In today’s world, where data and AI drive the future of business, recognizing the essential role of human skills in successfully deploying and integrating these technologies is crucial. Digital transformation is not just a technological journey; it requires a comprehensive organizational shift. This includes a balanced blend of technical progress, strategic foresight, and empowered employees.
Despite the prominence and potential of AI, many organizations’ efforts are falling short. After discussing with hundreds of executives, we found that only a small minority of organizations effectively adopt AI practices to reap its benefits. Most companies still run ad hoc AI pilots, apply AI in a single business process, or focus solely on financial reporting. Recently, the adoption of AI has advanced in many companies due to generative AI (genAI) and Copilot functionalities in existing software, especially Microsoft Copilot. However, as almost every organization does the same, will this lead to a competitive advantage? Probably not, as no tool or technical feature alone is the elusive silver bullet. More innovation and novel thinking are needed to harness AI’s great potential truly.
Why the Slow Progress?
The slow progress boils down to the inability to rewire the entire organization. AI is transforming business operations, but many organizations fall behind because they don’t understand the skills needed to leverage data and AI effectively. This points to the critical need for companies to actively rethink the skills and composition of their current and future workforce.
Skills of an AI-Enabled Organization
The widespread nature of AI means it is not just the responsibility of the data or IT department but of the entire organization. Therefore, new and improved skills are needed across the organization to contribute to its AI journey. These skills vary based on roles, functions, and organizational hierarchy.
Skills of the Leaders
Any transformative journey requires leaders to create a clear and compelling vision of how the organization uses data and AI to secure a competitive edge. Leaders must understand the technical aspects of data and AI and foresee market trends, envision potential technology applications, and anticipate the broader impact on their industry. Setting clear objectives, creating a strategic plan, and communicating it throughout the organization is essential.
In addition to boosting their skills, leaders must foster an environment where data-driven decision-making becomes the norm and continuous learning and adaptation are embedded in the company’s DNA. This usually calls for a wider skill development push across the organization to equip everyone with adequate knowledge about data and AI.
Skills of Business Domain Experts
For many organizations, the majority of personnel represent white-collar experts whose core work is knowledge work. Their work is already changing profoundly with increased adoption of AI. In areas like financial services and legal, manual research is becoming obsolete. Collaboration between SMEs and AI-native applications streamlines tasks, creating efficiencies previously unimaginable.
However, AI applications do not appear magically. Beyond traditional business acumen, there is a growing need for business experts to ideate data and AI-based solutions that address both existing and emerging business challenges and opportunities. Sometimes these solutions can be purchased from vendors as turnkey solutions, but more often, there is a need to create something bespoke for the organization, developed either in-house or supported by external vendors. A common vocabulary between business and technical data and AI experts is needed to secure fruitful conversations and collaborations. Innovation frameworks like data thinking can also provide significant value.
Future business SMEs need to be data literate and AI literate. Data literacy means the ability to read, write, and communicate data in context, including understanding data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application, and resulting value. AI literacy builds on data literacy but has a broader perspective; it is about understanding how different AI technologies can be leveraged to drive efficiency, innovation, and strategic advantage. It involves understanding how AI can be used in all areas of the company, enhancing current processes, and even revamping them completely. It is the ability to understand the possibilities and prerequisites of leveraging such capabilities and the value/effort analysis of new applications. Part of AI literacy is also the ability to leverage corporate AI solutions like Microsoft Copilot to their maximum benefit while being vigilant about emerging solutions supporting individual business domains.
Dedicated Technical Skills for Data and AI
The technical backbone of any data and AI initiative requires a diverse set of skills, including predictive/prescriptive analytics, generative AI, data engineering, and software development. Moreover, integrating AI into existing IT infrastructure necessitates skills in cloud computing, cybersecurity, and DevOps. Developing or acquiring these specialized skills is crucial for creating, deploying, and maintaining AI systems. Additionally, traditional descriptive analytics and knowledgeable system architects are still needed for efficiency and scalability.
Data Governance and Management Skills
As data becomes increasingly central to business strategies, with and without AI, the importance of robust data governance and management cannot be overstated. Skills in establishing clear data policies, ensuring data quality, securing data, and managing the data lifecycle are paramount. This involves understanding legal and regulatory requirements, especially in the context of GDPR in Europe, and implementing frameworks that ensure data is accurate, accessible, understandable, and used responsibly. Organizations must cultivate expertise in how data is collected, stored, accessed, and deleted, ensuring transparency and accountability in its use.
