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Most enterprises fail to implement AI beyond the pilot stage or a singular business process, and don’t leverage it to its full potential. The implementation process is riddled with long, hassle-filled decision-making processes, redundancies, poor KPI evaluation, and the inability to find suitable business use cases.

The best way to future-proof your organisation, making it resilient and agile, is dependent on how well you implement AI across various business processes and departments.

AI is a business investment

After talking to many leaders in the ecosystem, it has become clear to me that most organisations that fail to adopt AI efficiently treat it exclusively like an IT process and not a business process. When it comes to a massive investment like AI, the quicker the decision-making is, the more direct impact can be created immediately by prioritising the right use cases.

The best way to get decisions made quickly is to invest in AI from the business budget, not from the IT budget. The only time you involve the IT team or the cloud team is during the implementation process.

Investing in AI from the business budget means that executives will have more leverage to emphasise quicker decision-making and quickly integrate AI into business processes without too many hurdles.

The C-suite executives taking ownership of the AI systems across the enterprise is the best way to speed up processes and remove conflict that arises due to new technology adoption.

AI should always be a business investment, not an IT investment.

This will also require business and operational employees of the organisation to work alongside the technology team and focus on implementing AI to solve business use cases, and drive organisational priorities and changes.

Remove redundancies

There is a lot of emphasis in the ecosystem on ensuring that good data is used for AI processes. This has led to excess time being spent on engineering these data pipelines. Leaders have stopped over-engineering the data pipelines. They have started with whatever data is available by using it to their advantage against competitors. On the other side, they have also opened new data avenues as a secondary task.

I suggest organisations look for scalable solutions that ultimately reduce redundancies on data scientists over time. AutoML can automate the entire data journey – from preparation to updating models.

Expensive human resources are wasted very easily while implementing AI solutions. This manpower can be better spent on exploring new use-cases and not updating the existing ones to keep up with the efficacy.

Evaluation of AI as a business process

When it comes to selecting AI solutions, I recommend starting with use cases that directly impact revenue and reduce costs. This is why I suggest not to look for recommendation engines but explore systems that can guarantee decisions, sustainability, dependency and consistency. This will make scaling up more accessible and directly impact the company’s bottom line.

When measuring KPIs, companies make a common mistake to measure only accuracy KPIs viz f1-Score, Precision, Recall etc, but these are not useful to measure AI scalability and business performance. Measure AI performance like how you would measure a business process.

The best way is to bridge the gap between KPIs like accuracy and process efficiency, revenue hit, etc. This will ensure that the focus is kept on evaluating how AI solutions are driving organisational growth and scaling up business operations.

Build vs buy

Being in the AI ecosystem and after talking to many organisational leaders, I’ve realised the importance of right implementation.

There are plenty of solutions in the market for most business use cases. However, if they are not implemented well, any chances of AI driving organisational growth and resilience in the future are minimal. Most organisations are still looking for the right solution when they should be changing their implementation strategy. Along with implementation, start preparing a roadmap for automation of redundant business processes.

When you’re faced with the question of build vs buy, always go for buy because it’ll be faster than having an army of data scientists working towards implementing AI for a single business use-case. The time and resources should be spent on implementing the already existing solutions and exploring new use cases.