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Why you should think of AI as a team sport

Akkasha Sultan
Akkasha Sultan1/3/2023 08:01 PM GMT+08  • 5 min read
Why you should think of AI as a team sport
Instead of throwing every business challenge at your data science team, democratising AI can add speed and power to decision-making across your organisation. Photo: Unsplash
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We are seeing artificial intelligence (AI) projects shift from hype to impact, mainly because the right roles are getting involved to provide the business context previously missing. But domain expertise is key — machines do not have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced.

According to the International Data Corp, AI spending in the region is expected to reach US$46.6 billion ($62.3 billion) by 2026, with top use cases that include augmented customer service agents, smart business innovation and automation, and enhanced sales processes.

Business leaders in the region understand the benefits of AI. But when scaling AI, many leaders think they have a people problem — specifically, not enough data scientists. Still, not every business problem is a data science problem. Or at least, not every business challenge should be thrown at your data science team. With the right approach, you can reap the benefits of AI without the challenges that come with traditional data science cycles.

To deploy and scale AI solutions, leaders need to shift their organisation’s mindset to think of AI as a team sport. Some AI projects need a different set of people, tools and expectations for what success looks like. Learning to recognise these opportunities will make your AI projects more successful and deepen your bench of AI users, adding speed and power to decision-making across the workforce. Let us explore why and how.

Organisations can lean on AI to democratise analytics

Using AI to solve business problems has largely been the responsibility of data scientists. Data science teams are often reserved for an organisation’s biggest opportunities and complex challenges.

See also: A local spin on voice AI

Many organisations have successfully applied data science to specific use cases like fraud detection, personalisation, and more, where deep technical expertise and finely-tuned models drive hugely successful outcomes.

However, scaling AI solutions through your data science team is challenging for many reasons. Attracting and retaining talent is expensive and can be problematic in our tight labour market. Traditional data science projects can often take time to develop and deploy before the business sees value.

Even the most experienced, robust data science teams can fail if they lack the necessary data or context to understand the nuances of the problem they are asked to solve.

See also: Generative AI: Friend or foe?

Domain expertise essential to scaling AI across the business

AI is already bringing advanced analysis capabilities to users with more data science backgrounds. Machines can select from the best forecasting models and algorithms, and underlying models can be exposed, offering the ability to fine-tune them and make sure that everything matches what the user is looking for.

These capabilities give analysts and skilled business domain experts the ability to design and leverage their AI applications. Being closer to the data, these users have an advantage over many of their data scientist counterparts.

Putting this power in the hands of domain experts can accelerate development times, alleviate resource burdens, and eliminate hidden costs associated with traditional data science cycles. Plus, domain experts are ultimately best placed to decide whether an AI prediction or suggestion is helpful.

With more iterative, revise-and-redeploy model-building processes, business users can get value from AI faster — even deploying new models to thousands of users within days to weeks instead of weeks to months. This is especially powerful for teams with unique challenges that can benefit from the speed and thoroughness of AI analysis but may not be a high priority for data science teams.

It is important to note that while these solutions can help address the skills gap between analysts and data scientists, they are not a replacement for the latter. Data scientists remain critical partners alongside business experts to validate the data used in AI-enabled solutions. In addition to this collaboration, education and data skills will be critical in successfully scaling these solutions and tools.

Data literacy empowers more people to tap into the power of AI

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An organisation’s foundational data strategy plays a huge role in setting up its success with AI, but bringing AI solutions to more people across the business will require a baseline of data literacy.

Understanding what data is appropriate to apply to a business problem and how to interpret the data and results of an AI recommendation will convince people to trust and adopt AI as part of their decision-making. A shared language of data within the organisation also opens more doors for successful collaboration with experts.

Leaders can take a variety of approaches to build data literacy — from education and training to mentorship programmes to community-building data contests. Normalise the access and sharing of data and how you celebrate and promote successes, learnings, and decision-making with data.

Continuing to build your organisation’s data culture creates powerful opportunities to nurture skills and foster new solutions across the business. Many organisations have increased their investments in data and analytics in recent years as digital transformation has accelerated. It is not a reach to think of data as a team sport, and now we have the means to extend that mindset to AI too.

Akkasha Sultan is the country manager for Southeast Asia of Tableau at Salesforce

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