI in your industry? 

The key themes that emerge include:

• Loss of Human Touch: A primary concern is the fear that AI may reduce the relationship-building and empathetic aspects of the work, making it more metrics-focused and less personal.

• Job Security: Many respondents are worried about the possibility of job redundancy and being replaced by AI, which highlights a fear of displacement in the industry.

• Data Quality and Privacy: Issues around data accuracy, confidentiality, and GDPR compliance were frequently mentioned, indicating concerns about maintaining trust and security.

• Over-reliance and Creativity: There is apprehension that excessive dependence on AI could lead to a reduction in creative thinking, intuition, and personalised candidate searches.

• Bias and Accuracy: Concerns about AI perpetuating biases, making mistakes, or delivering inaccurate outputs were raised, suggesting a lack of trust in AI’s judgment.

Overall, while AI's potential is acknowledged, there is a notable apprehension about its impact on the human-centric elements of the industry, data security, job roles, and maintaining creativity in search processes.

11. How might AI impact your role in the future?

1. Time Spent on Admin Tasks

• Significant Impact: The majority of respondents (over 80%) expect AI to have a "Significant impact" on reducing time spent on administrative tasks. This suggests that there is a widespread belief that AI can effectively automate or streamline administrative work.

• Some Impact: A smaller portion sees "Some impact," indicating a perception that while AI will help with admin tasks, it might not entirely take over or transform these processes.

Conclusion: AI is expected to be a game-changer in reducing admin work, freeing up more time for employees to focus on higher-value tasks.

2. Quality of Candidate Matches

• Significant Impact: The results are mixed here, with a good proportion expecting a "Significant impact," but also a substantial number indicating "Some impact." This suggests that while AI is seen as a tool that can enhance candidate matching, there is less certainty about its effectiveness compared to admin tasks.

• Not Applicable: A notable portion of responses indicate "Not applicable," suggesting that some respondents believe that AI may not be relevant in affecting candidate match quality, possibly due to the nuanced human judgement required.

Conclusion: There is optimism about AI improving the quality of candidate matches, but it is tempered with caution, reflecting a belief that human insight will still be crucial.

3. Search Process Speed

• Significant Impact: This category has one of the highest expectations of significant impact, showing that respondents believe AI will greatly accelerate the search process.

• Some Impact: A fair number expect only "Some impact," indicating that while AI will be beneficial, there might still be some limitations in how much it can streamline the process.

Conclusion: AI is expected to expedite search processes, making it more efficient, but there may still be elements that require human involvement.

4. Accuracy of Leadership Assessments

• Significant Impact: There’s a reasonable belief that AI could have a "Significant impact" on improving the accuracy of leadership assessments, but this is not as strong as in other categories.

• Some Impact and No Impact: A substantial percentage selected "Some impact" or even "No impact," indicating skepticism about AI's ability to handle the complexities involved in assessing leadership potential.

Conclusion: There is uncertainty about how well AI can assess leadership qualities, with many still valuing human judgment in this area.

5. Client Satisfaction

• Significant Impact: A solid majority believe that AI can have a "Significant impact" on improving client satisfaction, likely due to enhanced efficiency, accuracy, and speed in the search process.

• Some Impact: Some respondents are less certain, indicating that AI's influence on client satisfaction might be indirect or dependent on how effectively it is implemented.

Conclusion: AI is expected to contribute to higher client satisfaction, but the extent of this impact might vary depending on implementation.

6. Ability to Identify Non-Traditional Candidates

• Significant Impact: Many respondents feel that AI will have a "Significant impact" on identifying non-traditional candidates, suggesting that AI's ability to process vast amounts of data could uncover candidates who might otherwise be overlooked.

• Some Impact: A considerable number still see only "Some impact," suggesting that there is a belief that human insight and intuition play a significant role in identifying non-traditional candidates.

Conclusion: AI is seen as a valuable tool for broadening the search to include non-traditional candidates, but human oversight is still considered important.

Overall Analysis:

• High Potential for Impact: The most consistent expectation is that AI will have a "Significant impact" on time spent on admin tasks, search process speed, and client satisfaction, indicating that these are the areas where AI is likely to be most transformative.

• Moderate Impact: Areas such as the quality of candidate matches, accuracy of leadership assessments, and the ability to identify non-traditional candidates are seen as having potential for impact, but there's more skepticism or belief in the necessity of human involvement.

Recommendations Based on Analysis:

1. Focus on Administrative Efficiency: Given the strong expectation that AI will reduce admin tasks, efforts should be made to implement AI tools in this area first to free up employee time for more strategic activities.

2. Enhance AI in Candidate Matching: There is optimism about improving candidate matching, but the approach should be iterative, with human oversight to refine and enhance AI capabilities.

3. Training and Integration: For aspects like leadership assessments and identifying non-traditional candidates, AI should be integrated in a way that complements human expertise, with adequate training to leverage AI insights effectively.

