The pressure to adopt AI seem enormous. We’re constantly told that AI can revolutionize business productivity and enhance the quality of our services. But when it comes to actually implementing AI, direction isn’t so clear. Should you overhaul current business practices and introduce AI in one go? Or is it better to adopt a steadier integration, even if that means it will take longer? As an AI-led company, we know adopting AI doesn’t have to be overwhelming. Here’s the simple and flexible route to automation.
It’s now undeniable: becoming an AI-enabled business enhances your market competitiveness. Nowhere is this clearer than in the successful entrance of big tech firms, like Amazon and Google, into niche industries that diverge from their core products. They didn’t have any specialist knowledge or experience about the new markets they were entering; they simply leveraged new technology. It is their smart application of tech that has allowed them to become market leaders in everything from the automotive industry to video streaming.
Harnessing a data-driven approach can massively improve performance and lower costs, facilitating a substantial market share among those who respond quickly to technological change. As such, there’s tremendous pressure to adopt AI immediately, with many warning that putting it off will make it impossible to catch up with AI-enabled competitors. So understandably, it can be tempting to undertake massive top-down business change, that entirely changes how data is collected, shared and analyzed within the company. But is this the right approach?
There are undoubtedly benefits to this “everything at once” AI approach. An analysis of the business and public sector has shown that across sectors, governments and private companies are currently only extracting around half of the current value of their data. If the widespread introduction of AI is a success, then a clear competitive advantage can be gained through improved analytics and insights.
However, a radical top-down transformation of business is difficult, meaning this strategy has substantial risks. Research by McKinsey suggested that as few as 30% of digital transformations actually succeed, with only 16% of these actually improving performance. This is because digital transformations don’t just involve introducing new technology into a business; they require engaging multiple stakeholders, ensuring that current technology is compatible and aligning company culture.
Employees are often resistant to adopt new technologies, meaning motivation and training is needed to ensure new tools are actually used. Similarly, insufficient infrastructure can plague the successful implementation of technology and has been widely accounted for failed digital transformations.
Ensuring that the all the changes required are fulfilled makes digital transformation a costly process that yields a low return on investment – at least, in the short-term to medium-term. So, while the idea of an immediate and widespread adoption of AI can be tempting, the decision shouldn’t be underestimated or taken lightly.
This doesn’t mean we should shelve the project completely. The benefits of AI are substantial and failing to invest in new technologies presents a missed growth opportunity of up to 120%. And thankfully there is an alternative approach that doesn’t require a complete business overhaul: integrating AI gradually using specialist AI tools.
One of the easiest ways to bring AI into the workplace is through investing in ‘lightweight’ AI software. These out-the-box solutions are developed by AI companies with consumer convenience and easy integration in mind. Instead of reinventing the wheel, they focus on doing one task really well – whether you’re looking to standardize customer service communications or automate essential business admin.
There are a huge variety of the types of ready-made AI tools on the market. Zoom.ai, for example, deals with communications – providing you with a digital assistant to automate repetitive tasks like arranging meetings. Others, like Timely, seek to remove the overhead and inaccuracy of manual time reporting, by automatically recording everything you work on and creating draft timesheets for you.
Certainly, there are drawbacks to the gradual adoption approach. There is potential, for example, for a competitive disadvantage to emerge if a competitor implements a business-wide AI overhaul and is able to achieve substantial improvements in performance. But for most companies, adopting ready-made AI solutions is a more realistic and relevant strategy. To name a few benefits:
For those who have greater sensitivity to cost, out-the-box AI is a much more accessible option. Lightweight programmes do not require the expensive overhaul in data storage infrastructure necessary for implementing ‘heavier’ AI solutions. And since these tools tend to package features, you only pay for what you need.
Similarly, like many others, both Zoom.ai and Timely offer free trials, meaning you don’t have to commit to the technology without first testing it in practice and proving its value. These “test drives” are extremely useful, since it allows businesses to determine whether these tools will offer tangible benefits and if staff will actually use them.
Of course, a gradual approach does not remove the longer-term costs that will inevitably be associated with upgrading infrastructure and unifying AI programmes. With this said, the gradual adoption of technology is likely to reduce the cultural resistance companies face when implementing technology upgrades, since lightweight tools help make technology more understandable and accessible. This is an important point, since companies that foster a digital culture are far more likely to succeed in implementing AI.
So, adopting AI doesn’t have to be as overwhelming as it first seems. For companies who don’t have the risk appetite or funds to undertake a complete business overhaul, adopting lightweight AI solutions offers a simple and effective route to automation. These technologies not only allow for an easy implementation and improved productivity but can also help develop the type of digital culture needed for longer-term AI adoption.