Dedicated Business Data & Analytics/AI Roles
Dedicated business data and analytics/AI roles will be essential in maximizing the impact of data throughout the organization. These roles could include business data analyst, data steward, and data and AI strategist, as well as roles that can be adopted on a need-basis (not requiring a full-time equivalent allocation) like data owner and business use case owner. A single data and AI team, no matter how big and skilled, will never alone harness the true value potential of data and AI. Balancing work tasks, coordination, and ensuring the harmonious interaction between data and AI teams and various business units will be key in transforming the organization toward its data and AI vision.
Balancing In-House Talent Acquisition, Development, and Smart Outsourcing
Deciding between sourcing and/or upskilling internal employees for data and AI capabilities versus outsourcing requires a strategic approach. Core competencies, particularly those that offer a competitive advantage or are critical to the company’s mission, should be developed internally or through new talent acquisition. These include strategic positions to ensure and develop AI ideation for business innovation, data governance, and key technical positions where proprietary knowledge is crucial. On the other hand, for highly specialized or project-specific tasks, outsourcing or partnering with external experts can provide flexibility, access to world-class expertise, and speed. Furthermore, the current pace of technical development suggests that companies should not over-invest in new headcount, as the outlook for the future keeps changing rapidly.
However, it’s essential to invest in and maintain core in-house data and AI talent capable of managing and integrating external resources effectively, ensuring alignment with business goals, and maintaining quality and security standards. Additionally, knowledge retention and information dissemination should be kept in mind when considering outsourcing.
Talent Development and Lifelong Learning
It is unrealistic to suddenly swap current company employees for new talent that already possesses all the needed skills or replace those skills with external experts. Hence, there is a significant push to invest in upskilling the current workforce into the new role requirements and ensure that the organization as a whole can harness the potential of new data and AI capabilities. As data and AI are ubiquitous by nature and eventually impacting all roles, companies need to educate everyone, from top leaders to operational levels. Not all learning programs are, nor should be, the same for everyone but should be tailored to the roles and responsibilities of the most important learning groups. To cater to the varied needs of individual learners, many companies are launching internal AI academies that offer various learning journeys. Most companies initially hire external support to write the academy curriculum and deliver training, but it is also a great investment to build in-house capabilities and processes for this aim.
Data and AI training are great springboards, but knowledge transfer is only one minor element of the learning journey. When planning for upskilling initiatives, companies should always keep in mind that training should lead to new behavior that can be applied in everyday work after the training has ended, supports business goals, and is measurable and tangible.
Lastly, upskilling initiatives usually do not fly very high or far without explicit commitment and sponsorship from the leadership team. Ideally, they are the first ones to participate in training and hence lead by example. They should also communicate the importance of continuous learning and skill development. The current rapid change in the business and technological environment necessitates a culture of curiosity and the hunger to continuously refresh knowledge. By empowering the workforce with the necessary tools, knowledge, and attitude, companies can build a resilient, adaptable, and future-ready team.
Practical Steps in Maximizing the Human Potential of Your Organization
- Skill Development Programs: Implement tailored role/function-based learning programs (e.g., via a company data and AI academy), leveraging both internal knowledge sharing and external learning content, certifications, and courses to enhance the AI and data literacy of the whole organization.
- Strategic Partnerships: Forge partnerships with technology providers, consultancy firms, and academic institutions to stay updated on the latest developments in data and AI, and to fill in the knowledge and skill gaps when necessary.
- Talent Acquisition and Retention: Prioritize hiring for critical roles that align with the organization’s long-term data and AI strategy, and focus on retaining talent through engaging work environments, career development opportunities, and competitive compensation.
- Agile Workforce Planning: Adopt an agile approach to workforce planning, allowing for the dynamic allocation of resources between in-house teams and outsourced partners based on project needs and strategic priorities.
Conclusion
The journey toward data and AI transformation is as much about cultivating and leveraging human skills as it is about acquiring and deploying new technologies. By elevating the data and AI capabilities of the workforce through holistic skill development and strategically balancing in-house talent with outsourced expertise, companies can navigate the complexities of this transformation more effectively. This balanced approach ensures technological proficiency and fosters the agility, innovation, and resilience required to thrive in the digital age.