4. Client-Centric AI Strategies: Since client satisfaction is expected to improve with AI, ensure AI implementations are client-focused, improving efficiency, accuracy, and responsiveness.

By leveraging AI's strengths where the highest impact is expected and ensuring a human-centred approach in more nuanced areas, Sax Bam can maximise AI's potential while minimising risks.

12. How might AI impact your role in the future? Analysis of comments

The data reveals several key themes about how respondents expect AI to impact their roles in the future, including both opportunities and concerns:

1. Efficiency and Time Savings

• Common Theme: The most frequently mentioned impact is the potential for AI to reduce administrative burdens and streamline repetitive tasks, such as data entry, report generation, diary management, and other manual tasks.

• Respondent Perspective: Many believe that AI could free up time spent on mundane activities, allowing them to focus on more value-added tasks, such as strategic planning, creative candidate mapping, and direct interactions with clients and candidates.

Implication: This indicates that there is a strong expectation that AI will significantly enhance efficiency, enabling employees to dedicate more time to higher-level responsibilities.

2. Increased Accuracy and Reduced Errors

• Common Theme: Several respondents anticipate that AI will help reduce the chances of human error, especially in tasks like data entry, report writing, and market mapping.

• Respondent Perspective: AI is seen as a tool that could ensure more accurate and consistent outputs, particularly for repetitive or detail-oriented tasks.

Implication: This suggests that AI is expected to improve the quality and reliability of outputs, reducing the risk of errors and increasing confidence in data-driven processes.

3. Enhanced Candidate Search and Identification

• Common Theme: Many believe AI could significantly improve the speed and quality of candidate identification and sourcing. It could help sift through large volumes of data to find potential candidates more efficiently.

• Respondent Perspective: There is optimism that AI could widen the pool of candidates, provide shortcuts for more straightforward research, and offer better matching of candidate profiles.

Implication: AI could be highly valuable in expediting the candidate search process, although human oversight will still be crucial for nuanced candidate assessments and interactions.

4. Concerns About Job Redundancy and Loss of Human Touch

• Common Theme: Despite the optimism, there is a concern that AI could lead to job redundancy, especially for roles heavily reliant on administrative or data-driven tasks. Several respondents expressed fears that AI could replace aspects of their jobs, particularly in areas like data entry and routine tasks.

• Respondent Perspective: There is a concern about losing the human touch in their roles, particularly in tasks requiring judgment, creativity, and personal interactions with candidates and clients.

Implication: Sax Bam needs to address concerns about job security and emphasise that AI is a tool to enhance rather than replace human capabilities, especially in tasks that require empathy, relationship-building, and complex decision-making.

5. Potential to Improve Marketing, Thought Leadership, and Training

• Common Theme: Some respondents believe AI could help produce more thought leadership content, identify marketing opportunities, and provide more frequent and accurate insights, enabling them to work smarter and be more productive.

• Respondent Perspective: AI could be used as a tool to enhance their expertise, such as helping craft reports, identifying trends, and even serving as a training resource for processes.

Implication: There is potential for AI to support not only operational tasks but also more strategic and creative functions, helping employees become more effective in their roles.

6. Importance of Adaptability and Keeping Up with AI Developments

• Common Theme: Several respondents highlighted the need to keep up with AI developments to remain relevant in their roles. They recognise that AI is evolving rapidly, and there is an expectation that they will need to adapt to these changes.

• Respondent Perspective: There is an acknowledgment that AI will be a bigger focus in the future and that employees must be proactive in learning how to leverage AI effectively in their work.

Implication: Continuous training and development in AI tools and practices will be essential to ensure that employees can fully harness the benefits of AI in their roles.

7. Areas Requiring Caution

• Concerns: There are concerns about potential over-reliance on AI, lack of creativity, and the risk of making the search process too automated. Respondents worry about losing the personal, nuanced aspects of their roles, which AI might not be able to replicate.

Conclusion:
Overall, respondents view AI as a tool with significant potential to improve efficiency, accuracy, and the speed of processes in their roles. However, there are concerns about job security, maintaining the human touch, and ensuring that AI complements rather than replaces the value of human judgment and creativity. To maximise the benefits of AI, Sax Bam should focus on integrating AI in a way that enhances productivity while addressing employees' concerns through training, support, and clear communication about the role of AI in their work.

13. How comfortable are you with the idea of AI assisting in final candidate recommendations or leadership development decisions? 

The data reveals the varying levels of comfort among respondents regarding the idea of AI assisting in final candidate recommendations or leadership development decisions. Here is a detailed breakdown:

• Uncomfortable: A significant portion of respondents feel uncomfortable with AI assisting in these decisions. This discomfort is likely rooted in concerns about losing the human touch, potential biases, or lack of trust in AI's decision-making capabilities. As AI becomes more integrated into processes, addressing these concerns will be crucial.

