The COVID19 outbreak has changed the world faster than anyone could have imagined.
Forced isolation has shifted meetings and activities to go on through web collaboration tools.
Arguably most importantly, the virus has ground the entire economy to a halt. For some industries (travel, retail, food services, fitness), unemployment is already rampant, and for more remote-friendly industries (tech, media), work may go on, but sales are slowed or frozen, and receivables less likely to convert to revenue than ever.
The downsides of the virus are easy to see – but business and public sector leaders are forced to think about the silver lining. Not just how they can survive this downturn, but how they can emerge stronger and more resilient from the rubble.
Finding the Upside
Outside of remote collaboration tools (Zoom stock was $69 at the start of January this year, and just hit $130 today) and eCommerce (Amazon announced it would hire another 100,000 workers to ramp up its services in this time of need), finding an advantage in this time of crisis isn’t easy – but those who are getting creative are doing so in terms of their business model, and their technologies.
The Problem: Leaders don’t know where to focus their AI efforts in the middle of (or directly after) this crisis.
The Solution: Knowing where to invest in risk-reduction, and which AI applications are easy enough to deploy to make a difference when it counts.
In this article – the first in a 3-part series – we’ll be exploring how the artificial intelligence priorities and strategies of companies are already changing.
Fortunately, here at Emerj, our market research goes on – working remote or not. Our AI Opportunity Landscape data is already telling a story of how AI startups and enterprises are adjusting their strategies. With thousands of business leader subscribers, and hundreds of expert AI researchers, AI founders, and enterprise AI leaders – we’ve been fortunate enough to gather a lot of perspective quickly on how AI priorities are shifting.
Over the course of the series, we’ll be releasing more and more about what we’re learning in terms of new AI priorities, risk-related use-cases, and strategic advice from leaders. This week we’ll kick off by getting a sense of what we’re seeing as the biggest changes underway – and the immediate moves that leaders should be making to allocate AI innovation budgets more effectively.
Changing AI Priorities in the Enterprise
Some of the changes in the immediate wake of the COVID19 pandemic are already visible – and as sentiments shift among AI vendors and enterprise innovation leaders, some kinds of AI projects and initiatives will become less important, while others become more important:
During a recent episode on our AI in Business podcast, IBM’s Rashida Hodge used the analogy of “technology tourism.” Previous podcast guest and AI innovator Dr. Charles Martin has used the term “toy applications.” The idea is the same: AI use-cases that are undertaken out of curiosity or out of a desire to appear hip and modern, with no genuine understanding of its strategic value, and no plan for actual deployment.
These kinds of proofs-of-concept (PoCs) and pilots are already a disaster in terms of wasted enterprise resources, but now enterprises have more pressing issues to deal with. Many of these novel PoCs are likely already being cut off entirely amidst the pandemonium of the virus. Real, strategic priorities are emerging as the only thing worth investing in – and those priorities have changed rapidly for the vast majority of firms.
What this means for AI startups: Startups either need a value proposition that can frame them as a solution to this crisis, or they need to sell to enterprises who are not hit exceptionally hard by the virus, and frame their value proposition as an enhancement, as a way to pull ahead of the industry at a critical moment of opportunity. Many AI pilot projects will be cancelled, and we can expert AI vendor homepages to change rapidly as they find moneyed markets to position themselves into.
What this means for enterprises: Projects designed to “check the AI box” (yes, I’ve heard enterprise leaders be that frank about their silly projects they’re using to maintain appearances) will be eliminated. Innovation and strategy leaders will need to frame all of their AI initaitives as efforts to either:
- Accelerate digital transformation. The Critical Capabilities required to fruitfully use AI in the enterprise require data access, harmonized data, data infrastructure, and new standards for teams/talent. Well-thought-out AI projects focus not “ROI” not only in terms of immediate economics, but long-term “unlocking” of agility and capability.
- Reduce near-term risk. There are a handful of AI applications across sectors that are bound to play a role in immediate recovery and rebounding – and anything that helps to reduce additional near-term losses (i.e. fraud detection in insurance, predictive supply chain applications) may see a spike in demand when the immediate shock of the pandemic cools down.
Yes, business models will change from the coronavirus. The changes from this massive economic shutdown and social isolation will be lasting. Norms in politics, physical health, work culture, and technology adoption will all shift (I recommend business leaders read Politico’s collection of expert perspectives on the lasting changes of the virus).
