Customer Experience Depends On Structuring People to Manage Information
I first heard about “it” in a Harvard Business article in 2016 and subsequently have been asked about “it” by clients and colleagues alike. “It” is yet another entrant into the C-suite. “It“ (actually a she or he) is a Chief AI Officer (CAIO). That’s right a human Chief Artifical Intelligence Officer (not a machine or robot occupying an executive office).
The Transformational Power of AI
In his thought-provoking HBR article, Andrew Ng suggested artificial intelligence will produce social transformation on par with electricity from 100 years ago and the internet from approximately 20 years back. Andrew, the chief scientist at Chinese internet behemoth Baidu Inc., also linked the emergence of the internet with the evolution of the essential corporate position of Chief Information Officer (CIO). All of which set the frame for Andrew’s central argument:
“To the majority of companies that have data but lack deep AI knowledge, I recommend hiring a chief AI officer or a VP of AI. (Some chief data officers and forward-thinking CIOs are effectively taking on this role.) The benefit of a chief AI officer is having someone who can make sure AI gets applied across silos. Most companies have naturally developed siloed functions in order to specialize and become more efficient.”
We’ve Been Here Before
It is this “same logic” that drew my support for the chief customer officer (CCO) concept around 2006. For historical reference, the first customer officer, Jack Chambers, was appointed in 1999 at Texas New Mexico Power but the role didn’t gain significant visibility for many of us until at least 2003, when a small but impressive group of CCO’s began to surface (e.g. Jeff Lewis at Monster.com and Marissa Peterson at Sun Microsystems). In the early days of the CCO movement, the case was being made (and continues to be made today) that Chief Customer Officers are needed to “break down silos” and “focus on enterprise-wide strategy placing the customer at the center of all corporate decision-making.”
Decentralized and Nimble
Despite the general attractiveness of placing senior level leaders at the helm of enterprise-wide efforts, I’ve found myself resistant to the slowly emerging Chief Artificial Intelligence Officer (CAIO) movement. It wasn’t until I read 2017 HBR article from Kristian Hammond that my unsettled feeling was given voice. Here is Kristian’s key thesis posited in his article so aptly titled, Please Don’t Hire a Chief Artificial Intelligence Officer:
“It’s not that I doubt AI’s usefulness. I have spent my entire professional life working in the field. Far from being a skeptic, I am a rabid true believer. However, I also believe that the effective deployment of AI in the enterprise requires a focus on achieving business goals. Rushing towards an ‘AI strategy’” and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.”
From my vantage point, AI is a monstrously powerful tool baked into the fiber of all aspects of data-savvy companies. It is best managed by agile teams that leverage Artificial Intelligence as a solutions and innovation tool. Neil Jacobstein the head of artificial intelligence and robotics at Singularity University went further by telling the Wall Street Journal that:
“It’s very important to match the speed of the technology with the nimbleness of the teams. And having a centralized AI guru at the top, where everybody has to ask questions of that person, is unlikely to be as fast and effective as having a decentralized organization with powerful teams. Centralizing AI across an enterprise might prove unwieldy compared to having small teams.”
AI Itself Can’t Solve This
Invariably large, data-rich organizations will wrestle with the question “to add or not to add” a CAIO. Until I hear a more convincing case for this new position in the C-Suite I doubt I will be recommending the change.
The big takeaway for all of us is an appreciation of the transformational power and potential of big data, machine learning, and artificial intelligence. Also, it is the awareness that machines won’t likely solve issues like how to structure our teams and leaders to use that very data as we pursue key business objectives. Some things can only be left to the intuition and modifications of people!
An Emerging Theme In Thematic Investing
Exchange traded funds (ETFs) are popular vehicles for market participants looking to engage in thematic investing. Thematic investing looks to take advantage of future growth trends, including disruptive technologies. Given that forward-looking approach, stock-picking in the thematic universe is equally as hard, if not harder, than in traditional market segments.
Go back to the late 1990s, before the bursting of the Internet/technology bubble. Back then, investors stood an equal chance of selecting E-Toys over Amazon or some no longer in existence networking equipment maker over Cisco.
“History is littered with examples of prospering industries with no indication of which company will come to dominate the industry,” according to Nasdaq. “This suggests that successful thematic investing is more about selecting baskets of investments rather than single securities.”1
The ALPS Disruptive Technologies ETF (DTEC) provides basket exposure to a broad swath of thematic investments. DTEC features exposure to not just one or two emerging technologies, but 10 such themes on an equal-weight basis.
The 10 themes represented in DTEC are as follows: 3D printing, clean energy, cloud computing, cybersecurity, data and analytics, fintech, healthcare innovation, Internet of Things (IoT), mobile payments and robotics and artificial intelligence (AI).
