We all have heard about AI by now, it is stack of capabilities, put together to achieve a business objective with certain capacity for autonomy, ranging from expert system to deep learning algorithms. In my several conversations, I have found myriad of uninformed expectations from businesses on what they think of AI and what they want to achieve from it. The primal reason that IMHO is happening is limited understanding and technology landscape explosion. Such a radical shift has left businesses with imperfect understanding of the capabilities of AI. While it is tempting to point out the challenges that businesses are facing today, it is important to understand the core problem. One of the company executive (lets call him Steve) put it the best, “in today’s times AI is pushed right in the 2nd sentence of almost every product pitch and almost every vendor is trying to sell with rarely anyone trying to tell. But most are unclear what are they doing with their AI and how it would affect us.”. This hits to the nail of the problem.
Due to buzz in the market and push from top software companies to push their AI assistant to consumer, market is exploding. This widespread investment and media buzz is doing a great job at keeping the business anxiety high. While this is tiring for businesses and could potentially challenge their core strength (if not understood properly), businesses need to respond to this buzz word as an Innovation maverick. Hopefully weâll talk about it below. Still not convinced to investigate AI adoption and need a reason? There are reportedly 35.6 million voice activated assistance devices that would make their way into American homes. That pretty much means that 1 in 4 household has an AI Assistant (total of 125.82 million households). This massive adoption is fueling the signal that everyone should consider AI in their corporate strategy as AI is slowly sliding into our lives and our work would be next. After all, you donât want to lose your meal to an AI.
So, hopefully you are almost at the edge of being convinced, now what are some of the considerations that businesses should remember (almost always) and use them to build some ground rules before venturing into high dose of AI planning, execution & adoption.
AI is no silver bullet
While AI is good for lots of things, itâs not good for everything. AI solutions are great at clustering (likely events), predicting future (based on past) and finding anomalies (from large dataset) but they are certainly not great at bringing intuition to the table, quantify & qualify culture. They are still lagging to provide trusted results when they are equipped with underfitted or overfitted models. AI solutions are amazing at normalizing the data to predict the outcome, which many times leave the corners unseen. AI also has bias problem that humans have been mastering for ages. So, go with AI but keep critical decisions around best intuitive algos with who could do it the best, yes humans.
eAI (Enterprise AI) is in its infancy, so donât yet give launch code to the kid
I am a South Asian, and sometimes when I am in my Indian-mode, my Indian accent jumps out and my interaction with Siri, Alexa and Google Home turn into an ego fest between what AI thinks I am speaking vs what I am speaking. The only difference is that AI holds more power in those interactions. Which is not yet scary, but I am sure it could be. If you have interacted with your AI assistance toys, you could relate to the experience when AI responds/reacts/executes due to misinterpretation. Now assume when consumer toys are programmed to react on misinformation, sometimes enterprise solutions could also suggest some fun and bizarre recommendations. Donât believe me? Read my previous blog: Want to understand AI bounds? Learn from its failures to learn more. So, itâs important for businesses to understand and create the boundaries of AI and keep it air-tight from your critical decision-making.
Focus on the journey and not the destination
Yes, I know you have heard about this before in almost every challenging streak you are about to take. We have also heard about the same quote with âJourneyâ and âDestinationâ reversed. But I like the previous one. It puts emphasis on learning from this project and prepare decision makers to not rely on these technologies without a robust and fail-safe qualifying criterion. Every step, every learning, every use-case (intended & un-intended) must be captured for analysis. Most successful deployment stories I have heard are the ones where AI led the ROI hockey stick from unexpected corners. So, businesses should always provision for ears that must be listening to those unexpected corners. One of the most challenging conversation I find myself in contains a clearly defined uptight goals with no room for change. We need to achieve X by Y. While this is music to corporate ear, this is a headache for new untested waters. Imagine jumping in a viscous slurry to get to other corner in 10min. Sure, you may or may not get there, but then you’ll be too focused in getting to the other side and not focused enough in finding hacks to get you through the slurry faster the next time.
Building up on the foundation is critical
Yes, letâs not talk about changing laws of physics. Wait, let us change the laws of physics but give respect to the fundamental laws. It is important to see fundamentals REALLY fail before we try to ignore them. Avoid going against gravity, but it should be allowed to experiment with it. Businesses exists because of their secret sauce: part culture, part process and part product. While it is very tempting to break the legacy and build it fresh, it is extremely painful and time consuming to make different aspects of business work in harmony. Ignoring the old in front of the new is one of the most under estimated and freakishly devastating oversight that innovation could put businesses through. Imagine a newly launched rockstar product and how everyone jumped to work on it. While it is cool to work on new challenges, it is critical to establish their compliance to the fundamental foundation of the business. There is no silver bullet as to what constitutes the foundation, but itâs a deep conversation that business needs to have before venturing into self-learning, self-analyzing and self-reacting solutions.
Donât rush to the road
I have a young child at home and I remember getting in a conversation with an executive about the young ones and AI (Wait, this could be a great title). Idea that you wouldnât trust your young ones with the steering of your car on road without much practice and / or your confidence in their abilities. You would rather have them perfect their craft in the controlled backyard, instead of getting them on highways early. Once they are mature and sane, yes, now is the time to let them drive in controlled roads and once confident, go all in. Current AI capabilities are no different. They require lot of hand-holding and their every outcome hits you with amusement. So, understand the play area for AI deployment and build strong ground rules. You donât want anyone hurt with overconfidence of a minor. So, is the case with these expert systems.
Time to get some meetings with your innovation team (if you have one yet), else time for you to create one. We are currently working on tech stack where most of the technologies are undergoing disruption. The time is excitingly scary for tech folks. As tech is now substitute spine of any business, and yet undergoing disruption. So, tech folks should be given enough ammo to fail and they should be encouraged to fail. The scariest thing that could happen today would be a team executing a scenario and giving it a pass due to the fear of failure. There needs to be responsive checks and balances to understand and appreciate failures. This will help businesses work with IT that is agile and yet robust enough to undertake any future disruptive change.
Understand the adoption challenges
If you are hurt that we spend little time to talk about adoption, my apologies, I hope to hear from you in the comment section below. Adoption holds the key for AI implementation. While you are undergoing digital transformation (you soon will, if not already), you are making your consumers, employees, executives crazy with this new paradigm, so adoption of yet another autonomy layer holds some challenges. Some of the adoption challenges could be attributed to understanding of capabilities. From poor understanding of corporate fundamentals to inability to deploy a fitted model that could be re-calibrated once ecosystem sees a shift, the adoption challenges are everywhere.
So, while it is great to jump on AI bandwagon, it is important now (more than ever before) to understand the business. While IT could be a superhero amidst this, understand that along with more power comes more responsibility. So, help prepare your tech stack to be responsible and with open ears.
If you have more to add, welcome your thoughts on the comments below. Appreciate your interest.