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For decades, the ability to achieve economies of scale and grow through acquisition has been the not-so-secret sauce many industry leaders have followed to drive business performance and market share.
It’s a basic recipe, really. You develop products. Bring them to market. Build a sales force and channel organization. Give them more products to sell. Identify market demand for capabilities you’re not offering. Partner or acquire a company to add them to your portfolio. And so on, and so forth.
When done well, this formula can deliver fast-and-furious growth for organizations. Indeed, by sticking with it for decades, some have even become huge conglomerates. At some point though, the model begins to falter and even fail. Companies become too complex. Too slow moving to keep pace or adjust to market trends. And they often find themselves leapfrogged by more nimble competitors. Many stab at turning themselves around through diversification or acquisition. Few succeed.
Businesses in this predicament must then ask themselves two fundamental questions: Do we streamline and refocus on the products and services that made us great in the first place? Or do we invest in digital technology that will allow us to conquer any complexity holding us back while accelerating growth?
While traditional organizations often view the former as the most prudent approach, it rarely returns companies to greatness. That’s why more progressive companies – especially those thinking beyond the COVID-19 era – are adopting technologies such as cognitive automation to get them to the next level.
Cognitive automation utilizes artificial intelligence (AI) and machine learning (ML) to provide end-to-end, real-time visibility across an enterprise organization’s operations. One of the challenges many companies face is, the larger they become, the more operational and customer data they have. In mature organizations, this potentially useful data may be all over the place – in disconnected ERP and CRM systems around the world. This makes it difficult to find, aggregate, index, analyze. And it’s almost impossible to fully utilize it to advise the thousands of decisions businesses need to make every day.
This represents significant missed opportunity. All of that data, if harmonized and acted upon intelligently, could dramatically optimize manufacturing, inventory and supply chain efficiency. It could improve demand forecasting and spot hidden market trends to advise product development and marketing. What’s more, it could democratize access to data, giving every authorized employee the means to make faster and smarter decisions that contribute to the company’s overall success.
Technologies such as cognitive automation not only facilitate this but make useful real-time recommendations. They assume time-consuming tasks and thought processes that normally lead to a decision so employees can focus on more strategic activities. In short, they shift the model from people doing most of the work to one where software does much of the work and augments decision making.
It may sound fairly futuristic. But enterprise organizations have been heading toward this for a while now. In fact, according to Gartner, 48 percent of companies surveyed said they were either deploying or piloting decision-augmentation technologies. In addition, large firms with revenues of $1 billion or more have set aside an average of about $5 million to invest in a balanced portfolio of AI technologies and use cases across functions, the analyst firm said. This trend has even continued through the global pandemic, with a recent Gartner survey finding 47 percent of respondents’ AI investments unchanged and that 30 percent actually planned to increase such investments.
If the corporate experience of the past several months has taught us anything, it’s that we are operating in a far more volatile global economy than at any time in recent memory. With little warning, companies may have to completely reimagine their business models. Rationalize real estate. Right-size production. Streamline supply chain processes. And understand constantly shifting customer sentiments.
These pressures mean the average company needs to be more agile, adaptable and resilient than ever. They must be able to constantly capture market signals and react to what the data is telling them faster than humanly possible. And today, the only way to do that is by establishing a self-driving enterprise.
Self-driving enterprises run on cognitive automation platforms that understand how your business works and can answer many of the questions employees may have. They make real-time recommendations about how to improve operations, predict business outcomes, and take certain action autonomously.
Think about it as Google on AI steroids. The system crawls billions of rows of data in CRM, ERP and most other transactional systems companies might have. It then creates a replica of all that data in the cloud, harmonizes it and retrieves the pertinent, real-time business metrics individuals need to fully understand their business. It also builds a permanent memory of every decision and resulting outcome to improve future processes.
The good news for enterprise organizations is they do not have to rip-and-replace legacy systems to take advantage of self-driving enterprise features. Rather, they can incrementally install chunks of software to create a “cognitive layer” on top of existing transactional systems. While the solution continually extracts input data for real-time analytics, it does so with minimal impact to underlying systems. All data, meantime, is encrypted and securely transferred to the cloud.
For users, the system is about as simple as it could be. And considering the complexity of things it’s handling, that’s phenomenal. You could quite literally pick up an iPhone and say, “what’s my forecast?” And in the blink-of-an-eye, an Alexa-style voice might let you know where things stand and even offer to break down the numbers for you. If you happened to be on a PC or Mac, meantime, you’d be able to receive timely information and personalized insights along with suggested courses of action.
These are the types of capabilities enterprise organizations need in order to adapt and remain resilient when confronted by endless change and challenge. The larger a company becomes, the more vulnerable it may be to economic or market volatility – not to mention upstart competitors nipping at their heels.
For these businesses, there is no time to waste. By embracing cognitive automation and becoming a self-driving enterprise, companies of all sizes can beat down adversity and pave the way to prosperity.
1. Gartner, Artificial Intelligence Trends: Decision Augmentation, Jan. 13, 2020
2. Gartner, Hype Cycle for Artificial Intelligence, 2020, Svetlana Sicular, Shubhangi Vashisth, July 2020
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