A Marketing Artificial Intelligence Roadmap
Artificial Intelligence (AI) is seemingly everywhere. According to Gartner, worldwide AI software revenue is on track to total $62.5 billion in 2022, with one third of organizations with AI technology plans have stated they will be investing $1 million more into their development and implementation over the next two years.
When discussing AI, there’s always another topic to discuss, that being Machine Learning (ML) methods.
As every company was beginning to be put through the lockdown gauntlet in 2020, it caused businesses to prioritize. The key priority for many of these businesses was AI and ML initiatives. After all, if you’re a tech company who suddenly finds yourself without a cohesive team who all gather in one place (referred to as an ‘office’ in the before-times) to sort out potential issues, you’re going to begin putting all your chips on the immune-uncompromisable machine.
According to an Algorithma report, 83% of organizations implementing AI and ML initiatives have increased their respective budgets year-over-year. No surprise seeing as how ML models can perform and generalize tasks that would be either too time-consuming or physically impossible for any TEAM of human employees.
It is not a magic business vitamin though, as businesses do struggle when it comes to creating such solutions that can quickly scale alongside their business; ML models are especially effective scaling tools, as they can automate almost everything, including:
- Customer Support
Again, sounds wonderful, but such models can be extremely finicky, and their implementation can cause more headaches than they solve.
Businesses are facing a choice with AI: whether to develop projects through traditional means (i.e. a laborious yet effective method in which a definitive roadmap is laid out to ensure the project meets the deadline and overcomes most, if not all potential roadblocks), or through the implementation of ML (teaching machines to perform a potentially complex task not through instruction, but through exhaustive examples and subsequent feedback.)
I imagine that given the choice, many business owners or team leaders would just choose the standard route, and while it does take longer, assuming you have a reliable and steadfast team, it is more immediately reliable. Adopting any AI model CAN save you a tremendous amount of time and general legwork, but it is a heavy initial investment, not only with money, but with time and implementation as you chase down a seemingly endless series of bugs and hitches.
The implementation of such models, not to sound too pretentious, is something of an art, and it requires not only a great deal of time, but an unholy amount of patience should you not have the pre-requisite experience or knowledge.
This surge in AI models, coupled with the general lack of AI knowledge on the part of many businesses, has led to many businesses scouting out proven AI/ML experts. In a 2020 report, it was found that data science jobs will increase by around 38% in the next 10 years; with demand for ML jobs will rise by 37% over the same period. This will be a veritable experiential gold rush for any aspiring, or established, data scientists.
One thing which many people seem not to be clear on, and quite understandably, is how exactly such technologies can affect are daily lives. Thankfully, in much of the same way as it would be difficult to explain how barren our current world would be without transistors, there are concrete examples, such as self-driving cars.
The self-driving cars sector is growing rapidly, with the market expected to be worth $400 billion by 2025.
Other such early adopters of ML are those in the e-commerce industry and financial institutions. This is because they have a tremendous amount of data and manual processes that ML can optimize and blow through in a hitherto undreamt-of fraction of the time.
Now, delving into the financial world is a nomenclature mine-field and it can be incredibly easy to get confused, but think of it like this: every time you use your credit card, it’s an ML model that decides whether or not the transaction seems suspicious. Another example, which comes from e-commerce, is dynamic pricing; hundreds of times per day, a system decides what price to put on a single product, matching current demands, while also predicting future demand trends.
Not to saw that ML models are so mystical and foreign to us that we kowtow to them for their predictive capabilities, but they can be incredibly useful, and can save the hairlines and blood pressure of many a human analyst.
Many businesses are left in the dark when setting out on their AI implementation journey, well allow us to provide you with a torch: the first thing you should seek to do, BEFORE even investing in such a technology, is to look at your business and figure out what it is you want to streamline or how exactly you wish for this system to save you time. After this, you should set clearly defined goals (such as the reduction of manual administrative work by 80%.) After this, you will need to find an AI/ML expert who can build an initial implementation and help forecast the overall impact on the machinations of the business.
If there’s one thing you should take-away from this, it’s that ML models are not coded, they are trained. Through trial and error, rinse and repeat tactics they are built to do to whatever it is they’ve been doing, only better and faster than they did last time. This is why it is vital that you, as you would with humans, provide high-quality training data to aid in the continuous improvement of your ML model.