In today’s rapidly evolving Data, Analytics & AI market, organizations face a number of challenges that demand creative solutions. Recently, I hosted a gathering for Data & AI leaders and experts to provide insights into some of the biggest challenges facing our market.

This article highlights our key findings from our presentations, Q&A and high-level discussions and provides actionable insights and recommendations to address.

Preparing for Five Generations in the Workforce by Ryan Parish

As we look ahead to 2030, the workforce will span five generations, each with unique needs and characteristics. Addressing health and wellbeing is paramount to retain talent across this diverse demographic.

The Cost of Ill Health

In 2022/3, the Health and Safety Executive (HSE) estimated the annual cost of short-term ill health and workplace injury at £20.7 billion. Additionally, 35.2 million working days were lost due to ill health, with 1.8 million people suffering from work-related illnesses, primarily stress, depression, anxiety, and musculoskeletal disorders.

The economic impact of mental ill health in England in 2022 was a staggering £300 billion, double the NHS England's budget for that year.

Generational Characteristics

Generation A (<18): Hyper-connected, visually motivated, independent, with short attention spans and poorer mental health.

Generation Z (18-34): Entrepreneurial, career-focused, pushing for workplace change, and motivated by social issues.

Millennials (34-50): Digitally native, value teamwork and consensus, need meaningful motivation.

Generation X (50-65): Technologically comfortable, value work-life balance, direct yet open to feedback.

Baby Boomers (65+): Self-sufficient, value workplace visibility, prefer full-time over part-time.

Mental, Physical, Financial, Environmental, and Social Wellbeing

Businesses must enhance their support systems to retain staff and reduce sickness absence. Mental health is a significant concern, with 49% of ill health cases in 2022 due to stress, anxiety, or depression.

Physical wellbeing is also critical, as presenteeism remains a challenge, with 46% of workers admitting to working while unwell.

Financial wellbeing is under pressure, as evidenced by the CIPD 2023 UK Working Lives survey, where only 48% of respondents reported meeting their bills without difficulty.

Moreover, hybrid work environments and robust ESG policies are essential to attract and retain talent, as more than a third of UK workers might quit if forced back to full-time office work, and 52% would stay longer with a company committed to ESG values.

Goodbye Hybrid? The Lifestyle-to-Work Approach

The hybrid model is now the minimum expectation for many employees. The Candidate Experience Report by Greenhouse indicates that 40% of candidates would not apply for a job without their preferred working model, and 77% would consider leaving if flexible work policies were reversed.

Future workforce expectations will include flexibility in work style, supported by comprehensive benefits packages. Employers need to innovate continuously to stay competitive and attract top talent.

Data & BI Leaders: Navigating a Rapidly Growing Sector

The data analytics sector is projected to grow exponentially, reaching $778 billion by 2030. To attract and retain top talent, leaders must offer more than competitive salaries:

Current Offerings: Consider low-cost healthcare options, hybrid working models, and a focus on work produced rather than hours worked.

Next Steps: Add flexibility, develop genuine ESG policies, and review wellbeing benefits.

Future Plans: Prepare for a global talent pool, support digital nomads, and adapt to the 'lifestyle-to-work' approach.

Gen AI Use Case: Product Data Industrialisation by Carl Smith

The use case presented focuses on leveraging Generative AI (GenAI) to optimise product data, improve search engine visibility, and drive sales.

Key Points

Products that rank higher in search engine results sell more. This can be achieved through:

SEA (Search Engine Advertising): Paid ads to boost visibility.

SEO (Search Engine Optimisation): Enhancing web content to improve organic search rankings.

SEO Strategies

On-Page SEO: Optimise content quality, keywords, meta tags, URL structure, internal linking, mobile-friendliness, and page performance.

Off-Page SEO: Build high-quality backlinks, leverage social signals, and optimise for local SEO.

Technical SEO: Improve site architecture, mobile optimisation, SSL, speed optimisation, XML sitemaps, and robots.txt.

Product Description Optimisation

Length and Structure: Use concise sentences, bullet points, headings, and images.

Language: Highlight unique features and benefits with clear, meaningful writing.

SEO: Naturally incorporate high-frequency, low-competition keywords.

Customer Value: Emphasise how the product benefits customers with lifestyle writing.

Call to Action: Summarise benefits and include a clear action prompt.

Buyer Persona: Tailor descriptions to stimulate customer desires and reflect brand character.

Key Takeaways

Prompt Engineering: Better prompts lead to improved GenAI outputs.

Input Quality: Detailed input data yields better results.

GenAI as a Tool: Requires iteration and validation for optimal use.

SEO Optimisation: GenAI helps create high-quality SEO product descriptions.

Stepping Stone: Use GenAI outputs as a base for further refinement.

By adopting these strategies, businesses can enhance product visibility, improve search rankings, and increase sales through effective use of GenAI in SEO optimisation.

Securing Investment for Data & AI Initiatives by Faye Murray

Challenges and Barriers

Securing funding for data and AI projects remains a significant hurdle, with over 70% of executives citing it as a challenge. Many projects fail to move from pilot to production due to funding issues, perceived high costs, and lack of executive sponsorship.

