There’s no doubt that artificial intelligence is seen as an enabler of business innovation and digital transformation.
However, business leaders often struggle with understanding the implications of AI in their everyday operations. This is oftentimes due to common misconceptions regarding AI adoption in business.
In this blog, we debunk five of the most common misconceptions we’ve come across when speaking with business leaders who are considering AI adoption in their enterprise.
1. AI will replace humans in the workplace
Oftentimes any mention of AI is synonymous with the gloom-and-doom fallacy that deep tech will completely replace humans in the workplace in a not-so-far future. Although automation will undoubtedly cause some job-loss, machines completely replacing humans is far from reality.
In fact, the implementation of AI and machine learning technologies in business will act as a catapult of job creation. According to a report released by PwC, over 7 million jobs will be displaced by AI between 2017 and 2037. However, it will also lead to the creation of 7.2 million jobs.
When considering the use of AI to enhance processes, business leaders should think of this adoption as a collaboration between technology and humans. If occasional misjudgements in any business operation is not critical for its success, then a simple automation approach may be appropriate. But if any error runs the risk of having major repercussions, then humans must be involved in this process and decision-making.
In the later case, Human-in-the-Loop (HITL) or Expert-in-the-Loop (EITL) models may be used. These models are simply ones that require human interaction. For example, a system may be put in place where the AI output can be produced with a confidence factor or ranking, where only the output that surpasses a certain threshold of confidence will be bypassed into the system or database, while the output that didn’t surpass the threshold is flagged for humans to check.
Deciding when humans are needed and relinquishing control over what machines can control is a big part of deploying AI into business, and a majority of the time, a collaboration is necessary.
2. AI is expensive
While implementing automation at an enterprise level is not exactly cheap, there are ways for businesses to adopt process automation without it costing an arm and a leg.
In the past, AI was limited to the use of large enterprises and vendors who had the capabilities to build their own in-house technical teams. However, nowadays smaller businesses are reaping the benefits of this technology, too.
The key when opting for AI solutions within any company is determining exactly what you want to accomplish. The more narrow your scope, the better. This is especially important for smaller businesses. Focusing on one particular objective within one business process allows you to test the use of AI within your business without breaking the bank. Once proven successful, this can open the door for later projects and budget justifications.
There are also a huge number of AI vendors and AI startups that have emerged in the last years who offer highly-specialised solutions. Partnering with an AI vendor and outsourcing some of the expertise involved is another viable option if budget is limited.
3. Only technical people can understand AI
One recurring theme we hear from business leaders is they feel they don’t have enough technical knowledge about AI to initiate and plan AI projects.
This is simply not true. Although data engineers, data scientists and machine learning engineers are needed to analyse the data, build the AI models and put them into production, they aren’t typically the people who are evaluating business cases for AI in their enterprise.
Business leaders are well-equipped and capable of developing an AI strategy, and can work hand-in-hand with technical teams to validate and put their plan into motion. In fact, AI strategy should be led from a business perspective first, starting with the evaluation of the end value that is wanted (e.g. revenue increase, cost reduction, better user experience, etc.) and identification of a proper business case. This way an AI strategy can be designed that leads to the end value benefits.
To better prepare for technology adoption, non-technical professionals should take some time to get up-to-speed on the capabilities of AI, decide on a clearly-defined objective for AI’s use, and create an AI business design to evaluate business cases for AI adoption.
4. AI can solve all your business problems
There is no plug-and-play AI solution, and AI is most definitely not a magic pill that will solve all your businesses’ inefficiencies and bottlenecks.
As stated, a business objective must be clearly defined. Bespoke AI models are then created for a very specific process and data sets in order for a successful AI solution to be feasible. AI capability must be built from scratch and highly customised.
Expecting a one-size-fits-all solution that addresses an enterprise’s every need is simply unrealistic. Remember, start small. When one project is successful, this solution can be considered for similar processes in other departments across the company (with adaptations and customisations, of course), or new business cases may arise.
5. Data is king
The idea of “the more data the better” is a misconception. When it comes down to how much data is needed for a successful AI project, it really depends on what exactly you are trying to do.
So how much data do you need?
In our last blog we discussed the three thresholds to consider when deciding what and how much data can really do for you. We won’t get into the details of these thresholds in this blog, but it’s worth taking a look to better understand the use of data.
What we will stress in this blog is that the amount of data is not as important as its relevance to the problem you need to solve, and that having a lot of data doesn’t always create competitive advantage.
What matters is possessing the right data. AI models can only attain their designated and desired objectives if they are fed with the right sort of data. Significant effort should be spent gathering, analysing and feeding algorithms data that is relevant to the desired outcomes, as opposed to trying to train these models using large quantities of irrelevant data.
With the myriad of information floating around regarding AI, it can be easy to be misinformed about its capabilities and implications in enterprise. Debunking these common misconceptions is a first step to understand what AI can do for your business.
If you’re interested in learning more about how to initiate, validate and achieve ROI for your AI projects, get more information about Nexus’s AI Factory or get in touch for more information and a training specialist will contact you shortly.