Insights from Motasem El Bawab
At the heart of any business strategy, the organization’s ability to extract and analyze its consumer data is paramount for establishing strong customer relationships. This starts with understanding the internal processes that inform the customer-facing products and services the business should be investing in, and the technological solutions it chooses to help the workforce meet its objectives.
One of the roadblocks to achieving this level of operational efficacy is data disparity – whereby the employee’s ability to do their job is hindered in some way by the quality of resources and information available to them. Increasing the level of data quality that a company owns is therefore a bold and necessary step towards improving both the employee (EX) and customer experiences (CX) in tandem.
Artificial intelligence (AI) plays various roles in helping an organization boost the CX. According to a Forbes Advisor survey – covering multiple industries – close to three in four businesses (73 percent) currently use or plan to use AI-powered chatbots, whereas three in five (61 percent) leverage AI for email campaigns, for example, and more than half (55 percent) to personalize their service offering.
While instant messaging and other automated communications methods are popular tools for businesses to adopt, there are several other ways machine learning tools can improve operational processes, too, by making data analysis more accessible to the workforce, leveraging functions such as data dashboards and AI-powered personal assistants.
These functions adopt a “garbage in, garbage out” approach, says Motasem El Bawab, N3XT Sports Chief Information Officer (CIO) – designed to simplify the organization’s data-collection capability by removing redundant data sets. “With human-like prompts,” he says, “you can ask the data any question you’d like and the AI will be able to deliver you the relevant insights”.
“Our team at N3XT Sports is currently working on the mission of making data more accessible and understandable to sports properties,” Mota continues. “Now that OpenAI and machine learning models are becoming mainstream, they no longer require a heavy computational workload. In years gone by, organizations required a lot of processing power to be able to implement machine learning models into their digital framework – whereas, with the different cloud solutions available to sports properties today, it’s becoming much easier to use them.
“For example, you can apply AI to analyze your database, manage and optimize data storage, and to single out a process that you require amongst hundreds of thousands of data points. In addition, it also gives you greater visibility of the database itself. For instance, if you build an application for the database, you don’t necessarily need to be a coder or understand structured query language (SQL) – with the use of AI, employees can assess the data themselves, at a granular level, by using the right prompts. So it no longer becomes about querying the data through code, but asking the right questions.”
‘BASIC LEVEL OF DIGITAL MATURITY NECESSARY FOR AI IMPLEMENTATION’
Such is the quality of the machine learning software available on today’s market, in principle, AI-powered solutions can be applied to any computational system inside an organization and adapted to serve departments at every level of the company’s wider operation. As sports properties become more digitally independent and better equipped to manage first-party data, their existing customer-data service providers are adapting, too – providing built-in AI functions that can be tailored to their client’s needs.
For example, the US technology giant Microsoft has a long-term partnership with OpenAI around the research and development (R&D) of advanced AI technologies ready to go to market. This includes the new AI-powered experiences available via Microsoft’s Azure OpenAI Service, which helps developers to build AI-optimized infrastructure and tools. As OpenAI’s cloud provider, Azure also powers all OpenAI workloads including the creation of API services.
“At the API level, these models can be integrated into any system, internal or external,” Mota expands. “If we take a CRM system, for example, you can use AI to gain better analytics, optimize marketing campaigns, or tailor your editorial output. On the client side, whereas initially AI was predominantly used for chatbots, you can now apply it to much more, including recommendation engines and to personalize the UX.”
Machine learning capabilities can have a dramatic effect on productivity levels and customer satisfaction if deployed appropriately, Mota suggests. Nevertheless, in order for AI to maximize the return on an organization’s CRM or technological investments, sport’s entities must first assess their digital maturity to discover where AI can prove most effective or otherwise implement a data strategy if they do not yet own one.
For example, Salesforce launched Einstein GPT – billed as the world’s first generative AI tool for customer relationship management (CRM) – and can be applied to multiple areas of a business, including for sales, service delivery, content marketing, product recommendation, data analytics and data management, and fast-track automation to improve the UX.
“The principle of AI is to enhance the UX – and, in the case of sport’s digital transformation, simplify dataflows,” Mota says. “Therefore, your level of digital maturity needs to be at a level where you have some sort of data-collection strategy via the organization’s digital touchpoints. This doesn’t have to be connected by a single sign-on (SSO), but your data needs to be accessible somehow, either via API, or queries. This would obviously make the adoption much easier.
“For example, if you are having issues with measuring the necessities and utility levels of technologies, AI can help you with certain things – but not everything. You will likely need to start by solving this before integrating AI because it can add more strain to your systems. So, while we are still in the early stages of sport’s use of AI, there is a level of maturity that’s required from a technology viewpoint, as well as resourcing, for AI to be an effective ally.”
WHAT’S N3XT?
There are several cases where AI-based solutions are serving the CX – though fewer for enhancing operational workflow. The Spanish football league organizing body LaLiga is working with Sportboost, to develop a program called Meet LaLiga which is aimed at discovering startups that are working on AI-power software for optimizing business processes.
As part of this initiative, LaLiga aims to pilot machine learning technologies that “bring a differential and disruptive concept, as well as different forms of collaboration”. Speaking on the subject of AI and its place in the modern sports business, Gonzalo Zarza, the Chief Data Officer for LaLiga Tech, the league’s technology subsidiary, outlines a need “to start thinking about AI as a powerful tool to fulfill problem needs”. Given the challenges the sports industry still faces around technology integration and change management, Mota adds that “it is worth thinking of AI as your assistant that can be used to carry out multiple activities without creating more workload or disrupting the organization’s workflows”.
Our team at N3XT Sports works tirelessly to develop and implement data and digital transformation strategies across a multitude of sports properties at federation level, competition level, and club level. To learn more about how to implement AI-powered tools into your organization, fill out the form below and we’ll be in touch. Our goal is to drive the digitalization of the sports industry and our clients.