• Neutral: Many respondents hold a neutral stance, suggesting they may be open to the idea but aren't fully convinced or opposed. This group represents a segment that could be swayed positively or negatively based on how AI is presented, implemented, and demonstrated in the decision-making process.

• Comfortable: A smaller, yet notable number of respondents feel comfortable with the idea of AI assistance. These individuals might recognise the potential efficiency, accuracy, and data-driven insights AI can offer. Their comfort level indicates a readiness to adapt and embrace AI as a supportive tool.

• Very Uncomfortable: There are fewer respondents in this category, indicating that while there is some strong opposition, it is not widespread. This group may need additional education, reassurance, or exposure to AI's capabilities to feel more at ease with its role in decision-making.

Implications:

• Training and Education: Given that many respondents are neutral or uncomfortable, there is an opportunity to provide training and education about how AI can complement human judgment and decision-making. This could help shift perspectives and increase comfort levels.

• Trust Building: Trust is a significant factor in AI adoption. Ensuring transparency, explaining AI's role, and demonstrating its benefits in pilot projects can help build confidence among those who are currently uncomfortable or neutral.

• Customisation: Sax Bam might consider offering more tailored AI solutions that allow human intervention or override, ensuring that AI serves as a supportive tool rather than a replacement.

Overall, while there is a range of comfort levels, most respondents are either neutral or uncomfortable. This suggests that with the right approach, there is potential to increase acceptance and comfort with AI-assisted decision-making in leadership and candidate selection processes.

14. How comfortable are you with the idea of AI assisting in final candidate recommendations or leadership development decisions?  Comments

The comments in response to the question about how comfortable participants feel with AI assisting in final candidate recommendations or leadership development decisions reveal several key themes:

1. Nuanced Human Judgment: Many participants emphasise the importance of human judgment, especially for assessing a candidate's cultural fit or the interpersonal dynamics that AI may not be able to capture. This is echoed in comments about AI lacking emotional intelligence or observational skills that humans bring to the table.

2. Bias and Reliability Concerns: There are concerns that AI may introduce bias or may not be as reliable as human consultants, particularly when dealing with diverse or nuanced situations. Some express skepticism about how seriously AI recommendations would be taken by clients.

3. AI as a Tool, Not a Decider: A significant number of participants are open to AI assisting in the decision-making process, but with a clear understanding that AI should serve as a tool rather than the primary decision-maker. They view AI as providing an additional data point or assisting with mundane tasks, but the final call should rest with humans.

4. Client and Candidate Relationships: Several comments suggest that AI cannot replace the personal touch required in building relationships with clients and candidates, which is crucial in the leadership advisory and executive search field. There are concerns that over-reliance on AI could erode this human connection.

5. Potential Benefits: Despite reservations, some participants see AI as a helpful tool to enhance efficiency, identify candidates, and reduce time spent on administrative tasks. There is openness to using AI for early-stage processes like candidate sourcing, but with reservations about its role in final decisions.

6. Uncertainty About AI's Role: A few participants express uncertainty or lack of knowledge about how AI would be used in their role, indicating a need for more clarity on the applications and limitations of AI in leadership decisions.

7. Ethical Considerations: Some comments highlight ethical concerns about relying on AI for final decisions affecting a candidate’s career and livelihood. There is a sense that decisions with significant human impact should always involve a human element to ensure fairness and empathy.

Overall Sentiment:

The overall sentiment is cautious optimism. While participants see potential benefits in using AI to improve efficiency and reduce bias, they maintain that final decisions—especially those involving candidate selection and leadership recommendations—should rely on human expertise and judgment. Many are open to AI as a supporting tool, but they emphasise the irreplaceable value of human interaction in their field.

15. How might AI help us improve diversity and inclusion in executive search and leadership advisory? 

The comments regarding how AI might help improve diversity and inclusion (D&I) in executive search and leadership advisory reveal a range of insights and concerns. Here's an analysis of the key themes:

1. Uncertainty and Limited Knowledge:

Many respondents express uncertainty about how AI can support D&I initiatives. Several comments reflect that individuals do not feel knowledgeable enough about AI to understand its impact on D&I. This suggests a need for more education and awareness about AI's capabilities in this domain.

• Examples: "Not sure", "I don't know", "I probably don't know enough about AI to answer that."

2. Bias Reduction:

A recurring theme is that AI has the potential to reduce human biases in the search and evaluation process. Respondents hope that AI can offer an objective viewpoint and help uncover talent from non-traditional or underrepresented backgrounds by not focusing on factors like age, gender, or ethnicity.

• Examples: "It can remove unknown human bias", "I suppose it could remove human bias."

3. AI’s Potential to Widen the Talent Pool:

A number of participants believe AI could help broaden their reach, identifying candidates they may not have considered otherwise or expanding searches into less traditional networks. AI could potentially look beyond existing, often homogeneous, networks to discover candidates from diverse backgrounds.