Funding Difficulties: High costs and uncertain ROI hinder investment.

Economic Constraints: Competing priorities and financial limits reduce funding.

Perception Issues: AI projects are seen as risky investments.

Lack of Understanding: Limited awareness of AI benefits.

Executive Sponsorship: Difficult to secure necessary leadership support.

Strategies for Success

Demonstrate Value Quickly: Focus on quick wins with clear deliverables and quantifiable outcomes.
Use POCs: Allocate time for innovation and tie POCs to strategic objectives.
Creative Resourcing: Leverage internal and external resources cost-effectively.
Align with Business Objectives: Ensure projects are aligned with strategic & business goals.
Continuous Improvement: Regularly update and iterate strategies.
Internal Champions: Foster a data-literate culture and recruit internal advocates.

Business Case

Identify the Problem: Clearly define the issue.
Analyse the Cause: Determine the root problem.
Assess the Impact: Outline the consequences of inaction.
Propose the Solution: Describe the benefits of the solution.

Preparing for AI: AI-Ready Data and AI-First People by Christopher Knight

To leverage AI effectively, organisations need high-quality, timely, relevant, accurate, and consistent data. AI-ready leaders understand AI's strategic value and drive adoption, while AI-first teams and contributors integrate AI into their workflows and continuously learn and innovate.

By addressing these challenges and focusing on strategic, flexible, and innovative approaches, leaders in the data, analytics, and AI market can navigate the complexities of a multi-generational workforce and rapidly evolving technology landscape.

Key Actions for Becoming AI-Ready

Data Preparation

● Invest in data management tools and practices to ensure data quality.
● Regularly update and clean data to maintain its timeliness and accuracy.
● Standardise data formats and integrate data from various sources.

Skill Development

● Provide training and development programs to enhance AI skills among employees.
● Encourage continuous learning and staying updated with the latest AI trends and technologies.

Leadership and Culture

● Develop AI literacy among leadership to foster a supportive environment for AI initiatives.
● Promote a culture of innovation and experimentation with AI.

Strategic Alignment

● Align AI projects with business goals to demonstrate value and secure investment.
● Prioritise AI initiatives that offer clear, measurable benefits.

Collaboration and Communication

● Foster collaboration between AI specialists and other departments.
● Maintain open communication channels to keep stakeholders informed about AI projects and their progress.

AI-Ready Data

Ensuring your data is ready for AI is crucial for successful implementation. The key characteristics of AI-ready data include:

Timeliness: Data must be up-to-date to be relevant for AI applications. Old or outdated data can lead to inaccurate insights and decisions.

Accuracy: The data should be correct and free from errors. Inaccurate data can significantly impact the performance of AI models.

Consistency: Data should be uniform across different sources and systems. This means following standardised formats and ensuring data is harmonised.

Completeness: Data sets should be fully populated with minimal missing values. Incomplete data can lead to biased or incorrect AI model outputs.

Availability: Data should be easily accessible when needed for AI processing. Delays in accessing data can slow down AI initiatives.

Validity: Data values must fall within acceptable ranges and adhere to business rules. This ensures that the data used in AI models is sensible and meaningful.

AI-First People

Building an AI-ready workforce involves cultivating AI-first leaders, teams, and contributors. This human aspect is as important as having the right data.

AI-First Leader

● Understands the strategic value of AI.
● Integrates AI into the organisation's vision.
● Drives AI adoption across the organisation.
● Focuses on leveraging AI to enhance decision-making and innovation.

AI-First Team

● Comprised of members proficient in AI technologies.
● Adaptable to changes and new AI developments.
● Collaborates effectively to integrate AI into workflows and projects.

AI-First Contributor

● Actively utilises AI tools and techniques in their role.
● Continuously learns about AI advancements.
● Contributes to the organisation’s AI initiatives by applying AI to improve processes and outcomes.

Summary

In conclusion, navigating the landscape of data, AI, workforce diversity, and digital transformation demands proactive strategies and adaptive approaches.

4 key takeaways

Implement flexible work arrangements and policies

Flexible work policies that cater to the preferences of different generations, such as hybrid work models are vital. This adaptation can enhance employee satisfaction and productivity across the board.

Invest in comprehensive SEO strategies (both on-page and off-page)

The successful use of GenAI SEO strategies like optimised product visibility and sales performance on digital platforms are key to ultimately improving sales. This includes focusing on technical SEO aspects like site architecture and mobile optimisation.

Build a compelling business case for data and AI initiatives

By emphasising quick wins and low-hanging fruit for these business cases, you demonstrate immediate value and ROI. Engage internal stakeholders early and align projects with strategic objectives to secure necessary funding and resources.

Establish AI-ready data practices by ensuring data is timely, accurate, and accessible

Foster a culture of AI adoption among employees (AI-First People) by providing training and resources that promote proficiency and innovation in AI technologies.

About the author

Josh is a Senior Consultant with over 5 years of experience, based in our Birmingham offices. With a passion for recruitment, Josh has a wealth of knowledge within the technology space, providing clients with leading Data professionals of all levels.

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Data & BI Leaders Event 2024 – Key Findings