Strategic Insights and Clickworthy Content Development

Month: April 2018

B2B Chatbots are Poised for Explosive Growth

Chatbot use is on the rise, and the use cases are growing. According to Gartner, by 2021, more than 50% of enterprises will spend more each year on bots and chatbot creation than traditional mobile app development.

In a recent blog, Gartner Brand Content Manager Kasey Panetta said, “Individual apps are out. Bots are in. In the ‘post-app era,’ chatbots will become the face of AI, and bots will transform the way apps are built. Traditional apps, which are downloaded from a store to a mobile device, will become just one of many options for customers.”

Chatbots and virtual assistants such as Alexa are being interwoven into consumer lifestyles. KPMG Digital Enablement Managing Director Michael Wolf says his company sees tremendous potential on the B2B side.

“B2B chatbots and virtual assistants could be the interface across multiple systems,” said Wolf. “We’re seeing a lot of growth in that, and the enterprise platform companies are making investments there, either acquiring the capability or acquiring the platforms to do that stuff.”

Implementing chatbots and implementing virtual assistants differs, based on their respective designs and capabilities. Traditional chatbots are script-based, so they respond to pre-programmed inputs. Virtual assistants utilize machine learning to continually improve their ability to understand and respond appropriately to natural language.

“One of the problems with bots is modeling what they think customers want rather than training the system with real people, not just employees and customers, but the person asking the questions. What are they asking?  How are they asking it?” said Wolf. “If you just try to follow your same traditional route paradigms without concentrating on learning and design thinking, you’re going to get less desirable outcomes.”

Expanding B2B use cases

Like other forms of automation, chatbots and virtual assistants are seen as human-augmenting technologies that enable humans to focus on less repetitive, higher-value tasks.

David Nichols, Americas Innovation and Alliance Leader for EY Advisory sees numerous opportunities for B2B chatbots, including internal employee communications, most HR interactions, and everyday interactions such as checking invoice status, delivery status and updates, and customer service interactions.

“The biggest challenge with B2B companies is getting suppliers and customers to use the Chabot functionality,” said Nichols. “Also, B2B companies don’t usually place the same priority on customer personalization as B2C companies. As a result, the customer service interactions at B2B companies don’t usually have the same level of detailed customer segmentation and interaction history. This will present a challenge when developing the use-cases and scenarios for the bot conversation flow.”

In HR scenarios, chatbots provide intelligent means of re-engaging with candidates, specifically sourcing, screening, and updating candidate information.

“[Using] other methods these interactions can take days to weeks for an organization to handle,” said Chris Collins, CEO of recruitment automation company RoboRecruiter. “Chatbots significantly increase the speed and scale that you can operate down to hours and combined with AI can keep the data active.”

That could lead to more positive recruiting experiences for candidates, contract workers, and employers. Similarly, from an outward-facing standpoint, chatbots and virtual assistants could improve brands’ relationships with customers.

It might seem counter-intuitive that an AI-driven chatbot can help companies build relationships with their customers, but remember, the ‘Millennial Mindset’ is quickly becoming the dominant purchasing orientation, and those customers want to efficiently self-service,” said Anthony SmithCEO of CRM solution provider Insightly. “In 2018, B2B chatbots will be utilized not only for lead generation, but also as virtual business assistants and they will handle different tasks such as scheduling and cancelling meetings, setting alarms etc.”

Depending on the enterprise applications chatbots are integrated with, they’ll be able to undertake more complex tasks, such as placing orders, invoicing and other B2B activities that are time consuming and usually require precision. However, there are challenges,

“Integrating chatbots with the major payment systems and with social media is tough and it will probably take time, but once this is covered, chatbots will be able to take orders directly through social accounts and that will be a revolution,” said Insightly’s Smith.

Application integration is critical

Automating business processes requires tight integration with enterprise systems. Exactly how many and which systems depends on the purpose of the chatbot. However, because user experience is vitally important, it’s critical to understand what the users of such systems will want to do with them.