• Examples: "AI might help extend our reach into the market to find new candidates", "Hopefully improve it - finding people that have come up via non-conventional routes."

4. Concerns About AI Reinforcing Bias:

Despite the potential benefits, there are concerns that AI might replicate or even reinforce existing biases if it's not carefully designed and monitored. AI is seen as only as good as the data it's trained on, which could potentially reintroduce biases inherent in the data.

• Examples: "However, it seems there is some evidence that AI can re-produce the biases of whoever built it", "It has the potential to be harmful, as well as supportive."

5. Efficiency in Identifying Diverse Candidates:

Several respondents mentioned AI's ability to make the search process more efficient, allowing for a broader exploration of candidates beyond the existing database. They see AI as a tool to automate candidate sourcing and diversity tracking, allowing consultants to focus on building relationships.

• Examples: "Faster and more effective search methods", "It could help find candidates we may never have found before."

6. Ethical and Legal Complexities:

A few comments hint at the ethical complexities of using AI in diversity-focused searches. Concerns are raised about data privacy laws and how AI could navigate these while helping to promote D&I. Some respondents also highlight the challenge of ensuring that AI maintains ethical standards without becoming too invasive or violating legal regulations.

• Examples: "I am unsure as to how this might be possible given data privacy laws", "Ethically very complex."

7. AI’s Role in Supporting Inclusion:

There are hopes that AI could assist in the creation of more inclusive candidate packs and interview questions, which might support diversity in the selection process. AI is seen as potentially supporting processes that foster a more inclusive environment, both in terms of candidate identification and assessment.

• Examples: "Tracking diversity of candidates throughout the process", "Suggestions for more inclusive candidate packs and interview questions."

8. The Unknown Impact of AI on D&I:

Many comments reflect ambivalence or an incomplete understanding of how AI might impact D&I. Some responses suggest that AI's role in D&I is still speculative and will depend on how the technology evolves and how well it is implemented.

• Examples: "It might widen the pool", "I don't think it will necessarily."

Conclusion:

Overall, respondents see potential for AI to help reduce human bias and identify diverse candidates more efficiently, but there are concerns about how effectively AI can address these issues without reinforcing existing biases. Education about AI's role in D&I and careful implementation will be crucial in harnessing its potential while mitigating risks.

16. To what extent do you think AI could enhance our ability to identify and develop high potential leaders? 

Analysis:

• Somewhat: The majority of respondents (approximately 70%) believe that AI could "somewhat" enhance the ability to identify and develop high-potential leaders. This indicates a cautious optimism about AI's role in this area, reflecting that while AI can provide assistance, its impact may still be limited without human insight.

• A lot: Around 12% of respondents think AI could have a significant impact on identifying and developing high-potential leaders. This suggests that a smaller segment is more confident in AI's capabilities in leadership development.

• Not much: About 18% of respondents are more skeptical, feeling that AI wouldn't have much influence. These responses could indicate concerns about the limitations of AI in understanding the nuanced human qualities required for leadership.

This analysis shows a mix of opinions, with a majority acknowledging AI's potential role, but also highlighting skepticism about its capacity to fully handle leadership identification and development. ​

Analysis of Explanations:

The explanations reveal a mix of optimism, curiosity, and caution about AI's potential to enhance the identification and development of high-potential leaders. Several key themes and insights can be drawn from the responses:

1. Efficiency and Time-Saving:
   • Many respondents see AI as a tool that could free up time for more creative and strategic thinking. It could help identify individuals who aren't already within established networks or databases, which would allow more time to be spent on nurturing and developing potential leaders rather than merely identifying them. For example, one respondent stated, "It should give us more time to think about those creative options, and to assess potential."
   • Speeding up the search process and expanding the pool of candidates through data analysis and broader search capabilities was mentioned frequently. AI could “aid identification faster allowing for exploration of a greater field,” and respondents see AI as a means to bring fresh talent to the fore more quickly.

2. Supporting Leadership Development:
   • Several responses emphasise that while AI can assist in gathering data and making initial assessments, human involvement remains crucial, especially in the mentoring and development of leadership potential. Respondents highlighted that the human judgment required for leadership development may not be fully replaceable by AI. One respondent put it well: “I think a lot of that comes from meeting people and acting as a slight mentor, encouraging them to go for roles they may not have ever considered for themselves.”
   • AI’s role in leadership development is seen as supportive, providing additional insights and helping identify potential that could otherwise be missed. AI might be able to help track leadership achievements, such as identifying candidates featured in articles or nominated for awards.

3. Data-Driven Insights:
   • Respondents view AI as a valuable tool for analysing larger datasets, collating information about candidates, and providing objective insights. One response mentioned, “AI may help find people we do not currently know,” which shows the belief that AI can widen the search beyond typical networks. Additionally, AI could analyse career trajectories, skills, and other factors quickly, highlighting candidates who might not have been previously considered.
   • However, some respondents expressed concerns about the quality of data being fed into AI systems. There’s an acknowledgment that “AI is only as good as the data we enter,” which suggests that data biases and limitations could hinder AI’s effectiveness.

