From data-driven consumer insights to financial analytics, artificial intelligence and machine learning augmentation are often viewed as a strategic assets to making better decisions across organizations.

So much so, that over 84% of C-suite executives believe they must leverage AI as a primary means of meeting their growth objectives.

While organizations acknowledge the positive end-goal, many can find it difficult to scale it across the business and projects can be left at a standstill or even fail to go beyond the brainstorming process. What’s more, despite developments in cutting-edge research and open-source libraries, data scientists aren't utilizing the necessary tools to harness the potential machine learning can bring.

As 75% of executives admit they risk going out of business in five years if they don't scale AI, the pressures to get it right and empower the organization are higher than ever before. But how can organizations better support data scientists to achieve business value from AI?

Break down unnecessary barriers

Unsurprisingly, poor preparation, lack of transparency and the misuse of data can negatively impact organizational objectives and can hold back the augmentation process.

If organizations want to see an effective change in the new year, they will need to break down silos between the data science, business, and IT teams to unify AI initiatives and objectives. In addition, organizations also need to adapt, have collective buy-in support from all levels, and embrace AI as a strategic component to achieving the last stages of AI maturity.

The alternative is that poor data management, the lack of transparency, and the siloed approaches to work present in different industries could actually exacerbate problems even further. As demonstrated In the financial sector, overlapping work and poor data mapping can impact trades that can do more to hinder business payback and ROI.

Not only that, but with the fast-moving nature of the sector, compliance factors are always changing, leading institutions to make choices between model performance and regulations. In the new year, organizations can't afford to poorly utilize these optimization tools, and data scientists can be supported to do what they do best by providing insights.

Better tools to augment AI efforts

With differing architectures, workflow options, and additional software available for organizations to invest in, it’s also easy to assume results can be generated by adopting transformation initiatives. Although there is truth to this line of thinking, a lot of time can be spent deploying and augmenting AI/ML models that often involve further trade-offs such as accuracy and efficiency in architectures.

Providing an easy solution or automating can be difficult but data scientists can use AI optimization to augment an organization's AI capabilities and address complex modeling problems. Among the many benefits, AI tools can develop algorithms faster, optimize processes more efficiently and effectively enhance model performance.

Without it, more time and money can be spent on projects that do not demonstrate value, rely on manual processes, and can’t provide usable data. If a data scientist can't find and utilize data,  businesses can't outline where the talent lies or what areas can generate value and it will be even harder to demonstrate if there is progress in an organization.

Learn from past mistakes

As competitors take advantage of the fast-paced business environment, organizations will need to ensure they're not taking labored and unstructured approaches to building and deploying AI.

Although much of this calls for a strategic rethinking, it also requires a better way for data scientists to augment and deploy AI/ML models internally to optimize their time and ensure a positive return on investment. Fortunately, with AI optimization platforms like TurinTech that can build AI models at scale, there is an opportunity for executives to support the enhancement of the model selection, model tuning and provide a better explanation of the end-to-end process.

Additionally, data scientists should embrace new areas of code optimization that can further enhance the performance of models to get more accurate and efficient AI into production. Once businesses ingest data effectively and launch models at scale, they can also optimize models in production to run at peak performance without intensive engineering efforts over time.

Why wait?

It's hard not to see the benefits that AI and ML can provide but it requires strategic thinking, and data scientists will need to leverage the best technologies to provide faster data-to-value projects. The organizations that will empower them to do this will gain a considerable advantage, and there is no better time to get the ball rolling than this year.

Dr. Leslie Kanthan is CEO and co-founder of TurinTech, an AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle.

Dr. Kanthan is an expert in graph theory, quantitative research, and efficient similarity search techniques. He previously worked for Credit Suisse, Bank of America, and Commerzbank.