A few years ago I saw this headline news flashing all over the internet.
Our Dealers are Missing UP to $18 Billion in Easy Sales
heT Chairman and CEO of Caterpillar suggested that the company and its dealers were losing $9 – 18 billion in easy sales revenue as their sales, both internal and dealer networks, weren’t monetising the real value of data.
He worried that..
They are not tapping into the wealth of real-time customer data now at their fingertips; they are not communicating with each other; and they are not providing customers across the globe with a consistent experience when it comes to everything from e-commerce to parts and services pricing.
Long story short, the whole idea was to convert the company’s mentality from dumb iron sales to data-driven, machine learning-driven sales.
So I read the press, thoroughly understood their strategy from their annual reports, read their investor decks and then eventually wrote to the CEO.
But first I organised his dilemma and linked it to the value loss he had alluded to.
The company had been scrambling for additional revenue to circumvent a volatile economy and demand. Let’s say, I had a bit of an understanding of the global steel market from my first startup when we helped the world’s largest steel company with revenues of ~$120 billion so we knew where the markets were heading. It was no secret!
After peaking at $65.9 billion in 2012, sales plunged nearly 16% a year later as capital investment by the global mining industry tanked as expected. More sales warnings were on the horizon.
I though it was time to map their defined global driver, which I’m sure a strategy consultant might have created for them, to data-driven goals and actions. Eventually in my letter to the then CEO, Doug Oberhelman, I suggested the following three actions:
- Include the overall ecosystem and partner network in your data-driven, AI transformation strategy. In fact, include them all and try to capture both machine-chatter and consumer-chatter in a unified console.
- If you don’t have an AI transformation program underway, involve all those 178 business from the start. Ask your CIO to get you a current-state analysis of where you are!
- Educate your partner ecosystem so they can effectively use the digital & AI technologies and platforms to make more calls and secure targets within their territories. Just knowing about those IoT-ready billion machine parts is not enough, dealers and consumers want to know only what applies to their domains and territories. In other words, contextualize that chatter!
I think since then a lot has happened within Caterpillar and I’m hoping that they have made some progress by now. My intention was to scratch the surface and see if there was a way to start mapping and identifying potential data-driven MVP (minimum viable AI projects) where machine, and deep learning, could be potentially applied to create services, solutions that could help both bridge the gap as well as build new bridges to the additional revenue stream that the CEO was hoping for.
So there is more than a dime to make out of data, that’s for sure!
Need for a Chief AI Officer? Not really, you just need a competent executive who is business savvy and technically sophisticated about machine and/or deep learning technology!
Many organizations do the top-down strategy where they hire strategy consultants. This involves a thorough temperature check into various aspects of their organization such as finance, marketing, sales, procurement, legal, IT, M&A, R&D, etc. and return back with multi-dimensional state analysis.
Top down-strategy essentially reports how far separated the firm is from being a data-driven AI company.
Top-down strategy is then translated to various domains, budgets are handed out and this trickles down to a multi-year program that eventually morphs into something great – if the firm has dedicated focus and is resilient to executive succession, or goes into Project Death Valley if folks start running around without goals, milestones and talent.
Bottom’s up journey is driven by domain experts in marketing and sales or machine learning divisions who see value in AI and go with it!
Read the source article in Forbes.