4. Human Intuition and Emotion:
   • A recurring sentiment is that AI can only go so far in assessing the emotional and interpersonal qualities of candidates, which are crucial for leadership roles. AI’s potential to miss important nuances of personality, emotional intelligence (EQ), and cultural fit is a concern. Respondents believe these aspects are best evaluated through human interaction. For example, one response stated, “This feels like it needs to be by human to human interaction,” indicating that leadership decisions require a deeper level of personal connection than AI might offer.
   • Some respondents are also cautious about AI’s inability to assess potential or personal attributes accurately. One stated, “I think a lot of what we do is about potential, and this feels like it's harder for AI to assess or quantify.”

5. Future Potential and Uncertainty:
   • There is a belief that AI could significantly improve over time and might eventually be able to provide more robust insights into leadership potential. However, many respondents believe that AI has not yet reached this level of capability. One response mentioned, “We are not there yet though,” suggesting that AI's role in leadership development may evolve but isn't fully reliable now.
   • Uncertainty and lack of knowledge were also common. Some respondents were unsure about how AI would be used or lacked enough information to give a confident answer. This indicates a need for greater education and transparency around AI’s capabilities in this context.

Key Insights:

• AI’s Strength in Identification: Respondents generally agree that AI could enhance the search and identification process by broadening candidate pools and using data to uncover hidden talent. It’s seen as a valuable tool for initial screenings, offering efficiencies and new insights.

• Human Judgment is Crucial for Development: AI is less trusted when it comes to leadership development, where personal interaction, intuition, and emotional intelligence are essential. There’s concern that AI lacks the ability to assess leadership potential fully or account for personal growth and development.

• Bias Concerns: Several respondents raised concerns about AI’s potential biases, especially since AI systems rely on the quality of data inputs. While AI can theoretically reduce human bias, there’s a risk that AI may perpetuate the biases present in the data it is trained on.

This analysis highlights both the potential benefits and limitations of AI in leadership identification and development, with a consensus that AI is best used as a tool to assist, rather than replace, human decision-making.

17. What AI training or resources would be most beneficial to you in your role?

This dataset provides insight into which types of AI training and resources would be most beneficial for participants in the context of executive search and leadership advisory. Respondents were able to select multiple options, so we can see where there is overlap in the desired training areas. The following key themes emerge from the data:

1. Specific AI Tool Training (Most Requested):
   • The most frequently selected training need is "Specific AI Tool Training," indicating a strong desire for hands-on, practical knowledge of how to use AI tools directly relevant to their roles. This reflects the growing recognition that AI tools will become integral to daily operations, and employees need to know how to navigate them effectively.

2. General AI Awareness Training (Second Most Requested):
   • Many participants selected "General AI Awareness Training," suggesting that there is still a need for basic knowledge about AI. This may be driven by the fact that AI remains a relatively new and evolving technology in executive search, and many employees might not fully understand how AI operates or how it can be applied in their work. General training would give them foundational knowledge and a sense of comfort with AI technologies.

3. Data Analysis Skills:
   • A significant number of respondents indicated that they want to improve their data analysis skills. AI and data go hand-in-hand, and understanding how to analyze large datasets is becoming increasingly important in leadership advisory roles. Many participants likely recognise that with the influx of AI-generated data, they will need to be proficient in analysing and interpreting this data to make informed decisions.

4. Ethical Considerations of AI in HR:
   • Ethics is a strong concern, with many respondents selecting "Ethical Considerations of AI in HR" as a critical training need. This reflects the understanding that AI has the potential to introduce or perpetuate biases, and there is a need for training that ensures ethical, fair, and inclusive practices when using AI, particularly in HR-related tasks such as hiring and leadership development.

5. AI Governance and Best Practice:
   • Finally, many respondents see the need for "AI Governance and Best Practice" training. This suggests a desire to understand how AI systems should be managed, monitored, and implemented responsibly within organisations. Governance training would provide clarity on the proper usage of AI tools, ensuring compliance with legal, ethical, and organisational standards.

Key Insights:

• Overwhelming Interest in Practical AI Tool Training: Specific AI tool training is the most selected option, showing a need for practical skills in how to use AI effectively in daily tasks.

• Strong Interest in Ethical and Governance Training: Many participants are concerned about the ethical implications of AI and the governance required to implement AI in a responsible way. Ethical considerations and governance practices are seen as vital to the adoption of AI technologies.

• Data Analysis Skills in High Demand: The growing importance of data in AI-driven processes highlights the need for stronger data literacy across teams.

18. To what extent has AI been implemented in your department or team's processes? 

• Limited implementation: 50.0% (26 responses)

• Not at all: 38.5% (20 responses)

• Moderate implementation: 11.5% (6 responses)

This data suggests that while AI has seen limited implementation in half of the departments, a significant portion of teams has not implemented it at all. Moderate implementation remains relatively low.