Generally speaking, fund issuers have been quick to respond to disruptive and transformative technologies, bringing products to market to tap these themes. Prior to DTEC coming to market late last year, there were ETFs devoted exclusively to cloud computing, cybersecurity, robotics and other themes featured in DTEC. However, few use the basket approach to themes employed by DTEC.
February, a rough month for U.S. stocks, highlighted the advantages of DTEC's multi-theme methodology. Seven of the 10 themes found in the fund finished the month lower, but DTEC was able to outperform the S&P 500 on a monthly basis.
Focusing on individual themes can be rewarding over the long-term, but not all investors have the risk tolerance for such a strategy. Consider this: the Indxx Global Robotics & Artificial Intelligence Thematic Index jumped more than 48% in 2017. That type of performance is enough to seduce many investors, but that same benchmark slipped 7.60% in February, generating monthly volatility of 34.10%.2 Said another way, that robotics and AI index's February slide was more than triple the loss experienced by DTEC during the month.
While it probably is not accurate to call the indexes devoted to individual disruptive themes “old,” many use old school weighting methodologies. For example, the two largest components in the ISE Cloud Computing Index are Netflix, Inc. (NFLX) and Amazon.com Inc. (AMZN). Only two members of the S&P 500 have larger market values than Amazon while Netflix currently has a larger market cap than Wal-Mart (WMT) and McDonald's (MCD).
Holdings subject ot change as of 12/31/17
For its part, DTEC not only equally weights its 10 disruptive themes, but its 100 components as well, potentially reducing single stock risk in the process. As the chart below confirms, equally weighting stocks is rewarding across sectors and market capitalization segments.
Past performance does not guarantee future results
Annualized returns for the past 10 years show seven of the 11 S&P 500 sectors, when equally weighted, outperform cap-weighted equivalents, according to S&P. Three of those seven sectors – financial services, healthcare and technology – are prominent parts of DTEC's roster.
1 Source: Nasdaq Dec. 28, 2015 https://www.nasdaq.com/article/what-thematic-investing-is-and-its-strengths-and-risks-cm559209
2 Source: ETF Replay data
An investor should consider the investment objectives, risks, charges and expenses carefully before investing. To obtain a prospectus which contain this and other information call 866.675.2639 or visit www.alpsfunds.com. Read the prospectus carefully before investing.
An investment in the ALPS Disruptive Technologies ETF (DTEC) may be subject to substantially greater risk and volatility than investments in larger and more mature technology companies.
There is no assurance that the market developments and sector growth based upon the themes discussed in the article will come to pass.
ALPS Disruptive Technologies ETF shares are not individually redeemable. Investors buy and sell shares of the ALPS Disruptive Technologies ETF on a secondary market. Only market makers or “authorized participants” may trade directly with the Fund, typically in blocks of 50,000 shares.
ALPS Advisors, Inc. (AAI) has engaged IRIS Werks, LLC (IRIS) to produce analysis and commentary on ALPS-advised ETFs. IRIS currently has a compensated business relationship with AAI. AAI is not affiliated with IRIS.
The content and opinions expressed in this article are that of the author and not the views and opinions of AAI. In addition, AAI assumes no responsibility to ensure the accuracy of the content written by the author.
There are risks involved with investing in ETFs including the loss of money. Additional information regarding the risks of this investment is available in the prospectus. Past Performance is not indicative of future results.
The fund is new and has limited operating history.
ALPS Portfolio Solutions Distributor, Inc. is the distributor for the ALPS Disruptive Technologies ETF. AAI is affiliated with ALPS Portfolio Solutions Distributor, Inc.
The author is not an investment professional and this article should not be considered investment advice. While the information and statistical data contained herein are based on sources believed to be reliable, the author takes no responsibility to ensure the accuracy of the content. Additionally, this article should not be relied on or be the basis for an investment decision. Information that is historical is not indicative of future results, and subject to change.
S&P 500®: A capitalization-weighted index of 500 stocks designed to measure performance of the broad domestic economy through changes in the aggregate market value of 500 stocks representing all major industries.
S&P SmallCap 600®: A capitalization-weighted index that measures the small-cap segment of the U.S. equity market.
S&P MidCap 400®: A capitalization-weighted index that measures the mid-cap segment of the U.S. equity market.
Indxx Global Robotics & Artifical Intelligence Thematic Index: The Indxx Global Robotics & Artificial Intelligence Thematic Index is designed to track the performance of companies listed in developed markets that are expected to benefit from the increased adoption and utilization of robotics and Artificial Intelligence ("AI"), including companies involved in Industrial Robotics and Automation, Non-Industrial Robots, Artificial Intelligence and Unmanned Vehicles.
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