Artificial intelligence (AI) will be at the center of value creation in the future enterprise. It has the potential to augment and automate both physical labor and the knowledge worker. AI powered cognitive automation has the potential to make ordinary knowledge workers into super humans leading to superior performance, triumphing over human genius and activity alone.
However, enterprises should not focus on artificial intelligence alone if they wish to leap forward in cognitive automation, productivity, and competitiveness while staying relevant for attracting talent. There are many elements to consider for enterprises who wish to include AI in their operating model. What follows are seven reasons why enterprises must keep the bigger picture in mind, if they want AI to really drive business value:
AI without data is just a formula
“AI isn’t magic, it’s just maths – albeit really hard maths.” But without data, what is left of AI is just a formula. And AI algorithms are data hungry. To make accurate predictions – with 99.9% accuracy – AI needs exponential amounts of data. And when human lives are stake, anything less is simply not good enough.
Without data you cannot extract value from AI. If you do not own the data in your value chain, it must be purchased or otherwise acquired. Digital natives, companies that have built their enterprise around data, have an early advantage; think Netflix and Amazon. The nondigital native enterprise will have to catch up, requiring connections to dozens or hundreds of internal legacy systems as well as dozens of external sources, maybe even thousands in the case of IoT connections. The data model needs a read capability to feed up-to-date information to the algorithms and writeback capability to underlying legacy systems. A static data lake will not suffice anymore. As I heard a Chief Technology Officer mention: “Data lakes that are not dynamic and can’t operationalize in day to-day business become data graveyards.
AI not operationalized is a brain in a jar
Companies with advanced analytics and AI capabilities tend to employ groups of data scientists working apart from the rest of the organization, possibly for months at a time, before presenting results to stakeholders. Their algorithms need to be transparent, a glass box. Subject-matter experts in any function need to be able to interact with AI algorithms, change them, tune them, and operationalize them in a user-friendly way. AI needs to be plugged in to the operating model of the enterprise. Otherwise, you end up with a brain in a jar.
AI without a goal is like a self-driving care without a destination.
AI needs a problem to solve. A clear goal. Then, if the right algorithms are applied and clear rules or data is available, AI can solve about any problem. However, machines can solve any problem but they just don’t know what problem is the relevant one. When there is no goal, for now at least, AI wouldn’t really know what problem to solve.
What do we optimize in a decision, function, business or value chain? Do I maximize customer service or profit, minimize costs or something else? Goals can change during economic and product lifecycles and don’t always have to be logical. A business logically wants to optimize EBIT, but might decide to incur losses to gain market share. AI doesn’t know this without help to set a goal or a destination.
Artificial Intelligence without a goal is like an autonomous vehicle without a destination.
AI un-orchestrated might lead to a dead end
In future operating models, the business processes will be mostly digitized. We already see the rise of the “digital twin,” the automated process that exactly copies the steps from a human to analyze, decide, and act, whatever the functional area is. Companies must be able to digitize their processes, a vital prerequisite for any operating model that has AI at its core. There are many tasks, decisions, and actions to undertake to automate a business process from the knowledge worker. AI will not be necessary in every step along the way but needs to be guided with inputs, outputs and know when to act. Without smart process guidance, AI does not know which turn to take in an enterprise decision process, which might lead to a dead end.
AI without a human touch can get creepy
AI is good in logic because it is maths. It is not so good in being social and feelings. Things like feeling empathy, showing kindness, creativity and understanding social context and relations. The Microsoft chatbot Tay, famously went from an innocent chatbot to a bully (and that’s a euphemism) in less than 24 hours. AI without human guidance around your enterprise behaviours, values, purpose, morals, and ethics can get creepy.
AI is not always superior to Humans
AI is superior to a human in highly frequent, low costs, data driven, logical decisions and actions. But sometimes humans are simply better. Strategic decisions for example, are infrequent, with a high impact and complexity of relationships and interconnectivities. Humans are superior in these types of decisions.
Defining your vision, who you want to be as a business or understanding the social or cultural aspects of decisions and actions, can only be done when the human is leading, not the machine.
Before integrating AI in an operating model, enterprises need to understand when in a business process the machine is leading, when the human should be leading and when focus should be on human-machine collaboration. Organizational structures, roles & responsibilities and incentives must reflect this.
AI doesn’t change a corporate culture & mindset
For any significant change in an enterprise, a shared vision and purpose is prerequisite to embrace and sustain the change. This is not different for change where AI is involved. Digital natives, whose culture is already data centric and AI collaborative, have a natural advantage. Nondigital natives must drive this new cultural change from the top down.
While the right corporate culture can help, much is also dependent on the individual mindset of the employee. One can look at Garry Kasparov for inspiration: he is a highly motivated and extremely competitive individual. Although the world’s best chess player, he lost to a machine while the world watched and then proclaimed to be the first knowledge worker put out of a job by a machine. But he took an interest in AI and the value it can bring when the machine is working together with the human. Todays knowledge worker requires a similar mindset, to embrace human-machine collaboration.
Automation has been happening for hundreds of years. It has long been focused on improving productivity of boring, repetitive, or dangerous human activities. We now have automated production facilities, warehouses, and transport in our physical supply chain. The next step through AI is to automate the knowledge worker, either supporting or automating their cognitive tasks.
AI will enable cognitive automation. AI algorithms will gather, analyze, and interpret data, and make decisions and execute them. It will do so at a higher speed, larger scale, greater consistency, and precision, and with more endurance than any human is ever capable of.
But AI will not be able to do it alone!
If you want to learn how leading companies are implementing AI driven cognitive automation, join the cognitive automation summit May 25-26. You can register here