Jason Janicke, Senior Vice President of EMEA & APJ at Alteryx, explains that the rapid rise of Generative AI presents a significant opportunity for CIOs to become pivotal change agents in shaping the future of their organisations.
As interest in Gen AI continues to penetrate the media and business markets, executives everywhere are faced with expectations to pilot, scale and measure the impact of the technology.
A recent PwC survey found that 64% of UK CEOs see Gen AI as enabling greater productivity in their workforce. That rings true given the success that businesses are enjoying from initial pilot programmes of the technology.
In a survey of global IT business leaders, we found that businesses have run an average of three pilots using gen AI, with 77% stating that the projects were very or extremely successful.
Charting the course for scaling AI, however, is paved with difficulty. The responsibility falls to IT decision-makers to innovate while navigating budget cuts against a precarious macroeconomic backdrop. Despite this, the meteoric rise of Gen AI is a massive opportunity for CIOs to forge a role as the change agent that shapes their organisation’s future – steering the ship through tumultuous times.
Success in stepping up into this role, however, isn’t guaranteed. CIOs need the right priorities to ensure that scaling Gen AI strikes the right balance between the technical and business needs of IT – and ultimately fuels the goals of the organisation’s broader strategy.
The data stack is just the start
Organisational tech stacks need to undergo change to be positioned for the era of intelligence with ubiquitous Gen AI and Machine Learning. Squeezing maximum insight from company data is essential to discovering efficiency gains, increasing profitability and uncovering new revenue streams.
The right data stack is important – but even more critical is having a way for business users to access, and generate insights from, data. High-quality data is required as an input for AI to generate high-quality output.
Modern data stacks facilitate this when they nail the fundamentals: storage, data governance, ETL, scalability and flexibility across on-premises, public, private and/or multi-cloud environments.
So, now that you have your data stack optimised, how can you ensure your business users can interact with it to drive the insights that the business needs? Business users often feel ‘locked out’ of enterprise data with cumbersome reporting and request processes.
CIOs can empower business users – while maintaining governance and security – with self-service analytics tools that make sophisticated AI-driven functionality intuitive. With a functional analytics ‘interface’ to the data stack, CIOs can map and shepherd AI applications toward high-impact use cases.
This looks different for every business – but it’s the CIO who has the ideal mix of technology and business acumen to identify these use cases and the required data, in collaboration with business leads. It could be data stemming from CRM tools, financial systems, web analytics or all three. The important parts are ensuring high-quality data sets, creating cohesion among them and optimising workflows.
Responsible policies and practices
CIOs may have the tech stack to funnel AI-ready data into high-impact use cases but still encounter resistance from the wider C-suite. There’s good reason for this: the risks in harnessing Gen AI are real and widely understood.
Such hesitations could lead boards to block the use of LLMs outright. But employees, in and out of work, are already using gen AI. We’ve found that 76% of the general public feels positive about the increasing use of Gen AI to create content they consume.
Instead of blunt diktats, CIOs should oversee stringent data governance and implement responsible AI frameworks. Such frameworks will establish where AI will and won’t be applied and acceptable levels of risks. And strict data governance will ensure that wherever AI is applied, it’s done so leveraging the most accurate, high-impact data.
This is pivotal for more organisations to put in place. In our previously mentioned recent survey of global IT business leaders, just 33% of EMEA respondents said their business ensures the data they’re using to train Gen AI is diverse and free of bias. CIOs need to get a handle on this by leveraging effective data and analytics platforms that have stringent governance and security embedded within.
Scaling AI means bringing people along
Another aspect of CIOs stepping into the wider business is shaping the future of work and collaboration. In the intelligence era, data literacy must go beyond IT and into the business through the democratisation of data analytics. AI has a massive role to play in making it happen.
Integrating natural language processing (NLP) into data analytics software allows a much wider set of business users to prep, analyse and interpret data. Engaging with data in a natural way invites more exploration and applications of data to turbo-boost and accelerate speed to insight. This is an important step to enable everyone to make more insightful and confident decisions with analytics and AI.
CIOs should be advocates for these technology shifts to be accompanied by a culture of continuous learning and innovation within teams across the business. This doesn’t require all employees to become data scientists. It entails giving more employees the knowledge and self-service analytics tools to leverage Gen AI for critical insights that have true business impact.
Paired with domain-specific business expertise, soft skills come into play as a great foundation to generate effective outputs from LLMs. CIOs that understand Gen AI can act as a force multiplier for the business (think of turning a team of two data scientists into a virtual team of six). This is an opportunity to guide their organisation away from heavy reliance on technical skills and nurture a broader set of AI and analytics skills.
The imperative for businesses to make Gen AI rollout a success is a massive opportunity for CIOs to be a change agent for the entire business. Building a resilient tech stack, cultivating a data-skilled workforce and establishing responsible AI policies and practices are three key pillars for moving Gen AI from pilot mode into scalable use cases that unlock true value.
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