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Making AI work for your business: In conversation with Google Cloud experts

In a recent webinar, Caroline Quintanar, Google Cloud partner engagement manager at Making Science — the digital acceleration company specialising in marketing cloud solutions and AI — sat down with Michelle Liu, customer engineer at Google Cloud, to demystify the reality of AI, its impact on everyday business operations, and why AI isn’t coming for our jobs.

Making AI work for your business: In conversation with Google Cloud experts

Despite the recent hype around artificial intelligence, the tech isn’t new — it’s in our smartphones, homes, and businesses.  However, Making Science believes it has the potential to be as seismic a technological change as the internet and the smartphone.

Many of us are using AI without even realising it, but its increasing visibility and the conversations around it can make the technology appear intimidating, such are its seemingly limitless capabilities. But it’s important to demystify some of the confcerns around AI.

Everyday impact: humans are still important

Over the past few months, Making Science has encountered the recurring concern across different industries and roles — the common fear that AI will put humans out of work. Liu’s response to this is: “We see AI as an assistive tool… so should you be scared? Probably not, because you’ve already been using it, maybe without even knowing it. If you're a Google workspace user, you'll start to see these generative AI features being built into tools that you’re already using today.”

Like the AI experts at Making Science, Liu (pictured, right) does not believe that businesses are ready to hand over complete control to AI, but it can “automate a lot of mundane tasks so you can focus on the more interesting or complex aspects of your job. The human in the loop is still really important”. 

The business case: frontline of customer service

Generative AI, described by Quintanar (pictured, right) as “the tool of the year”,  is accessible and available to most businesses at a reasonable cost — tens or hundreds of dollars per month rather than the tens of millions that it would cost to build your own large language model (LLM). According to Liu, using a “generalist chatbot interface”, such as Google’s Bard, is a “very easy way to get started”. She advised using it as a research assistant for report writing or strategising while being wary of inputting sensitive information.

Liu added: “The productivity software tooling can be really helpful and there are YouTube videos that show how easily you can give it a prompt and have it draft a report then ask it to translate the report into different languages.”

Generative AI is being integrated into a lot of platforms at an unprecedented pace, and the chatbot has the potential to move to the front line of customer service. “Retail is an exciting space,” said Liu, citing the example of a chatbot being used as a shopping assistant, customised and plugged into a business’s order and delivery system so that “it's actually helpful to your user base”. Travel is another area where a bespoke chatbot could add real value to the customer.

While the creation of a customised shopping assistant is one for an internal or external tech team, the creation of a chatbot is remarkably straightforward, according to Liu. “It’s been really cool watching customers build MVPs (minimal viable products) in two hours and have a fully fledged chatbot or a search tool powered by generative AI.”

Getting started: be specific, stay safe

Liu advises “being intentional with the questions that you ask a large language model”. The responses you receive are only as good as the information provided to it. “So it takes some exploring and playing around with,” added Liu.

It’s tempting to think of AI as the solution to every problem — but it’s vital to focus on the specific use cases. “There’s still a place for having strong traditional analytics and a good data foundation,” said Liu.

There are understandable concerns about security around the sharing of sensitive information with an LLM. Some of Liu’s customers have produced an AI ethics standard guide, essentially an addendum to an employee handbook. She advises “reading the small print” of any third-party companies that businesses might engage with.

Myth-busting: the cost conundrum

There is an assumption that moving services from ‘on-prem’ to the cloud cuts costs. “But you really have to look at the trade-off,” said Liu. If you’re paying for a service rather than hardware and infrastructure, moving to the cloud introduces cost variability, which requires a change of mindset. “If it’s done correctly, it will be cheaper for the quality of the service,” she added. 

That’s where Making Science can make a difference with its proprietary tech and new products Composable Customer Data Platforms (CCDPs) and Customer Data Architecture (CDAs) on Google Cloud.

CCDPs are designed to be used without dependency on technology teams, while CDAs aggregate information from different areas of a business and make it accessible across all relevant teams. Both products provide long-term solutions that enable organisations to own, and take control of,  their data for increased efficiency. 

Making Science partnered with Google Cloud expert Michelle Liu to deliver insights and responses arising from conversations about AI with clients and prospects. Making Science is a trusted consultant and advisor for anyone interested in starting the AI journey. You can contact them here.

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