In the new normal of Generative AI (GenAI), where data is essential to propel organisations forward, maintaining a clear data and artificial intelligence (AI) strategy is a strategic imperative. The risks of neglecting or ignoring data and AI can be considerable, from operational inefficiencies to reputational damage. Instead, by approaching investment strategically, organisations can unlock opportunities for innovation, growth and sustained success. The journey towards a data-centric future, now intertwined with the omnipresence of GenAI, begins with a single, strategic step – an investment in a fit-for-purpose data and AI strategy.
The convergence of business data with AI, especially GenAI, continues to transform the landscape in unexpected ways. Whether you're directly using AI or not, it's becoming a part of your business through integration into enterprise software and solutions. It’s appearing in cloud-based productivity tools from Microsoft, to Salesforce and SAP and embedded in the processes of your suppliers and partners. This embedded intelligence enhances efficiency, automates routine tasks and augments human capabilities. It also increases the amount of data being held, exposing organisations to risk if not well managed.
The importance of a data and AI strategy to mitigate risks
Organisations that fail to recognise the strategic role of data risk being left behind in the competitive marketplace and are underprepared to respond and direct the inevitable changes in their technology and vendor ecosystems. Operations that lack data-driven insights have been shown to become increasingly inefficient, hindering their ability to adapt swiftly to market changes. A failure to prepare for and invest in AI now means missed opportunities for innovation, limiting your ability to remain relevant through the creation of in-demand products and services. A considered data and AI strategy can help you:
1. Get more done with less
If you don't use data to adjust team sizes based on demand, improve production schedules and minimise maintenance downtime, you’re experiencing waste and cost inefficiency. The ability to forecast demand accurately, thanks to data insights, empowers you to optimise resources effectively. By using AI algorithms to analyse production data, you can refine production schedules, minimising idle time and maximising efficiency. Proactive maintenance, guided by AI predictive analytics, reduces downtime and ensures that assets operate at peak performance, contributing to cost savings and improved operational reliability.
This is in addition to the significant productivity benefits that can be realised through effectively embedding GenAI and Machine Learning into business-decision support tools.
2. Avoid lasting consequences for failing to correctly manage data
A forward-looking data strategy strengthens strategic data platforms with novel business-driven use-cases to deliver value. It should also seek to retire platforms that no longer serve a valid purpose and dispose of data that is no longer required. With customers increasingly valuing transparency and privacy, errant data use is a sure way to erode trust and credibility.
Organisations with a solid understanding of their data holdings better target data governance and security initiatives to reduce the risk of data breaches and accidental exposure. Before AI tools are introduced in the environment, sensitive data must be appropriately categorised and protected, this is even more important when the design requires business data to be integrated into the AI ecosystem to contextualise results. In an era where data is a precious commodity, not investing in data security and ethical use can potentially lead to a breach of regulation and legislation and severe reputational damage that can be near-impossible to overcome.
3. Gain competitive advantage, and when used appropriately, prevent latent risks from materialising
A further complication for the modern digital organisation is the increasingly vast amounts of data (and metadata) collected through digital means such as sensors alongside data assets e.g., audio, video and photos. It’s clear that valuable market insights can be captured through inspection and analysis of this data, which is why data is referred to as the new currency. To identify important patterns or trends and take action, a clear approach for maintaining this influx of data is needed.
On the other hand, organisations may retain data that poses compliance risks, and if not properly assessed, could result in severe consequences. This data may include Personally Identifiable Information (PII) or Sensitive Personal Information (SPI), requiring careful management. Additionally, aggregated data sets may be in play, necessitating the expertise of data scientists for thorough analysis.
Some insight into real-world examples:
A transportation company that fails to analyse data on vehicle maintenance and driver behaviour may be at risk of failing to act to prevent accidents or safety violations.
A retail company that does not properly analyse customer data may be at risk of fraud or other criminal activity.
A financial institution with a large gender payroll gap due to historical hiring and promotion practices may be exposed through mandatory reporting.
Therefore, it is crucial for organisations to invest in data analysis platforms and processes to ensure that they are able to identify market trends and address potential risks before they become major issues.
Why organisations need to invest in data and AI now
Investing in data and AI is identified as a key pillar for business model reinvention, in PwC’s new 27th Annual Global CEO Survey - Australian insights. It unlocks benefits that can catapult organisations into a new realm of competitiveness.
Modern user experiences and applications are crafted through data-driven insights, ensuring that products and services align seamlessly with customer needs and expectations. The ability to make informed, data-driven decisions becomes a strategic advantage, allowing organisations to pivot quickly in response to market dynamics.
Leaders must embrace the transformative power of data and AI, not just as technological tools but as integral components of a future-ready business strategy.
