Artificial intelligence and machine learning have long been acknowledged by business leaders as a means to leverage business growth, from valuable customer insights to operational efficiency.
Yet it can be difficult to scale, and, despite cutting-edge research, data scientists are still struggling to fully utilise AI and ML. With this in mind, we have put together some tips that will help organisations better support their data and technology teams to understand and achieve business value from AI and ML.
Integrate previously siloed teams
To begin with, businesses need to break down any unnecessary barriers between data science, business and IT teams. A lack of transparency and communication will only hinder progress and – if you want the organisation to understand AI and ML and its benefits – it’s best to have all teams adding to the same conversation. Without this, the developments and initiatives made in one team could be overlooked by another. The entire business needs to show support and buy-in of AI, and understand its strategic value.
Without transparency and company buy-in, an organisation risks falling short and exacerbating problems. By integrating siloed teams and prioritising AI across all levels of the business, poor data management is averted and organisations do not risk having to make snap decisions based on the fast moving nature of the sector, without properly understanding where the business stands.
Choose the right tool
With multiple types of AI development tools on the market to choose from, some businesses will be keen to invest in them, but might struggle to increase the adoption rate among their data teams. After all, data scientists need more than just automation from their AI tools. They also need transparency and model code to boost their productivity and creativity, which most AI tools do not provide.
Organisations should instead look to AI optimisation platforms to build optimal models at scale. They can be used to support a company’s AI capabilities to address complex modelling problems with full transparency and flexibility as well as model code, while using the same architecture or software that data scientists are already familiar with. The benefits are clear, with these tools able to create better and faster algorithms, that can optimise processes more efficiently and effectively.
AI optimisation does call for a strategic rethink, but ultimately it allows for a more efficient way for data scientists to augment and deploy AI models across the business, optimising their time and leading to a better return on investment.
At TurinTech, we enable businesses to build AI models at scale, offering executives the ability to truly understand,support their data scientists and demonstrate value behind AI. On top of this data scientists are able to embrace new areas of code optimisation that can help make models perform more accurately and efficiently.
To learn more read the full article in AI Business here.
Author: Dr. Leslie Kanthan, CEO, TurinTech