Analysis of AI Implementation in Departmental or Team Processes:

From the responses, it is clear that AI has not been formally implemented in most teams or departments, and the use of AI tools like ChatGPT is primarily driven by individual experimentation rather than organisational policy. Below is a more detailed analysis based on the key themes identified in the responses:

1. Individual Use of ChatGPT:
   • Common Practice: Many employees mention that they use ChatGPT for specific tasks such as drafting proposals, emails, and summarising large amounts of information. However, this use is largely informal and done on an individual basis rather than as part of a formalised process.
   • Purpose: ChatGPT is often used to improve the efficiency of writing-related tasks. People leverage it for generating initial drafts, improving language, summarising content, and exploring search ideas.
   • Self-Initiated: The general sentiment is that employees are "dabbling" with AI tools, with no clear company-wide endorsement or formal integration. The initiative to use these tools is coming from individual team members rather than being a department-wide strategy.

2. Lack of Formal Implementation:
   • No Official Adoption: The majority of respondents indicate that AI has not yet been officially implemented within their departments. Some note that while AI tools are discussed, they have not been formally integrated into the organisation's workflows.
   • Testing and Experimentation: A few teams, such as the social impact practice, are experimenting with tools like Co-Pilot for summarising interviews and report writing, but this is still in the pilot phase rather than full implementation.

3. Positive Impact of AI in Specific Tools:
   • Calendly Mention: While Calendly is not strictly an AI tool, its mention highlights the positive impact of automation tools on the team’s efficiency. Some respondents view AI similarly, expecting it to help streamline repetitive tasks and reduce manual labor.
   • Minimal AI Use Beyond ChatGPT: Apart from some mention of Calendly, most AI use is centred around ChatGPT. Other AI tools or systems have not yet been broadly adopted.

4. Concerns and Cautious Use:
   • Privacy Concerns: Some respondents mention concerns around privacy when using AI tools. For example, one individual uses ChatGPT only occasionally, worried about the data privacy implications.
   • Resistance to AI: Some respondents express that their departments are "AI-averse," and some people are cautious about revealing their use of AI tools because they perceive that their teams may react negatively to AI adoption.

5. Lack of Training and Guidance:
   • No Formal AI Training: Many respondents mention that there has been no formal training on AI within their teams. This suggests a need for structured training programs to help team members understand how AI tools can be effectively and responsibly used.
   • No AI Policies: There appears to be no formal guidance or policies on how AI should be implemented in daily tasks, leading to fragmented and inconsistent usage across teams.

6. Opportunities for More Comprehensive AI Integration:
   • Potential for Greater Use: Some responses express a desire for more comprehensive AI integration, indicating that employees see the potential for AI to reduce repetitive tasks and free up time for more strategic work. Several individuals mention that the company has only "scratched the surface" of what AI could offer, and they expect more opportunities in the future.

Conclusion:

• AI, particularly ChatGPT, is being used in a limited, informal capacity within teams, mostly for writing-related tasks.

• There has been no formal adoption of AI across departments, and some individuals are hesitant to openly use AI tools due to concerns about privacy and the perception of AI within their sectors.

• There is an opportunity to formalise the use of AI through training and the implementation of policies that encourage ethical and productive use of AI tools.

• Departments that embrace AI more fully could realise significant efficiencies, especially in tasks that involve drafting, summarising, and research processes.

This analysis suggests that while AI is making inroads into individual workflows, there is significant potential for broader, more strategic adoption if supported by formal training, guidance, and a clearer AI strategy within teams.

19. What are the main barriers to AI adoption in your industry?

Analysis of Barriers to AI Adoption in the Industry:

From the responses provided, several key themes emerge regarding the main barriers to AI adoption within the industry. Below is a detailed analysis of the trends, including the most frequently cited concerns.

1. Lack of Technical Expertise:
   • Most Common Barrier: The most frequently cited barrier to AI adoption is a lack of technical expertise. Many respondents feel that their teams do not have the necessary knowledge or skills to effectively implement and manage AI systems.
   • Impact on Adoption: Without sufficient technical expertise, even identifying suitable AI tools or understanding how to leverage AI for business purposes becomes difficult. This is a critical barrier to address for Sax Bam looking to advance AI adoption.

2. Resistance to Change:
   • Cultural Resistance: Resistance to change is a significant barrier cited by many respondents. This suggests a hesitation among staff or leaders to adopt new technologies due to concerns about disrupting established processes or skepticism regarding the benefits of AI.
   • Industry-Specific Factors: The industry’s reliance on the “individual touch” and personal relationships is a factor contributing to this resistance. The fear that AI might diminish the personal, relationship-based elements of the work is a recurrent theme.

