The coming decade will have no patience for businesses with old ways of working. Companies that continue to deliberate on adopting AI technologies will find themselves brutally snared between digital-born companies with inherent AI drivers and early adopters with highly operational AI, steadily losing customers over a widening gap in quality and intuitiveness of offerings. The time for organizations to be on the fence with AI is over.
The Covid pandemic has come when AI technologies have started maturing, with even scaled use-cases in several companies. Many businesses that had barely dabbled with AI as experiments before 2020 are now turning to rapid digital and AI-led transformations to tide over new economic challenges. As these small and local to large and multinational businesses accelerate their digital journeys, their capabilities have started to surpass their peers. Meanwhile, consumers with fast-growing digital savvy are increasingly choosing to interact with companies that provide more value, faster delivery, and a more intuitive purchase journey – all of which are enhanced by AI in a digital world. The AI Divide – that differentiates businesses that use AI intelligently versus those that don’t – is growing wider each day and will increasingly determine business success in every market and industry.
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To survive and thrive in the next decade, businesses are going to adopt AI. And to use AI tomorrow, you need to embark or extrapolate on your AI journey today—a journey that begins with a fundamental decision to remodel and repurpose your business with AI at the core.
Let’s take a look at what’s holding many traditional businesses back and how the hurdles can be overcome.
The Building (or Stumbling) Blocks of AI Adoption
Culture – There was a time when people said, “Culture eats strategy for breakfast, ” which implied that no amount of strategy change would work until you first changed your organization’s culture. But today, if you wait for the culture to change, there may be no breakfast left to eat! People often question their organization’s culture or their executives for failure to change. Remember, if your organization lasted and thrived the pandemic, there must be some goodness in it that has brought you this far. Why not focus on the good and use that as a starting point – How can you use AI to make your organizational strengths even more substantial? This way, you are augmenting what’s already there, and it’s easier because radically changing culture, attitudes, and perceptions is the most challenging thing to do in the world – and you can’t afford to let that process hold you back.
People – The people who enable your AI processes are the fundamental blocks of your AI journey. Currently, there is a talent war growing, and there are severe talent shortages. Companies need to adopt a multi-level strategy to build teams. Organizations need to take a multi-pronged approach, assign a champion to lead the organization’s AI strategy, hire individuals (only if there are no available resources in-house), and infuse the team with business team members, ensuring the culture and actual business alignments. However, it is not always essential or feasible to hire AI specialists for everything. Businesses must realize you cannot do everything in-house, and you should not reinvent the wheel. It would be best to have an ecosystem with partner organizations to support your business’s exact needs and nature. These partner organizations could either be strategic business partners, technology platform providers, academia, or industry cohorts. The important thing is to not allow a shortage of internal resources to become a stumbling block and to move ahead by building your ecosystem.
Technology – As a company, once you have made a fundamental decision to start actively adopting AI in your business process or model, the actual move to AI need not involve a complete one-time overhaul of systems or migration to one large platform that requires massive investment. Companies must take a “Lego approach,” that is, they must build their AI capabilities as a Lego structure within the organization. That way, you can quickly replace the systems or completely change your “Lego” structure pretty promptly and efficiently, and you don’t have to incur massive spending and drive wide-scale adoption all at one go. You could also leverage open source technologies like Python, R, Java, etc., making it easier to experiment and switch – unlike when you invest in a paid technology, you get tied to that company and are limited by what that company or technology can do. The key to technology adoption is to adopt a phased approach to derive the maximum business benefits that current technology can offer while keeping the door open to adopting new and evolving technologies in the future.
The other important thing is to avoid benchmarking with substantial companies like Google or Amazon because their entire scale and journey are different. You have to identify what’s right for your organization, what AI means for you and keep evolving that statement as your organization matures.
Data – When considering the prospect of using advanced analytics or AI, people often say, “we don’t have the data” or “we have bad data” to begin with. Yet, it’s rare that companies have no usable data. There is always enough data, to begin with. The real problem is when people have not identified the best use cases for AI or the most critical business challenges, to which existing data can then be mapped. It’s always the data inventory and data cataloging exercise that is vital for an organization to push forward. And you can always reach out to external consultants to do that for you.
Setting the Parameters of Success
Accountability – Eventually, all AI strategy starts at the top. And this means that the CEO, CAOs, CDOs, and CIOs must be held accountable for AI investments. As this is your senior leadership, there’s no one else within the organization who can assess the investments’ value and success. Also, a CEO may only think they are answerable to shareholders who may not think long-term, while the AI journey requires a short-, mid-and long-term approach.
One solution would be to have an additional board member or an advisor who focuses on this aspect. The other solution would be to have a ‘business performance scorecard’ that is entirely transparent within the organization, so the success parameters are well established and work as a clear guide and yardstick for the company’s AI initiatives. Most importantly, these parameters should provide room for failure because the trial is the only way to find what works best for your organization. And that brings us to ROI.
Return on Investment (ROI) – In my opinion, when you define your company’s budget for AI, at least half the budget should be allocated for ‘learning’ in the first couple of years. So, for instance, if you assign $100 for AI initiatives, you must expect ROI on only $50 and consider the other $50 as a “learning budget” that, in turn, helps you recover your additional $50. That sort of equal focus on immediate returns and continuous learning helps build a genuinely workable, valuable AI journey.
Today there are many well-recognized, proven use-cases in every part of the organization, from sales and marketing to supply chain, finance, HR, customer service, and others. And these are not small use-cases; many organizations have been able to scale them pretty effectively. All you need to do is begin somewhere.
2020 has proven that businesses cannot afford to be in the deliberation stage over digital transformation anymore. The time to wait and watch on AI is over. Start or perish…