Artificial Intelligence (AI) improves work methods and amplifies human potential. Key enterprise functions, be it operations, supply chain, HR, finance, or anything else, become more intelligent. But the caveat is using AI the right way. Using AI tools to add value requires much more than technical know-how. The million-dollar question facing enterprises is whether their tech leaders have the “AI IQ” to derive Maximum Benefit from AI?

The following traits underscore the ability of tech leaders to harness the power of AI to augment human capabilities. 

Ability to Learn and Unlearn

Mastering AI depends on the ability to learn and unlearn. 

In the ever-evolving field of technology, sustained success depends on lifelong learning. AI, in particular, is a developing field. New developments challenge existing paradigms almost every other day. Staying updated requires a continuous process of not learning and unlearning. 

Successful IT leaders keep up-to-date with the latest AI trends and technologies. They also unlearn outdated concepts and shed obsolete practices fast. They ingrain the process of learning and unlearning as a part of their daily routine and remain adaptable to embrace new knowledge.

The key conceptual acumen required are:

• Understanding the core principles of AI. Mastering AI requires knowledge of programming, neural networks, data science, and mathematics.
• Understanding how different algorithms work and their suitability for specific problems.
• Data-centric thinking, or thinking in terms of data when evaluating issues or problems.
• Understanding the potential biases, privacy concerns, and societal impacts of AI.
• Knowledge of how diverse AI tools and technologies operate to choose the right model for a given task.
• Communication and storytelling skills to convey the value of AI.

Ability to Visualise a Strategic Vision and Translate Such Vision into Action

Anyone with the right technical background and a sound mind can master the technical skills needed to develop and run AI models.

But developing practical AI models requires the foresight to visualise how AI can solve real-world problems. AI leaders need the ability to abstract complex problems into AI models. They must articulate a clear AI strategy and a roadmap to implement it.

The practical skills that matter for such ends include:

• Domain Expertise. For instance, a healthcare solution requires knowledge of medical terminology and industry regulations.
• The knack to identify the core challenges and opportunities and design AI models to address the same.
• Ability to align AI initiatives with organisational goals and measure the ROI of such initiatives.
• Ability to identify and assess the impact of the AI solution on business operations, revenue, and customer experience.

Consider a customer service chatbot. Designing and running a chatbot requires only technical expertise. It requires domain expertise to design chatbot interactions that align with the company’s brand. Likewise, developing a deep learning model to diagnose diseases is a straightforward task.  The value of AI for the enterprise comes with integrating such a model into the hospital’s workflow.

Risk Management Capabilities

Any new process comes with risks, and AI is no different. Many things can go wrong when implementing AI solutions in enterprises. For instance, inconsistent data can lead to inaccurate models. Integrating AI systems with existing enterprise systems may throw up unintended consequences. Resistance to change may subvert the project. Regulatory demands may throw up a spanner in the works. The list is endless.

Success depends on the ability to analyse the risks and rewards of the AI implementation. The AI leader needs competence and ability to:

• Understanding and prioritising risks based on their impact.
• Develop and implement strategies to reduce risks.
• Prepare contingency plans to address unforeseen challenges.
• Develop an ethics framework to guide AI usage and development.

Penchant for Innovation

The transformative potential of AI rests on innovation. Complex challenges often require unconventional solutions, which depend on innovation.

The mindset to innovate is just as important as the process. 

Tech leaders succeed in enabling AI-centric innovation with:

• The willingness to break free from conventional thinking and explore new possibilities. At the enterprise level, AI innovators need a safe space to explore new ideas without fear of failure. There will be little experimentation and risk-taking in a culture that holds people accountable for failure.
• Commitment to continuous improvement. Innovation success depends on sustained experimentation. AI leaders committed to innovation set up rapid prototyping with feedback loops to refine and improve solutions.
• Openness. Open collaboration and free information sharing encourage diverse perspectives and promote knowledge sharing. A culture of hoarding information for power creates silos and prevents the free flow of ideas. It dilutes the energy needed to focus on innovation.
• Ability to reconcile innovation with costs and business objectives. Innovation benefits only with a strong underlying commitment to a specific objective. Innovation for the sake of it, without regards to an objective, can wreck enterprise finances.

An Agile Mindset

Agility is critical to maintain a competitive edge in today’s fast-changing and fluid business environment. AI leaders need an agile mindset or a willingness to remain nimble and change fast to meet emerging challenges.

The agile mindset manifest as:

• Adaptability. Success with AI depends on navigating the paradoxes and tensions that AI introduces. Effective AI leadership means being at ease with uncertainty. AI forces humans to redefine decision-making. What worked in the past is no longer relevant. Rather, success depends on being ready to change, staying nimble, and leveraging AI as a supportive tool.
• Embracing change and uncertainty. Fixation with any process or system is foolhardy in today’s fast-shifting world. New technologies and breakthrough innovations can render incumbent paradigms obsolete overnight. Success depends on adaptability to changing requirements, technologies, and market conditions.
• Customer-centrality or prioritising user needs. The best AI products facilitate end-user needs, motivations and behaviours.

Agility goes hand in hand with flexibility. For instance, enterprises with monolithic ERP systems cannot move fast to leverage opportunities. They need to upgrade to the latest cloud-based ERP to ensure flexible systems.

Amplifying human potential requires a robust platform that enables intelligent automation and recommendations. One handy tool that facilitates the same is Workday. Unlike many applications that have AI added at a later stage, AI embeds into the Workday core application architecture. This offers unrivalled adaptability. Workday’s transparency on models and the use of customer data builds trust. The platform’s automation capabilities allow humans to focus on higher-value tasks. Yet a human-in-the-loop remains the final decision-maker. Explainability enhancements offer insight into the logic behind the recommendations. Users get empowered to assess the best course of action.