AI is ready for business – Common use cases across Perth, Sydney and Indonesia.
To declare 2024 the year of ‘AI Adoption’ would be premature. It has dominated headlines, the taxonomy entering mainstream vocabulary, however the truth is whilst many businesses are interested in what it can unlock, the majority are yet to act.
The situation is remarkably similar to the early 2000s when small-to-medium enterprises struggled to validate the benefit of investing in a website post .com boom. They had succeeded in business up until that point without a digital presence and required ongoing education as to why this new touchpoint was essential to remaining relevant. A concept that is no longer discussed no decades later.
Much like the realisation of any technology, AI alone is not the silver bullet for businesses, instead it’s the objective-led opportunity it unlocks that excites a business: operational efficiency, cost reduction, competitive advantage, data insights, increased productivity or enhanced customer experience.
Here’s a collection of thought provoking use cases that we’ve implemented with our clients in 2024.
Tender Production – The Probity team you didn’t know you had.
For companies doing Tenders, the process has always been a marathon, not a sprint. A team or individual can go missing for weeks during the preparation process. A lot of information is repurposed from previous tenders, however teams are often not clear on what’s been produced in the past and more importantly what’s been successful.
Imagine uploading a Tender and having the first draft produced in seconds based on your private knowledge base of existing tender submission?
Imitated Sales Validation – Asking all your pinpointed questions in advance.
Many businesses judge their marketing strategy on lead volume. A sales team often takes that high-level enquiry and then aims to filter out the ‘tyre kickers’ with a series of questions that determine how serious or educated that prospect is. Given everyone is unique the order, or necessity, of various questions differs and Sales people pride themselves on their ability to weed them out quickly.
Once trained, AI as a sales consultant can determine exactly what to ask, what to ask next and know what information to pass over as a result – all delivered in your language and specific to your product, service or brand.
Optimised Pricing – Live analysis of competitors to drive profit.
We’ve all jumped on Webjet or HotelsCombined when necessary. These amalgamators act effectively as a live pricing market. What many don’t appreciate is how often the dynamic pricing of a hotel room needs to change as demand outweighs supply. Typically this would be driven by an algorithm, however clients are now leaning into RAG (Retrieval Augmented Generation) to provide real-time assessment of competitor pricing and then applying their own commercial logic to automatically adjust the pricing being displayed on their websites or fed to marketplace APIs.
The Fastest ASX Announcements in Australia
There are over 2,200 companies listed on the ASX and no single ASX announcement is ever the same – whether it be format, length or use of graphs and visuals. When these announcements are released, they have traditionally been absorbed by a human professional working in financial services, summarised for key points, before drafting communication to their clients.
Given these announcements are also immediately available on the ASX API feed, many clients have utilised AI to facilitate that process in seconds, including crafting the branded EDM and emailing it out to a specific segmented list in their CRM, all before their competitors have downloaded the PDF off asx.com.au
The AI Quote Builder – Find and replace has an expiry.
Tender teams are not the only ones saving weeks of their year in time. When dealing with a vast array of suppliers, manufacturing brands offer their product sheets for download in Excel, PDF or other, with no consistent formatting. The smartest thing to do was save the latest versions down to the centralised T: drive.
With a constant stream of skews, quote variations and freight login, a salesperson would then sift through the T: drive searching for product codes and the current price, manually constructing their quote(s) and losing hours of their life in the process.
When this data is instead stored in a knowledge base with an agent trained to know what to look for, or even personalise for a specific client, this process becomes instantaneous to the point that even the process of saving the files down to the T: drive is now redundant.
Non-Generic Quote Consulting – working weekends, for free.
Similar to the sales validation, the creation of a quote is often unique for that company, individual as it contains a lot of tailored information relevant only to them. When the entire quote is passed to an AI Agent, this allows the Agent to answer quote-specific questions at all times of the day – whether it be about the savings, the product itself, delivery times or comparison of another product.
Intelligent Online Support – A Chatbot first, it was actually helpful.
The Chatbot brand is badly tarnished and even AI has a long road ahead to repair the anguish that we all suffered as a result.
Chatbots were logic driven, typically by wildcard search, and simply put – they sucked. Fortunately, the simplistic programming of these interfaces has been substantially improved when linked to an AI agent. When this agent only responds only in reference to your company information – whether it be a process, a specific person to speak to, providing the link to a members area, assisting with account details reset or advising on booking time availability – and then provide the necessary next step, the world “helpful” has finally entered the game for many of our clients, saving their support teams a wealth of time.
Data Insights – The death of static dashboards and query builders.
Most businesses have collected an ocean of data yet aren’t really sure what they want to learn from it. Some feed it into a Management Dashboard through products like Power BI, others have custom build software that produces visuals in terms of productivity or NPS. These all serve a particular purpose, however helping clients ask anything of their data has uncovered insights beyond their imagination. There’s no longer a need to “run a report”, instead the plan has been to continue to train a model to the point that it understands what the data means, its relationship to parts of the business and what to look for in terms of insights or anomalies.
Every staff member can then ask questions relevant to their role: “by state, which products have seen the greatest demand in enquiry over the past 3 months?”
So what are businesses waiting for?
Governance. It’s the necessary first step before embarking on any aspect of the technology.
Organisations that fail to set up proper foundations, run the risk that AI runs amok with inaccuracy, security, privacy and data protection issues. Ultimately if these risks are not addressed up front there will be an inability to scale any of the immense and potential benefits.
AI Governance establishes the principles, policies, and procedures for the responsible development, deployment, and use of AI within an organisation. Developing a robust AI Governance Framework ensures that AI initiatives align with ethical standards, regulatory requirements, and organisational goals.