3. Data Privacy Concerns:
   • Privacy as a Key Barrier: Many respondents are concerned about data privacy when it comes to adopting AI tools. Given the sensitive nature of client and candidate information in the executive search and leadership advisory industry, ensuring data security is a high priority.
   • Hindrance to Widespread Adoption: Until AI tools can address these data privacy concerns effectively, this will remain a significant barrier to their widespread adoption.

4. Ethical Concerns:
   • Concerns About Bias: Ethical concerns also play a substantial role in preventing AI adoption. Respondents are worried about biases that AI systems may inherit from their developers or datasets, potentially leading to discrimination or unfair practices in recruitment and leadership development.
   • Balancing Efficiency with Fairness: There is a sense that AI, while efficient, might not be able to maintain the ethical standards that are critical for evaluating leadership potential or making key hiring decisions.

5. Implementation Cost:
   • Cost as a Barrier: A significant number of respondents view the cost of AI implementation as a barrier.
   • Need for Cost Justification: In order to adopt AI tools, many teams will need a clearer understanding of how these tools can bring value and justify the costs associated with their implementation.

6. No Clear ROI:
   • Uncertainty Regarding Value: Some respondents cite the lack of a clear ROI as a reason for not adopting AI. There is uncertainty about whether the benefits of AI outweigh the costs, particularly in an industry where personal relationships and human judgment are highly valued.
   • Lack of Proven Success: Until AI can demonstrate measurable improvements in outcomes, there will be hesitancy to invest in its widespread use.

7. Awareness of AI Tools:
   • Lack of Awareness: One respondent mentioned not being aware of what AI tools are available, highlighting that a lack of knowledge about the available AI solutions is also a barrier to adoption.
   • Education and Exposure: Providing more education and exposure to relevant AI tools could help address this barrier and encourage exploration of AI’s potential benefits.

Key Findings:

• The lack of technical expertise and resistance to change are the most common barriers, with many individuals and teams unsure about how to effectively implement AI.

• Data privacy and ethical concerns play a significant role, especially given the sensitive nature of data in the industry.

• Cost and ROI are important factors, with many respondents questioning whether AI investments can be justified.

• There is also a need for more awareness and education around the available AI tools and how they can be effectively integrated into Sax Bam’s processes.

Conclusion:

Addressing these barriers will require targeted initiatives focused on building AI skills, demonstrating the ROI of AI tools, ensuring robust data privacy measures, and addressing ethical concerns. With these barriers in mind, Sax Bam can develop a more strategic approach to AI adoption that mitigates concerns and enhances its ability to leverage AI technology effectively.

20. Are there specific AI use cases or concerns in executive search and leadership advisory Saxton Bampfylde should explore? 

The feedback on specific AI use cases and concerns in executive search and leadership advisory highlights several key areas of interest and apprehension. The responses can be grouped into the following themes:

1. Maintaining the Human Touch:
   • Several respondents emphasised the importance of preserving the human aspect of the work. The personal touch in interactions with clients, candidates, and colleagues is seen as crucial to the industry. This indicates a concern that while AI may improve efficiency, it should not replace human judgment, empathy, and relationship-building.

2. Bias and Ethical Concerns:
   • Bias in AI: One of the frequently mentioned concerns is that AI could replicate the biases of those who design and operate the systems. This underscores the need for training and awareness to ensure that AI use does not inadvertently perpetuate discrimination.
   • Ethical Use of Data: Respondents also raised concerns about ensuring candidates’ privacy and obtaining consent when using AI for assessments. Clarifying AI's role and ensuring transparency with clients and candidates were seen as essential, suggesting that Saxton Bampfylde should develop clear guidelines or a "statement of use" regarding AI.

3. Privacy and Data Security:
   • Data Privacy: Several respondents mentioned the importance of protecting personal data. They suggest that Saxton Bampfylde explore how AI usage aligns with data privacy laws and what implications it may have for the candidate and client experience. There is also a suggestion that the company might benefit from an in-house AI solution to keep proprietary and sensitive data secure.
   • Consent and Transparency: Alongside privacy, there is a call for transparency with clients and candidates regarding AI use in search processes, particularly regarding consent for AI to be used in candidate assessments.

4. Efficiency and Administrative Support:
   • AI for Admin Tasks: A recurring theme is the use of AI to reduce the administrative burden. Many respondents noted that AI could improve efficiency in tasks like taking notes during conversations, summarising data, and managing routine paperwork. This would free up consultants to focus on more strategic and relational aspects of their work.
   • Research Acceleration: Respondents also saw potential for AI to accelerate research tasks, particularly in candidate identification and information gathering. Implementing AI for these activities could make the search process more efficient and allow more time for meaningful candidate interactions.

5. Environmental Impact:
   • One respondent raised a concern about the environmental impact of AI usage. This indicates an awareness of the broader implications of AI and suggests that the company should be mindful of the carbon footprint associated with AI technologies.

