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What Transformational Business Leaders Need to Know About Artificial Intelligence

Ingrid Burton, CMO, H2O.ai, discusses the basics leaders need to know about AI in the workplace

The global interest around artificial intelligence (AI) is at an all-time high, and it is important to understand why. AI has proven capable of solving real business issues and addressing increasingly complex situations. Organisations and business leaders should start with the idea of how AI can help by identifying a business problem, or which use specific scenarios that it can help address, with the end goal of creating better business outcomes. Essentially, building an AI strategy that will yield results and ensure long-term success.

This article will examine the key pillars of an effective AI strategy and outline the challenges that it will need to address––talent, time and trust –– and the best practices to ensure AI success in the long-term, and what business and IT leaders need to know to achieve a successful AI Transformation across their organisation.

1: Talent: Data is a Team Sport

Because AI can drive better business results, there are fundamental aspects of an organisation that must be considered. Ultimately, the long-term success of an AI strategy will depend on the people and culture within the company.

Firstly, the current technical skills gap and lack of AI talent available must be addressed. Research from EY indicates that 56 percent of tech professionals involved with AI say that the lack of available talent is the biggest barrier to increasing AI adoption amongst businesses. As a role that is in extremely high demand, expert level data scientists are being quickly snapped up by tech companies and the internet giants as soon as they hit the job market, leaving many companies with a much smaller talent pool to hire from.

Simply put, there are not enough AI experts available in the workplace. More people need to pursue a career in data science and AI, and this can be accomplished by the implementation of STEM programmes at the primary, middle school and university levels. In addition, machine learning should be fast, accurate and available to everyone. By democratising AI, we can further advance our understanding and awareness of data and decision-making for all businesses. To achieve this, businesses should find a technology that can simplify machine learning and data science problems, even if an organisation doesn’t have a dedicated data science expert on its team.

However, when a company does have data scientists, they must understand that data is a team sport. Getting people with different skill sets to work together effectively, enabling teamwork across an organisation and working well as a team to make the data work for them is crucial to building a successful data-driven business. The “data team” consists of everyone from the functional business leaders to devops professionals and analysts, to data engineers and data scientists. Culturally, this team must be collaborative in order to be transformative. They must learn to work within the existing culture of a company, to bring a lasting positive change.

2: Time: Obtaining Results Faster

Using data is a great way to make informed decisions. But how do you glean insights from data that enables more efficient and effective decisions? Business leaders are generally overwhelmed with data from multiple areas of their organisation and need to address a range of use cases that are primed for AI, including how to attract new customers, make a credit-scoring decision, detect fraud or pinpoint the correct treatment for a certain patient.

Essentially what many businesses are trying to do is extract real insights from data. To make the best and most informed decisions requires not just data, but also time. AI can assist in making the right decisions, easier, cheaper and in record time. By building AI models, data science teams can visualise every scenario based on data that the company already has. Data can then be used to re-train the model in the future, allowing it to continuously learn and improve. IT and business leaders seek out solutions that can help speed time to insights and time to better results.

3: Trust: Explain the AI

Debatably, the biggest obstacle preventing AI success is trust. As organisations assemble strong data and AI teams, trust in the technology itself is one of the most important ingredients to the successful integration of AI into a company’s culture and business processes. For instance, how will people within an organisation trust an algorithm over the decades of existing human intuition and experience?

To overcome this challenge requires a more comprehensive explanation of AI to people within the organisation about what the technology is, how it will be utilised and how it will ultimately help people by allowing them to accomplish more, faster, in order to complete larger, more creative projects, and solve more critical and complex problems. The goal here is to have AI running in the background at all times – it’s the permanent Plan B. Plan A is still to use your manual tool base, namely the humans who work at the company. New technologies on the market today can address the explainability and interpretability of a model, so also factor that in when considering a solution.

4: Influencing Change via a Maker Culture

From an organisational and cultural perspective, it’s crucial to remember that AI is not simply a new fad, but it is the catalyst for a chain reaction in the direction of change. With AI, organisations can create a maker culture, where learning is best done through doing. AI is capable of instilling a product culture that continues its life cycle inside the businesses processes. As companies adopt AI strategies, they become makers and influence change.

It has been both fascinating and exciting to witness the development of AI over the last few years, as companies begin adopting AI strategies. It will only get more interesting and rewarding for businesses moving forward, as they continue on their AI Transformation journey.