As VP of Operations at BairesDev, Damian oversees the entire customer relations life-cycle, safeguarding the company’s operations.
Artificial Intelligence Technology
Over the last couple of years, the long-standing promise of artificial intelligence (AI) started to bear fruit for companies across many industries. Companies that have adopted AI have begun seeing how it can help them get valuable insights from massive data sets and automate processes for increased efficiency. AI also aids businesses to become agile, innovative and scalable.
All of these benefits have always been important but have become crucial for many companies ever since the Covid-19 pandemic first hit. Now companies that don’t have the necessary agility, innovation and scalability often struggle to overcome the challenges in this new landscape. So it’s fairly logical to think that companies will increase their investments in AI. In fact, a recent IDC Spending Guide projects that AI investments will increase at a compound annual growth rate (CAGR) of 20.1% in the 2019-2024 period.
That estimate doesn’t paint the whole picture, though. That’s because businesses might throw more money to new AI in the near future, but that doesn’t guarantee they’ll get a significant return on investment (ROI). While obviously necessary, economic investment isn’t the only essential aspect of successful AI implementation. There’s also the need for a sound AI implementation strategy to guide the whole process.
I’ve found this is precisely when many businesses fail. There’s a widespread misconception that integrating AI into any given workflow is a plug-and-play process. Naturally, those who see AI like that can be bound to fail, as they don’t see the many tasks and risks associated with implementing such a game-changing technology. I’d say it’s not their fault. I’ve found that a lot of so-called AI experts sound more like salesmen than true experts and push AI’s benefits and impact without talking about the challenges.
This leads to many executives buying into AI without the proper knowledge, especially of two of the most important things associated with AI implementation: identifying AI opportunities and defining clear goals. As the VP of operations at a technology solutions company, I want to review some of the ways in which you can do both, which will hopefully help you better plan your next AI-related project.
Where To Use AI For Maximum Impact
Any given business has plenty of opportunities for improvement where AI can have a significant impact. But getting to know them is trickier than it seems. That’s because people often associate AI with very specific tasks (such as processing data or automating processes), so they instantly believe that that’s all there is to it.
Naturally, AI can do that and then some, but saying that data-related or repeated mundane tasks are areas of improvement is too broad to be taken seriously in my view. There has to be some granularity in identifying opportunities because focusing on general topics can lead to disaster. In that sense, I suggest to look for AI improvement opportunities in three levels:
- Business level: processes that impact profits, revenue and growth.
- Customer level: processes that impact products, services and overall customer experience.
- Employee level: processes that impact operational workflow and structure.
If you think about it, the three levels overlap and mix with one another constantly. It makes sense, as a business is the result of the relationships between the business strategy, its customers and its employees. It’s impossible to think of revenue growth without considering changes in the products, services or customer experience, which, in turn, will only result from modifications to the operational structure.
This might have you believe that all AI-powered changes at the employee level will have an impact on profitability or on the customer experience. That’s not the case.
For example, you might implement an AI algorithm to reduce the workload of your customer support team, and the only measurable impact can be that your team is relieved from minor issues to focus on larger ones. The customer experience that results from it might be the same and you likely won’t gain new customers out of it (at least not directly), but the AI surely can improve your employees’ day-to-day lives, which can lead to an increase in employee productivity, satisfaction and loyalty.
Of course, this is just a theoretical example. In reality, it’s probable that operational changes have a certain effect on the other levels. The real take here is that you should start identifying AI improvement opportunities in the three levels without expecting a ripple effect. When you do so, you can add value throughout the chain and increase the likelihood of a more general boost that can ultimately lead to more impactful results in the broadest terms (improved customer satisfaction, boosted productivity, increased sales).
Aside from those levels, it’s also important for you to remember that getting to those broader results is only possible if you take one step at a time. In other words, start small with your AI efforts to eventually get to bigger things. Focus on identifying AI improvement opportunities that are reachable, achievable and measurable (such as the automation of incoming customer support communications through chatbots).
By embracing this mentality, you can build solid tech foundations for larger AI efforts, gain experience with the technology itself and refine your team’s know-how, which can then allow them to tackle more complex AI challenges. I believe the three levels are the pillars to AI growth, so you might as well focus on getting small things right rather than aiming for bigger things and falling (very) short of your objectives.
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As SVP, Professional Services at BairesDev, Damian oversees the entire customer relations life-cycle, safeguarding the company’s operations.
