Anita Samojednik is CEO of Paro, a startup disrupting the way companies access on-demand financial expertise
The pandemic forced the hands of many executives who had previously hesitated to sponsor transformation; many leaders needed a disruptive event like this to prioritize such activities and think outside the box. It’s been surprising to see the speed with which digital transformation has finally taken place, particularly within industries where companies are traditionally more risk averse.
As a logical step, businesses are now looking to AI to play a key role in their business model. However, the reality of AI is that only a select few have found a way to effectively operationalize it. In fact, a recent McKinsey survey of global companies found that as of 2020, only 15% of respondents “are scaling automation techniques across multiple parts of their business.” The survey also showed that one-third of respondents said their machine-learning algorithms are still at the pilot stage. So, even though the last few years have forced organizations to adopt new technology, there is still significant blue sky for leaders when it comes to AI and how it can help them drive business decisions. For businesses considering the jump, there are a few considerations to keep in mind.
Is AI Right For You?
While AI has certainly gained popularity over the past couple of years, when considered among other business technology like touchscreens and voice recognition, only 2.8% of businesses have adopted machine learning — the technique at the heart of modern advances in AI. This begs the question as to why? While small businesses might experience hesitancy in using AI given affordability or security concerns, businesses with adequate datasets and funds to support an AI investment can benefit from AI if they’re looking to expand their business into new markets or want to evaluate historic data to predict new trends.
This setup is ideal if you are a leader in an industry that experiences frequent tech-driven disruption, shifts in consumer patterns or whose success relies heavily on a positive customer experience. For example, let’s consider a customer success manager who needs to manage hundreds of clients with unique sets of needs that can’t easily benefit from a one-size-fits-all approach. There’s no way to give your personal touch to all of them, but using AI can help prioritize that time and make the proper recommendations. This tool has the ability to make accurate and tailored predictions on what’s best for each business so that managers can still maintain those client relationships despite the volume.
Data-Driven Decision Making
Just as AI technology can steer the customer experience, it can also be used as a tool for leaders to steer business decisions. Allowing AI to play a role in business thinking can guide leaders to make data-driven decisions toward growth and lessen the unpredictability of the result. Most executives look at the past to determine the clients they target and the services they offer. Essentially, they use their data to determine what has already been successful and how to continue replicating that success.
When utilizing AI in this respect, you can look at market signals and use those intent signals to target and source ideal clients and existing services that would be a value-additive match for them. Going a step further, those signals can also serve to identify not-yet-existing services that could potentially drive increased revenue.
Once AI systems are in place, leaders should use them to understand:
• What happened in the business yesterday?
• Is there something about the business that I don’t know, but should?
• What should I do to predict the future of my business, and how should I get there?
But business leaders tend to fear AI for two major reasons: They don’t understand its capabilities, and they’re concerned the technology might replace the human component in the workforce. While understandable, I believe that to truly comprehend the use of AI is to appreciate that technology does not operate effectively without business acumen to guide direction and implementation. Human involvement is irreplaceable. In tandem with human involvement, AI can be a core value driver for businesses on their path to innovation.
Building The Right AI Team
Indeed, the human component is just as important to another element of AI implementation success: building the right tech team. Businesses should not only be investing in people who are technically savvy (e.g., data scientists, data engineers, etc.) to get the job done. That same talent must also be able to translate AI’s potential capabilities to technically inexperienced leaders and stakeholders. This will allow for alignment across the business, for operationalizing data can’t fully prosper in a silo.
Leading the charge, I recommend a chief technology officer (CTO) who has experience spearheading and developing a team with business needs in mind. An experienced CTO should work closely with the CEO to identify areas where the wide-scale deployment of AI can be most impactful. Additionally, business leaders should look internally at the existing talent within their organizations to find opportunities to educate and upskill current employees on AI’s applicability and, in turn, democratize its use.
Finally, your tech team must be able to communicate changing capabilities as the technology continues to advance and the business evolves. As long as business leaders remain tech-agnostic and keep an open mind to the benefits of these technologies, especially as they become more accessible, the blue sky’s the limit.