That said, what was an intelligent moonshot six months ago is not a smart moonshot now. The world is different.
Previous moonshots were based on a business that was running with stability, with stable resource reserves, and a reasonably reliable forecast of the market, and a strong understanding of customer needs.
That’s out the window now. New strategies need to be drawn up – for AI, for technology, and for entire business models.
What this means for AI startups: “Sure thing” ongoing initiatives with enterprises are no longer sure things. Receivables are no longer reliable. Champions who brought the vendor in may now be “championing” entirely different priorities. Find the urgent need – have strategic conversations with existing clients and leads, and think about repositioning as part of the COVID pandemic solution. Past strategic alignment with clients shouldn’t be taken for granted.
What this means for enterprises: It’s back to the drawing board for AI and business strategy for most enterprise firms. Old moonshots may longer be priorities, and innovation budgets may need to be allocated in a radically different way. We’re already seeing this with our AI strategy advisory work:
- We’re helping one of our enterprise clients pivot their entire technology and AI strategy towards supply chain efficiencies and prediction.
- We’re helping a retail bank determine what AI pilot projects to cut, and which to keep, as they boil down their priorities.
I suspect that for the next 6 months, over half of our enterprise advisory and research work will anchor directly to COVID19 response.
Almost no company is keeping the tech and AI strategy they walked into 2020 with. New strategic considerations are imperative, and with that comes the need for a fresh perspective of what AI is capable of, and where it fits in.
More: Risk Reduction
“Stop the bleeding” is top-of-mind for enterprises, and businesses in every industry. While many AI applications require substantial time-to-ROI, many risk-related applications across the Emerj AI Opportunity Landscape show reasonably short time-to-ROI for a number of applications that might be more important now than at any point in the last half decade:
- Insurance fraud prevention: In times of recession, insurance fraud goes up. Using past patterns of fraudulent and non-fraudulent claims, insurance firms would be better able to filter for fraud immediately, using human investigation only when needed – and settling claims that need to be settled, fast. Keeping cash on hand and handling higher volumes of fraud may force technology adoption in this business process, and AI will undeniably play a major role in the future of fraud.
- Payment fraud prevention: As with insurance fraud, payment fraud and so-called “friendly fraud” (people making a purchase, then charging the money back but keeping the product) do up in times of recession. In many industries, payment fraud is an even easier application than insurance fraud when it comes to Ease of Deployment (one of the Emerj AI Application Scorecards we use to assess applications).
- Consumer behavior prediction: Amazon’s recommendation models aren’t recommending products based on what worked a year ago, they’re doing so based on what happened a week ago, or a day ago. Updating customer behavior models in retail means better being able to plan, to market, and to direct the supply chain. Travel, retail, and other sectors may see a surge of interest in applications that help them predict purchase behavior and customer activity more readily.
More: Proven ROI
Our AI Opportunity Landscape research focuses on finding the areas of strong AI ROI in a given industry – examining the full landscape.
Some of our larger and more R&D-focused clients use this data to determine acquisition targets, or to find blue ocean AI opportunities to expand their business.
Most innovation and strategy leaders we work with, however, use AI Opportunity Landscapes to find where ROI already exists – so that they can double-down on a sure thing. We suspect that even market leaders (the same group who once used our services for finding new markets) will be focused more and more on “what’s working now” as the primary reason to engage with us here at Emerj.
We’ll get more into high-ROI use-cases later in this series.
A quote from our interview with NVIDIA’s Jan Kautz (from our AI ROI interview series) applies well here:
I would suggest [businesses] to pick an area where they already have an existing system in place so that you can compare what the results of the AI system are and know if you are at least getting better results than the existing system.
An Imperative for Change
Some firms will say that they just don’t have any time to think about technology strategy.
In the next two weeks, that may be true. But over the coming month, differences in technology focus will come to define the future of entire industries, and will separate the relevant from the irrelevant.
- Innovation and strategy leaders who can communicate with company leadership and use new data and perspective to drive new initiatives will find a way forward through the crisis.
- Consultants who can actively inform their clients about smart technology and AI use-cases in this crisis will be able to stay relevant.
- Companies who slowly get back to the same “business as usual” will be outmatched by firms who have found new ways to drive efficiencies and new ways to serve customers (i.e. by firms who have taken this crisis as an opportunity for vital strategic change).