Analyst firms such as Gartner and IDC predict 2018 will see a sharp rise in the number of organisations deploying AI technologies as the benefits become more apparent.
Gartner reported recently that 59 per cent of organisations have started gathering information to build an AI strategy. By 2020, it estimates 30 per cent of CIOs will have AI as one of their top five investment priorities.
IDC predicted in April that global revenues for AI systems would reach US$12.5 billion this year, up almost 60 per cent from 2016. By 2020, it expects that figure will hit as high as US$46 billion.
AI and ML have a massive potential to enhance decision making, re-invent business models and ecosystems and remake the customer experience.
While many small to medium-sized enterprises (SMEs) might be tempted to take a wait-and-see approach, this emerging sector is already attracting significant investment.
This is not to say that businesses should rush into a complex and confusing market. However, there are a number of IT projects to consider for those looking to dip a toe into the world of AI and ML. While conducting small-scale experiments, these businesses can build skills and confidence in this space.
Here are eight AI and ML opportunities to consider:
1. Build a chatbot
Most companies know the top frequently-asked questions posed by their customers. This Q&A process can be handled by easy-to-build chatbots, using tools like Microsoft QnA Maker. Aside from customer service, chatbots can also be used to create information resources for staff, including answering questions from new recruits about HR or other areas of the business.
2. Implement a marketing automation solution
The marketing department has increasingly become the earliest adopter of new digital technologies. This helps to explain why platforms like Salesforce.com, Adobe Marketing Cloud and Microsoft Dynamics 365 have taken leadership positions in offering machine learning capabilities.
Features like being able to recommend related products to customers, showing personalised search results and curating sales leads have become standard. Companies can also create solutions to predict when a deal may be going cold, and even matching customers with the best customer service officers.
3. Reduce fraud and cyber security risks
Data analytics has a huge role to play in fraud detection and cyber security. But, given most companies have increasingly larger sets of data, they need to be able to analyse at scale to achieve peace of mind.
For example, using ML, a financial institution can identify unusual activity such as multiple payments being made just under the trigger limit. Other clues that might ordinarily go unchecked include new merchants behaving differently, or phishing attacks that might lure unsuspecting users to share information that shouldn’t be asked for.
4. Smarter inventory planning
Supply chain automation isn’t new, but ML is making it much more common. Instead of just historical sales data, ML lets the business use data about the way customers research their purchases online, the impact of shopping habits and other trends that affect buyer decisions, plus manage inventory by forecasting demand.
For example, online retailers can use ML to accurately predict what will sell in the next month, making it easier to reduce stock and free up more working capital.
5. Travel and logistics improvements
Employees that aren’t office-bound are always looking for more efficient ways to work. Whether it’s getting salespeople to prospects, sending deliveries to customers or picking the business location that will attract the most customers, routing and travel planning can have a big impact on the bottom line.
There are a number of simple AI tools that can help. For example, Bing and Google Maps provide predictive traffic services through their APIs so maps show distance and travel time. This can help with scheduling the right amount of support calls an engineer can make in an hour, or finding the best time of day to make deliveries. By adding asset tracking and location data, organisations have the beginning of an AI-driven logistics solution.
6. Smarter maintenance
Too often, businesses schedule machine maintenance and repairs based on when things have already broken down. This means the organisation ends up spending more time and money than needed, while possibly causing brand damage if customer demands aren’t met because of unplanned downtime.
AI and ML technologies can help companies make accurate predictions around their maintenance cycle, making it easier to co-ordinate site visits and stock spare parts. This ultimately leads to less downtime and less impact to the bottom line.
Today’s highly digital work environment has changed the way people think about work. This is combined with more data and digital tools to extract valuable insights about the business’s current and potential future workforce.
For example, AI tools can flag what words or expressions might diminish the effectiveness of a job advertisement, or increase it.
8. Improve workplace safety
Industries such as building and manufacturing face many potential work safety issues. Using sensors, cameras and facial recognition technology makes it possible to know when equipment is being used incorrectly, or by someone without the appropriate certifications or training. Wearable digital devices, including eye-tracking devices, are already being deployed as part of AI systems supporting better safety in these sectors.
AI and ML offer significant benefits that are specific to each industry. Organisations should start experimenting with small-scale applications now so they can ramp up their skills and experience with these emerging technologies. This will position these organisations for success in a future when AI and ML are the norm.
Where businesses lack the skills required, they should look to partner strategically with third-party providers. An IT partner like Brennan IT can consult and design a technology roadmap that aligns with the company’s business objectives.
Dayle Wilson is the chief operating officer of Brennan IT.