6. Client and Candidate Reactions:
   • Client-Driven Adoption: It was noted that client reactions will likely drive the adoption of AI within the company. As clients become more familiar with AI, their expectations will shape how the firm integrates these technologies.
   • Potential Disconnect: Some responses expressed concern that AI could create a disconnect between the firm and its clients or candidates. This suggests a need for careful implementation to ensure AI complements, rather than disrupts, the personalised nature of executive search and leadership advisory.

Key Findings:

• Balancing AI and Human Judgment: The industry is wary of AI fully replacing the human elements of judgment, especially in candidate evaluations and client relationships. AI should be viewed as a tool to support, not replace, human interaction.

• Bias, Privacy, and Transparency: AI introduces concerns about bias and privacy. To address this, the company could benefit from developing transparent, ethical guidelines on AI use, including data privacy protocols and candidate consent.

• Operational Efficiency: AI’s strongest potential lies in automating administrative tasks and streamlining research, allowing teams to focus on high-value activities.

• Client and Environmental Considerations: As clients drive the demand for AI, the firm must be prepared to address both the environmental impact and the evolving expectations of AI usage in the search process.

Conclusion:

Saxton Bampfylde should explore AI's potential in ways that enhance efficiency and improve research without compromising the personal relationships and human judgment that are central to the firm's success. Addressing concerns about bias, privacy, and ethical considerations will be crucial for successful AI implementation. Additionally, client education and transparent communication about AI’s role will be key to maintaining trust and satisfaction.

Action Plan for AI Implementation:

1. Prioritise AI Education and Training:
   • Targeted AI Literacy Programs: Since a significant portion of the team has low or moderate AI understanding, develop structured AI training programs tailored to different roles.
   • Workshops and Seminars: Hold regular workshops to educate employees on ethical considerations, data governance, and the practical applications of AI in their roles.
   • AI Champion Network: Develop an "AI Champions" program where early adopters with higher AI literacy share knowledge and provide mentorship within their teams.

2. Increase AI Adoption through Use Cases:
   • Pilot Programs in Administrative Tasks: Start by integrating AI tools to automate repetitive tasks, such as scheduling (e.g., Calendly), candidate sourcing, and summarising research data. This will showcase the efficiency benefits and free up time for more strategic tasks.
   • AI in Candidate Sourcing: Implement AI for improving candidate identification and broadening the pool by tapping into non-traditional networks. A focused pilot project could demonstrate its potential in enhancing diversity.
   • Case Studies and Success Stories: Highlight successful case studies of AI improving processes, such as faster candidate sourcing or better market intelligence. This will help reduce resistance to AI by showing real-world benefits.

3. Address Ethical and Data Privacy Concerns:
   • Develop Ethical AI Guidelines: Create clear ethical guidelines for AI use in leadership advisory, particularly focusing on fairness, bias reduction, and data privacy. Ensure that all team members are aware of these guidelines.
   • Transparency with Clients and Candidates: Develop a "Statement of AI Use" document for transparency with clients and candidates, explaining how AI will be used in the search process and how data will be handled securely.
   • AI Bias Monitoring: Implement AI tools that have bias-detection mechanisms and ensure diverse datasets are used for training models. This will help mitigate risks of bias in AI recommendations.

4. Client-Centric AI Implementation:
   • AI-Assisted Leadership Assessments: Work on integrating AI tools into the leadership assessment process in collaboration with human judgment, ensuring AI complements rather than replaces consultants' expertise. Use AI insights as an additional data point for assessments.
   • Enhance Client Experience: Use AI to streamline back-office tasks and improve response times, enhancing client satisfaction. Track improvements in client feedback after AI implementation in administrative workflows.
   • Client Education on AI: Proactively educate clients on AI’s benefits and limitations, managing expectations and building trust in AI’s role in their searches.

5. Monitor and Report AI Integration Progress:
   • Regular AI Usage Audits: Track AI tool usage across teams, measuring both frequency and outcomes. Use this data to continuously improve AI tool adoption and identify areas where AI has the most impact.
   • Feedback Loops: Collect feedback from employees on their comfort level and experiences with AI tools regularly. Address concerns as they arise to ensure smoother AI adoption.
   • KPIs for AI Success: Establish key performance indicators (KPIs) around time savings, quality of candidate matches, and efficiency gains to measure the success of AI implementation. Adjust strategies based on KPI results.

6. Build a Long-Term AI Strategy:
   • Strategic AI Roadmap: Create a long-term roadmap for AI adoption that outlines key milestones and growth areas, such as expanding AI’s role in candidate evaluations or exploring machine learning in predictive leadership assessments.
   • Phased Implementation: Adopt a phased approach, starting with low-risk areas like administrative automation before moving to more complex AI-driven processes like leadership assessments and client interactions.

By focusing on these areas, Saxton Bampfylde can build a strategic and responsible approach to AI integration that aligns with their values of human connection and ethical decision-making while embracing the transformative potential of AI technology.