Digital transformation increases pressure on data platforms
The ongoing pandemic has accelerated digitalization in all areas of our lives. It has pushed organizations over the technology tipping point and transformed business at unexpected speed. So how are organizations preparing to extract value from their investments - beyond the pandemic?
Insights from a recent interview study by SAS.
Between April and October 2021 SAS analytics experts carried out 90 semi-structured telephone interviews with their counterparts in partner organizations, to find out how their clients have adopted digital transformation more than a year into the COVID-19 pandemic. The interviews took place, and featured experts from the Americas, EMEA and APAC.
Participants of the study described the acceleration of the transformation process within client organizations. To stay competitive in this new business and economic environment they have adopted new strategies and practices and a high level of pragmatism. Many companies had seen that digitalization could be a strategic differentiator, encouraging adoption of technology to improve processes.
Consultants reported considerable interest in the potential for AI and advanced analytics. Many suggested that clients were setting up multidisciplinary teams or centers of excellence to explore AI or developing a data strategy based on data management and governance and IT–business alignment. Others described the development of a people–machine-based ecosystem, and a growing willingness to innovate and take some risks with the use of AI.
The need to drive transformation and innovation led many interviewees to highlight the importance of collaboration within multidisciplinary teams. An approach that may be simple in concept but can be a challenge in delivery. A data platform that energizes collaboration is key.
Conclusions
Pragmatic approaches to digitalization
The focus areas for most companies are staying ahead of business, speeding up digital projects, setting up hybrid and/or remote work, using collaborative tools, multiplying value through analytics, and automating business processes and operations.
Holistic strategy
The companies that have successfully achieved digital transformation and democratization of artificial intelligence (AI) have a holistic approach to their digital strategy. They aim to use analytics contributions effectively, are technologically and operationally mature and are breaking down organizational and data-based silos. In general, they consider AI as a means to an end, and a tool to achieve their objectives, rather than an end in itself.
Work in progress
The democratization of AI and advanced analytics is best described as "work in progress’. The companies that were furthest ahead were using AI and analytics to support operational decision-making and business processes. Those companies where democratization was not yet in place, or even in progress, were often using analytics, but were a long way from operational process automation. They tended to use AI in very narrow ways, rather than considering it more strategically, and focus on infrastructure or product features instead of the end-result.
Confidence in digital proficiency
The consultants we interviewed were generally positive about their own organization’s current level of digital proficiency and rated it as above average. They also expected the level to increase in the next two years and emphasized the importance of ongoing development.
Exploring the potential
of AI
Client teams were very interested in exploring the potential of AI, analytics, and automation. Strategies used for this purpose included establishing multidisciplinary teams and developing data management and governance strategies. Other approaches were moving towards better IT–business alignment and development of people–machine ecosystems.
Benefit from AI-driven transformation
Interviewees saw plenty of opportunities for clients to benefit from AI-driven transformation initiatives through scalability based on data quality and governance, speed, a holistic AI strategy, solution optimization using multidisciplinary teams, and trust in technology when making collective decisions.
Potential problems
Problems cited by interviewees included data privacy, data governance, ethical concerns, algorithm discrimination and bias, regulatory requirements, and mistrust in AI. They felt that these could be addressed through developing transparency and trust, education, algorithm risk assessment, and compliance with GDPR and new regulations governing the use of AI.
Different ways of adapting innovation
Consultants saw their clients engaging in a range of approaches to innovation. The spectrum ranged from micro to macro innovation, including open innovation, co-creation, external collaboration, increasing speed and agility and developing a structured innovation process as part of the corporate strategy.·
