84% of the C-suite executives believe artificial intelligence (AI) to be a crucial factor for achieving their growth objectives. All the industry leaders believe AI to be an enabler in their strategic growth and that AI investments need to be scaled across the enterprise for a positive return. Yet 76% struggle in scaling AI across various business streams.
It’s All in the Numbers
There is a significant gap of US$110 million in ROI from AI investments between companies in the proof-of-concept stage of their AI journey and the strategic scalers. But what are the key measures of financial evaluation that are affected by successfully scaling AI? In their study on AI investments, Accenture found an average lift of 32% on the Enterprise Value/Revenue Ratio, Price/Sales Ratio, and Price/Earnings Ratio.
A deeper analysis revealed that an enterprise-wide inclusion of multi-disciplinary teams and purposeful AI with noise-free data acted as critical success factors. In fact, interdisciplinary teams drive AI efforts in 92% of the companies that employ a strategic approach to scaling AI and are successful in it.
How Does a Multi-disciplinary Data Science Team Ensure Success?
While a standalone proof-of-concept project has a siloed AI operation typically led by the IT department with CTO or CIO at the helm, strategic scalers focus on analytics and data-driven approach led by Chief AI, Data or Analytics Officer.
The multi-disciplinary or interdisciplinary teams are comprised of data scientists, AI/ML engineers, data modelers, visualization experts, and business experts, in addition to IT professionals, engineers, and other specialists. A diverse team ensures the relevance of the technical architecture of the initiatives with the company’s requirements and enables faster cultural and behavioral changes required to scale AI.
By embedding AI experts across the organization, it is possible to cultivate and enhance the understanding of AI and its everyday application among employees. This strategy further pushes AI adoption and scaling at the enterprise level.
How Can Business Leaders Empower Their Multi-disciplinary Data Science Teams?
Education, training, and firsthand experience are the key facilitators in building interdisciplinary teams. Having said that, employees with a technical and coding background have the edge over those with no-coding backgrounds.
For empowering interdisciplinary teams, the business executives must enable them with an environment that offers:
- Ability to use both visual workflows and low-code environment, with an intuitive visual interface that allows non-coders to contribute
- Integrated data ops for efficient data preparation
- Data visualization for data engineers to explore, visualize, analyze, report, and cleanse the data
- Model development and experimentation studio for data scientists to build, train, and evaluate the models before putting them in production
- Model deployment and monitoring for the tech team to monitor model performance along with scheduling and governance
- Collaboration studio for managing the projects and collaborating with the various stakeholders
The environment must be so-built to enrich and enable the team to effectively manage the entire end-to-end data science lifecycle without worrying about roles, privacy, security, and resource management.
Managing and analyzing data holds far more value than just optimizing business processes. Data analytics can, in some cases, predict the unpredictable. At the very least, data and insights can help organizations adapt rapidly to unprecedented situations.
Read this whitepaper to learn how to leverage a platform-based data science approach to accelerate digital transformation initiatives, improve decision-making capabilities, and seamlessly automate customers’ journeys.
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