The fundamentals of implementing artificial intelligence

implementing artificial intelligence

Despite the temptation (or pressure) in embracing and implementing artificial intelligence (AI) in a business, decision-makers should not lose sight of the fundamentals when it comes to any technology implementation.

As with any business initiative, AI programmes must start with deciding on which organisational goals need to be accomplished. Once completed, the company must determine the roles that need to be put in place to reach these objectives. And finally, people must be identified who are best suited to assist the business in effectively implementing artificial intelligence. Fundamentally, this can be condensed down to goals, roles, and souls.

Of course, there needs to be executive sponsorship of the AI project, someone championing it from the top. This helps ensure that effective budget allocation will take place and the project can be done in accordance to the overarching business strategy.

Feasibility

Part of this will entail conducting a feasibility study. During this, companies should consider identifying at least five questions that examine the likely impact the project will have on transformational pillars such as customer experience, operational optimisation, employee empowerment and product transformation. Only if the AI implementation merits investment should the company go ahead. Over time, a methodology can be developed to enable the business to identify the next priorities as the initiative progresses.

However, one of the biggest challenges is how to transform the answers to these questions into a production scenario. While it is relatively straightforward to find insights that could change the business, turning them into an AI-powered process with the operational key performance indicators to support it, is not without its difficulties.

It is therefore vital to incorporate a process that factors in organisational change management. Not surprisingly, AI can also assist in this process – it can gather data on people’s reaction to the initiative, develop models to identify the champions (or opponents) of change, and bring that insight back into the decision-making loop.

Current investment

As with any technology investment, discussions will likely revolve around whether the organisation should build, outsource, or buy new solutions. The benefits and risks are all related to the in-house capability and capacity of the company when it comes to development, as well as its profile when it comes to timing and purchasing.

As with many other IT subject areas, there is a massive shortage of skills (at both developer and leadership levels) when it comes to AI projects. Even though this complicates the decision-making process, it does create opportunities for new investments.

This could take the form of outsourcing or buying small solutions that have a quick return on investment to help evaluate whether AI can deliver demonstrable business returns.

None of this can happen without an integrated approach. And the foundation behind this is having the teams in place, with well-defined roles and coordination taking place, to drive the AI implementation.

Best practice

There are a number of companies locally that are using AI to transform their own as well as their client’s processes.

For example, financial services providers are using AI models in conjunction with vehicle telematics to monitor driver behaviour. In turn, this is combined with a reward programme to incentivise good driving. This places a lot of emphasis on safety to reduce the number of vehicle-related incidents. Altron Netstar, as a telematics provider, has a driver behaviour alert solution that could work with any vehicle-related requirement.

Altron Karabina is also currently working with an organisation that uses AI to enhance call centre agents’ capability to cope with client requests. Their virtual assistant bots are improving first-call resolution and net promoter scores by offering the right level of contextual help to agents, with models that learn from historical call data, not by building complex rules engines manually.

It is difficult to imagine a time when people did not have access to services such as the likes of AirBnB and Uber. The next step of the AI disruption will impact on everything from banking, insurance, and traditional retail, and will generally benefit employees and customers alike.

This article was originally published in ITWeb.

Implementing artificial intelligence should not be a difficult process for your business, get in touch with us and let our experts assist your business on this journey.

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