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AI In Software Testing

The Business Impact of AI

Artificial intelligence (AI) is the new black, the next digital frontier, the new electricity, the new paradigm, and most probably the most disruptive force in technology in the coming decade. This list could go on forever. However, before we get too excited, let’s come back down and check the status quo.

Honeymoon

Forrester (Nov. 2017) stated that the honeymoon for enterprises (naively) celebrating the cure-all promises of (AI) is over because success with AI technologies isn’t easy. Well, that’s (obviously) no surprise, and in light of how some organizations are advertising AI, this prediction is bound to stand true. As such, it will be good for these companies to learn from history and adopt a more pragmatic as well as honest and realistic approach to AI.

Bottom Line. We need to sort AI facts from AI fiction so we don’t get fooled.

That’s the reason we compiled this article. It is meant as a helper to navigate the bullshit, since we don’t believe something is good just because it’s is trendy (Donald Knuth). We absolutely see the enormous value of AI, but we value good AI technology over popular AI technology.

Reality

Let’s first check reality. Forrester stated that 55% of firms have not yet achieved any tangible business outcomes from AI, and 43% say it’s too soon to tell. The wrinkle? AI is not a plug-and-play proposition; success at AI means hard work. So, unless firms plan, deploy, and govern AI correctly, new AI technologies will provide meager benefits at best or, at worst, result in unexpected and undesired outcomes.

According to Teradata (Oct. 2017), almost all (91%) enterprises expect to see barriers to AI realization when trying to implement it across their business. Lack of IT infrastructure and lack of access to talent lead the challenges and so represent the most significant barriers. In addition, the lack of c-suite understanding, their support for AI as a business capability, unclear business use cases, and a scaling strategy for AI are barriers that usually block AI adoption in an enterprise (Wolfgang Platz). That’s the deadly truth of AI in enterprises.

That sounds like an AI winter, but don’t panic! This doesn’t imply that AI won’t fulfill its promises; it’s just tough to put AI into practice today. As such, many enterprises are still in their experimental phase and so are asked to deal with hard facts to succeed at AI. It seems that AI has moved too fast into enterprises. So, how do we capitalize on AI? According to Wolfgang Platz, the following actions must be taken.

  • Start top-down with your desired business outcomes. Focus on them with a targeted heat map, and deliver incremental capabilities.
  • Culture. Build a culture in which personnel learn fast and pivot. Bear in mind that bad things happen. Accept that, and plan and build for it.
  • Increments. Pay for your success; don’t pray for it. Make small bets that deliver in weeks, avoid big bang deployments, and doubt vendor marketing claims.
  • Foundation. Establish an AI business foundation. Run multiple parallel tracks of production and innovation, and build a center of excellence for skills and processes.
  • Partnering. Build an AI ecosystem, not just an IT platform, and partner smartly with both nimbler and large companies.

Will AI solve your problems in 2018? Not likely, according to Forrester. It seems that the Tesler Theorem still holds true for enterprises: “AI is whatever hasn’t been done yet.”

Bottom Line. Putting AI to work to achieve tangible business outcomes remains the main challenge for enterprises, mainly because of the talent gap.

Fear

Is this the final diagnosis? Surely not! This doesn’t imply that AI won’t continue to evolve. It will continue to evolve at an ever-increasing, rapid pace in 2018. There will just be less hype (and hot air about AI) and a lot more action, according to Forbes (Dec. 2018). For AI to succeed, the hot air about AI has to come down to earth and get to work, according to PwC’s AI predictions for 2018.

So, beyond the declining hype and decreasing media attention in 2018, all the indicators (outlined below) show that investment in the development and integration of AI technology is continuing to increase in scale. The reason for this (obviously) needs no comment – but since comments are free and facts are sacred, let’s talk about the facts: It’s the fear of losing out! It’s the fear of becoming second class. It’s the fear that companies will be disrupted by innovative AI technologies from their competition. This fear fuels investments in AI, so these investments will continue to massively increase.

According to Statista (Sep. 2017), about 84% of enterprises believe that investing in AI will lead to greater competitive advantage. The reason for this is obvious. Companies (e.g., Amazon) with systems of connected intelligence and engagement get superior returns, just like smarter, more informed people tend to do better in life.

In addition, 75% believe that AI will generate new businesses while providing competitors new ways to gain access to their markets. So, expanding into new markets while protecting existing markets is the major challenge. The deciding factor isn’t AI technologies themselves but the data. Data is the new oil, so we expect a bloodthirsty war over data between traditional enterprises.

Finally, 63% believe that the pressure to reduce costs will require the use of AI. Hence, driving advances in AI to innovate at almost all costs has become imperative for most businesses (small, medium, and large).

Bottom Line. We expect less hype around AI and much more action in the near future. Investments in AI will grow constantly and rapidly.

How many enterprises are currently investing or planning to invest in AI in the near future? A survey by Narrative Science (Jan. 2018) found that 38% of enterprises implemented AI in 2016. This number rose to 62% in 2017, and according to Forrester, it will increase to about 70% in 2018. Forbes(Oct. 2017) stated that 80% of enterprises have active AI in production today.

Although predicting the future is difficult (Niels Bohr), analysts seem to agree. So, all in all, we can safely conclude that more than two-thirds of all enterprises worldwide will have invested in AI by the end of 2018. As such, these statistics illustrate that AI is being widely used today.

