Author: Todd Donzelli, the North America Technology & Innovation Strategy Leader at Infor.
It’s time we had a serious business discussion about artificial intelligence. Over the last few years AI has become the dominant buzzword across corporations, industries and boardrooms with everyone trying to figure out how to leverage this new technology to improve business outcomes.
It feels like we’ve been through this hype cycle before, discussing other new and innovative solutions such as blockchain and even cloud computing. Sometimes a new technology can have an immensely powerful impact as we saw with cloud computing. Other times a new technology simply can’t find a home or a real purpose in the world of business; see blockchain. I feel like we are at the same moment with AI right now. While we are all in love with the potential that this technology may bring for us most of us are struggling to understand how to make it practical and useful in our day-to-day business lives.
As I speak with executives and leaders throughout different industries, I hear 3 common and recurring themes:
“We don’t understand the AI landscape.”
“We don’t know where to start.”
“We don’t have the talent to begin.”
If you find yourself having these same thoughts or concerns or hearing these phrases uttered in meetings, you are not alone. Most business leaders struggle with some or all these issues. I’d like to offer my thoughts on how to address each one of these.
“We don’t understand the AI landscape.”
Many senior executives I’ve spoken with have told me that they’ve been asked by their board of directors “what is your AI strategy?”, somehow implying that there is a single monolithic capability known as AI. The reality is artificial intelligence is more of a domain than a single solution. Understanding the nuances and facets of AI can help you determine a strategy and a plan to make this technology practical and useful for business. There are three main areas of business function that align 2 distinct AI driven capabilities.
The first and I would argue the most impactful is the concept of predictive and prescriptive insights. Using AI to identify trends and patterns in data that lead to insights, predictions and prescriptions to augment the human decision-making process. Leveraging an AI concept known as Machine Learning (ML) this capability processes vast amounts of data, uncovering those patterns and trends invisible to the human mind. I believe this is also AI’s most powerful and practical capability for business leaders today. The single most important responsibility and value that leaders bring to an organization is the ability to make the right decision at the right time. Providing leaders an increased level of confidence based upon facts and data can greatly improve the decision-making process, having both top line and bottom-line impact for your business. This area of artificial intelligence provides both the largest opportunity for return on investment as well as demonstrable and measurable results.
The next area of AI enablement is the world of business and process automation, leveraging artificial intelligence aligned with other automation technologies to remove inefficiencies and labor-intensive tasks across the business environment. In this area efficiency is the name of the game with the benefits being measured in bottom line cost savings and margin increase. While not as impactful to your overall business, process automation can and does deliver effective measurable returns.
The last area of AI acceleration is improving workforce productivity and creativity. This is the domain of generative AI. I am sure we are all familiar with GenAI’s powerful capabilities to examine synthesize and create amazing new content. Those who have played with any of the open-source GenAI tool sets can attest to its capabilities. However, it is not without its challenges, limitations and drawbacks. Essentially GenAI is designed to make guesses, to infer, and to create something that did not exist before. By definition that means that it is not always going to get things right, hence the disclaimer on most commercial GenAI applications that you should always check the results for accuracy. It’s been even known to hallucinate, coming up with wildly inaccurate results that are essentially unusable. No one disputes the power and potential of this capability, but I suggest approaching GenAI through the lens of a business tool with caution. As this technology matures and becomes more reliable, I see significant upsides from a practical business perspective. However, we are in early days and anyone who tells you that this new capability is the solution to all your problems should likely be shown the door. Finally, from a measurable and impactful perspective GenAI and its ability to increase the creativity and productivity of your workforce is still difficult to quantify, leaving it as one of the most difficult to measure investments to make.
As you can see, artificial intelligence is not a simple or single capability but rather a multi-faceted domain of different technologies each with its own value and impact. Now that we’ve defined the different facets of AI domain, talk about the next challenge, which is where to start.
“We don’t know where to start”
As with any 2 technologies you do not simply roll out artificial intelligence into your organization. Rather businesses explore and experiment with this capability. However, according to a study last year from the CompTIA AI Advisory Council, nearly 80% of AI projects do not scale beyond proof of concept or lab environment. Most often that’s because they begin as an experiment in a lab, generally in IT, with an attempt to understand how the technology works rather than identifying a business problem or challenge that needs to be addressed. This is the proverbial building of a hammer and the search for nails. Companies who have successfully deployed AI in their organizations have begun with the quest to answer a question or solve a problem impacting their business, then determining whether the capabilities of artificial intelligence and insights from their own data can help them. Always starting from the context of business challenges and value-based outcomes will help you measure the true success of any technology.
“We don’t have the talent to begin.”
It is understandable. Not many companies do. AI is new to all of us. Recruiting, hiring and retaining talent in this developing space may be the most difficult challenge of all. However, if you are able to find solutions that combine AI technology, industry specific business processes and people with this unique set of skills, you can not only accelerate your journey into the practical application of AI, but you can also confidently focus on solving real business challenges.
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