How to use Generative AI to drive customer growth

29 July 2024 Consultancy.com.au

The emergence of generative AI is offering organisations a large number of avenues to drive customer growth, writes Greg Taylor, Vice President at Databricks.

Artificial intelligence (AI) adoption in business has been rampant over the past few years, driven by the quest for efficiency, productivity and innovation. Recent developments in generative AI are further accelerating the uptake of the technology, with analyst firm IDC expecting Australia's spending on AI systems to reach $3.6 billion in 2025.

It makes sense. The local business community has clearly cottoned on to the potential efficiency gains, cost reduction and long-term capabilities made possible by AI technology.

How to use Generative AI to drive customer growth

Greg Taylor is Vice President Consulting and Systems Integrator Partners for Asia Pacific & Japan at Databricks

By delegating time-consuming, repetitive tasks to an efficient AI system, for instance, businesses can free up time for people to focus on high-value tasks like creative strategic thinking and innovation.

So it may come as some surprise to discover that just 30% of respondents surveyed in MIT Technology Review Insights research commissioned by Databricks say their organisations are rapidly adopting AI tools. A much larger proportion (42%) say their organisations are moving somewhere between moderate and very slow when it comes to AI tool adoption.

With 98% of Australian enterprises using generative AI in some way but only 30% adopting it, there is still a long way to go before the business community is making the most of such technology. This AI gap opens up new opportunities for enterprise partners like consultancy firms and systems integrators to tap AI to drive efficiency and effectiveness for their clients.

Further reading: V2 Digital's State of AI in Australia: Embrace AI or be left behind.

The link between efficiency and cost reduction

The top use cases for AI among enterprise customers are varied but include augmented customer service agents, digital assistants, smart innovation and automation. In the current economic climate, one of the big focus areas is cost management through greater efficiency – and AI has the potential to deliver a step-change in efficiency.

For instance, AI-powered automation – in this case, what we call ‘boring AI’ – is helping to streamline mundane, repetitive tasks such as data entry, invoice processing and reconciliations. Delegating such time-consuming, often manual, tasks to an efficient AI system can free up time for employees to focus on higher-value tasks like problem-solving, strategy and innovation.

It should come as little surprise, then, that a majority (70%) of those surveyed in the MIT Technology Review Insights research considered cost-cutting a top priority for AI systems. Against this backdrop, four in five Australian businesses (80%) expect to become at least 25% more efficient in the next two years thanks to AI technology.

With the skills needed to implement advanced AI technology internally in short supply in Australia and further afield, enterprises are increasingly turning to their external consulting and integration partners to provide them with a viable AI strategy and the know-how to implement it effectively. This is where consultancy firms have the chance to step in and help out.

Employing enterprise data for an AI edge

Implementing internal AI systems for specific business outcomes within an enterprise is a very different proposition to simply tapping an external, internet-based platform like ChatGPT for some quick and easy marketing content creation. Data underpins the effectiveness of AI-based solutions, so it’s important for applications that rely on enterprise data to have easy access to it.

However, plugging business data that might contain customers’ personal details or commercially-sensitive information into publicly-available AI platforms can expose it to potential exposure. For AI applications that draw upon company data, it is best to deploy them within an enterprise’s own IT assets. But for this to work, the company data has to be managed correctly.

Historically, getting data management right for AI applications that require large data sets to operate properly has been challenging. This means that a big part of an external partner’s role in helping a business implement transformational AI internally is not only the design and implementation of the AI model itself, but also the data management platform to underpin it.

Fortunately, there is a new generation of data intelligence platforms emerging that is putting complex internal AI applications within reach of many businesses. Based on the data lakehouse architecture pioneered by Databricks, these data intelligence platforms use AI models to deeply understand the semantics of enterprise data, making more sense of data across the enterprise.

Using AI to drive AI

With AI-powered data intelligence platforms in place, enterprises can gain the level of insight and visibility they need to properly train and operate AI models, especially those like ChatGPT that rely on very large data sets to work as anticipated. For external partners tasked with helping businesses implement AI solutions, this means a two-pronged approach to enablement.

The first part of a successful AI initiative involves the data intelligence platform, ensuring the client’s data is usable. Only once this is in place can the second part, training the AI model, be accomplished. This should not be overlooked. The quality of the data used to train an AI model directly influences the effectiveness of its application and the business outcomes it delivers.

AI-powered data intelligence platforms enable capabilities such as semantic cataloguing and discovery, natural language access, automated management and optimisation, and enhanced governance and privacy – all essential to AI solutions. This is, in part, why such data platforms open up the doorway for external enterprise partners to dig deep into emerging AI use cases and bring new levels of efficiency and innovation to their client organisations.

Using AI to drive AI by leveraging data intelligence platforms not only boosts enterprise clients’ AI capabilities, it also has the potential to improve their overall data utilisation, because organisational data becomes easier to use for other functions such as general insight, analysis, forecasting and enhanced decision making, all of which will make client businesses stronger.