Bottom Line. AI is here and now, and adoption is rising.

Industries

This might suggest that every industry will have invested in AI in 2018. So, let’s check the stats. According to a study carried out by Spiderbook (Q4, 2016) in collaboration with O’Reilly, almost no industry is not investing in AI. This was already the case in 2016! As one would expect, the largest share of AI was (and most likely still is) used by the software and information technology services industry (32%).

Of course, there are differences by industry. If you’re a high-tech company, you’ve probably been doing all this for quite a long time. If you’re in manufacturing or healthcare, you are probably less developed. The difference is quite striking, but that was not the key finding. The key finding was that in every single industry, including traditional industries (e.g., government), companies are investing in AI – and the investments are huge. This has been confirmed by Tractica (Q4, 2016).

AI will expand its reach over almost every industry. It was found that advertising, business services, finance and investment, media and entertainment, and healthcare are some of the key industries that are likely to see major transformations and opportunities. Until 2025, consumer and defense sectors will continue to be major AI markets.

Bottom Line. AI will remake every business in every industry. Professionals from across the tech industry foresee widespread AI disruption.

Investments

How much is invested in AI? McKinsey (Jun. 2017) reports that tech giants (e.g., Baidu, Google) spent between $20B and $30B on AI in 2016, with 90% of this spent on research and development and about 10% on AI acquisitions.

AI investments have turned into a race for patents and intellectual property (IP) among the world’s leading tech companies. Private investors are jumping in, too. According to KMPG (Q4, 2017), venture capital investment in AI doubled from $6 billion in 2016 to $12 billion in 2017. McKinsey estimated that machine learning attracted almost 60% of that investment. The biggest AI-focused deals were Nio ($1 billion), Face++ ($460 million), and Indigo ($206 million). AI has also made its way into private equity.

Also, other external investors (e.g., angel funds, seed incubators) are becoming more and more active. These venture capital investments sound impressive (and indeed they are), but note that the investment in AI is relatively small compared to the total venture capital market, which reached an all-time annual record of $155 billion in 2017. Hence, AI attracted about 8% of all venture capital investment in 2017. McKinsey stated that information technology soaked up about 60%.

Revenue generation (apart from cost reduction) seems to be the main reason for these massive investments. For example, Netflix has approximately 100 million subscribers worldwide, and the majority relies on the streaming service to recommend titles to watch. So, by providing better search results, Netflix estimates that it is avoiding canceled subscriptions that would reduce its revenue by $1 billion annually.

Bottom Line. The investment in AI is enormous but still not in its golden age.

Market

How big is the market? It’s huge! Statista (Sep. 2017) predicted that the market will grow from $4 billion in 2018 to $60 billion in 2025, attaining a compound annual growth rate of about 53%. The largest proportion of revenues comes from the AI for the enterprise applications market (approx. 50%).

Nevertheless, McKinsey (Jun. 2017) stated that analysts remain divided as to the potential of AI. Some analysts have formed a rosy consensus about the potential of AI, while others remain cautious about its true economic benefit. This lack of agreement is visible in the large (almost colossal) variance of current market forecasts, which range from $644 million to $126 billion by 2025. This variance might indicate that either we are witnessing another phase in a boom-and-bust cycle or that analysts are just flying blind in their decision-making. Nobody knows, since even experts have a hard time understanding and tracking progress across the field.

Bottom Line. AI market forecasts must be taken with a pinch of salt.

Opportunity

Why should you care? A great resource to get the answer is the AI Index (Nov. 2017). It’s an initiative to track, collate, and visualize data related to AI. This report shows that the number of active US startups developing AI systems has increased 14x since 2000 and that the share of jobs requiring AI skills in the US has grown 4.5x since 2013. In addition, it was found that the number of job openings in the US asking for AI (e.g., machine learning, computer vision) skills more than doubled from 2016 to 2017.

So, what does this mean for you? No one can tell you with absolute certainty what will or is likely to happen to you because of AI, but we do know that AI is already displacing workers. This doesn’t sound like a glowing future, but bear in mind that AI will also create new jobs. According to Gartner(Dec. 2017), AI will actually create more jobs (2.3 million) than it eliminates (1.8 million) by 2020. Healthcare, the public sector, and education will see growing job demand through 2019, while manufacturing will be hit the hardest.

To understand this, we just need to study the past to define the future (Confucius). According to PwC (Q4, 2017), every (digital, mechanical) disruption resulting from technological innovations establishes markets that did not previously exist. For example, the creation of the Internet opened the way to e-commerce. The invention of personal computers created the possibility of connected computers, and the advent of cloud computing paved the way for applications in IoT.

There is no reason to believe that AI is an exception to this rule, so we are convinced that AI will create markets that wouldn’t previously have been possible or even imaginable. We see the rise of AI more as an opportunity than a threat for both enterprises and individuals. Don’t get us wrong. We are neither optimists nor pessimists but realists. So, take the risk or lose the chance. Ergo, yes, you should (at least) care.

Bottom Line. AI is more an opportunity than a threat. Don’t see the difficulties; see the opportunities, accept the challenge, and take the chance. AI is not only an imperative for enterprises. It’s also a business imperative for you.