Artificial intelligence is reshaping business—though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon. The technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable. The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade.

Yet, despite the promise of AI, many organizations’ efforts with it are falling short. We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process. Read More

#ai-first, #strategy

AI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap.  An estimated 80% of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product.  It’s clear that there is an intensively competitive market for artificial intelligence and machine learning specialists.  Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and “feature engineering.” Some analysts have even equated “AI talent” with such researchers.

However, AI talent goes far beyond machine learning Ph.D’s.  Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result. Read More

#ai-first, #strategy

In 2006, the most predominant form of digital social communication was still email, and AOL instant messenger. A decade later, things have obviously changed quite rapidly in the face of higher bandwidth, greater capabilities, and the explosion of social media. Software-as-a-Service is an area that is growing exponentially. The digital platformification of older industries, paired with new advances in the Internet of Things (IoT) makes the API space — the powerhouse driving Internet connectivity — ripe for investment.

APIs, or Application Programming Interfaces, are an important cog in this process, and the market surrounding them is thriving. As John Musser of API Science told us, their future ubiquity throughout our digital fabric is inevitable. APIs encourage standardization — have you ever used Twitter to log in to a third party application? They extend functionality so that more potential is at our fingertips — how often do you query a map embedded into a web application? By exposing assets to developers to create new apps with, APIs also inspire innovation, promote data matter experts, lead to creative projects, and subtly increase the end user’s experience.

The API space has produced an economy in it’s own right. APIs can be used to open new monetization streams alongside existing ones, but API-first companies have emerged that are entirely built around an API service. Twillio, Algolia, Contentful.com, and others are examples of companies that are exposing an API as their main product Read More

#ai-first, #strategy

In Part 1 we described these lessons:

• Information centric businesses are obvious targets. (E.g. insurance, mortgage lending, media, telecom, real estate brokerage).
• Since network size is the measure of success, it is more likely these will be B2C.
• Fragmented industries are good targets.
• Best of all find fragmented markets that are under served.
• Yes there is competition among emerging platforms so first movers who execute well are favored.
• Don’t wait to get started or you could end a commodity in someone else’s network platform.

Now let’s continue:

• In addition to fragmented markets, look for markets with a large imbalance of knowledge between buyer and seller.

Read More

#ai-first, #strategy

In the 1970s and 80s, investments in computer technology were increasing by more than 20% per year.  Strangely though, productivity growth had decreased during the same period. Economists found this turn of events so strange that they called it the productivity paradox to underline their confusion.

Productivity growth would take off in the late 1990s, but then mysteriously drop again during the mid-aughts. At each juncture, experts would debate whether digital technology produced real value or if it was all merely a mirage and that debate continued even as industry after industry was disrupted.

Today, that debate is over, but a new one is likely to begin over artificial intelligence. Much like in the early 1970s, we have increasing investment in a new technology, diminished productivity growth and “experts” predicting massive worker displacement . What’s different is that now we have history and experience to guide us and can avoid making the same mistakes. Read More

#ai-first, #strategy

As data scientists increasingly become critical resources in enabling companies’ exploration and exploitation of their digital resources, it’s also increasingly important that data scientists can provide accurate and focused business guidance.  If that seems like a mouthful, try these two scenarios.

Scenario 1: Joe is asked to recommend a portfolio of AI/ML projects that will improve performance, provide measurable ROI, and have relatively low risk of failure.

Scenario 2: Joan is asked to plan AI/ML projects that will maximize the value of the company, protect against competitors, and create the fastest possible market, revenue, and margin growth.

Pretty much any of us could do a competent job with the first scenario.  We’d look at the current business and try to find opportunities where traditional ML could be used like scoring, forecasting, or optimization.  If we’re sufficiently advanced we’d also look for AI opportunities like the application of NLP or image processing.  Since we weren’t asked to challenge the fundamental business model, we just looked to places where we could paste on AI/ML.  Read More

#ai-first, #strategy