“Some are just trying to redo web and mobile rather than using a design approach to using this,” said KPMG’s Wolf. “There’s an assumption because it’s not visual, it doesn’t involve design.”

In B2B contexts, there are a lot of repetitive tasks that take place within businesses processes, some of which require integrations with different types of systems.

“The injection of the chatbot is allowing consumer-like experiences. ‘I want my ERP to feel like Google

and ‘I want my CRM to feel like Amazon’ is a constant discussion for my customers,” said KPMG’s Wolf. “Applying an enterprise chatbot is obvious in that scenario.”

The end goal for virtual assistants is orchestrating everything necessary to answer a query or execute a request, which can involve a complex web of interconnections among disparate systems.

In short, the best way forward is iterative because requirements, technology and user expectations are constantly changing.

4 Ways to Improve Data Storytelling

Analytical results are often interpreted differently by different people. Sometimes the conclusions presented don’t align with intuition. Differences in experience and expertise can also come into play. An effective way to align thinking is through data storytelling, although there are better and worse ways to do it.

Data storytelling typically includes text, visualizations and sometimes tables to illustrate a developing trend or issue that requires attention if not action. Data storytelling can make the results more memorable and impactful for those who hear it, assuming the presentation is done effectively. Following are a few things to consider

1. Consider the Audience

Data scientists are often considered poor data storytellers because they struggle to align a story with the needs and knowledge level of the audience. Sometimes others are brought in to translate all the technical jargon into something that that is meaningful to business leaders.

Similarly, different parts of a business may require a slightly different focus that uses different language and maybe even different types of data visualizations to have the desired effect, which is understanding analytical results in context.

2. Tell A Story

Effective stories have a beginning, a middle, and an end. The beginning of a story provides context, setting the stage for the story itself. The middle tells the story, and the end usually includes a set of possibilities. Getting the end right is important because insights without action have little value. Are there actionable insights from the data? How can the results be used to drive strategy? In a business context, is there a significant revenue opportunity or an opportunity for cost savings? How much more likely is it that one course of action will succeed versus another? If you provide curated data points and visualizations that support the key points, you can often pre-emptively address the most likely questions and objections.

Effective storytelling also address issues beyond the “what.” Take a sales situation for example. Heads of sales are constantly monitoring progress against sales targets. Let’s say sales fell short or exceeded expectations last quarter. That leads to other questions such as why were sales better or worse than we expected? How could we use those insights to turn the situation around or increase sales even further? How well do we understand our customer base and their requirements? What levers work well and which don’t?

With some solid analytics and effective data storytelling, everyone in the room — the head of sales along with the C-suite or her team can have a common understanding of what impacted the sales results, why, how things are changing and what that means going forward, for the sales team, products, marketing, etc.

Data storytelling should also explain why the analysis was performed, how the analysis was performed, whether hypotheses were proven or disproven in addition to the important findings and what those findings mean for the audience. Some people make the mistake of showing the many steps required for an analysis to demonstrate how challenging the exercise was, which adds little, if any, value.

3. Quality Matters

Great stories can be derailed by simple mistakes, such as misspellings, a lack of focus and a propensity to demonstrate the mastery of a software program to the point of distracting the audience.

Misspellings and grammatical errors tend to be addressed by modern software; however, they don’t always catch everything. Some of them have default settings that limit the amount of text that can be included; however, that’s usually configurable. Sadly, it’ possible to overload stories with so much noise that the audience has trouble staying focused. The point is not clear, in other words. Similarly, trying to get too creative with the colors used in data visualizations can detract the audience’s attention away from the point.

Also consider the presentation of the data in relation to the data itself. On a scale of one to two, a move from one to two reflects a 100% increase. On an actual scale of 25, 50, 100, or 1000, a single-digit increase would appear differently.

4. Be Prepared to Address Alternatives

One of the reasons businesses have placed greater emphasis on analytics versus traditional reporting is the ability to interact with data versus passively consuming it. There is a parallel with data storytelling which is a move away from the traditional and static business presentation format that tends to reserve questions for the end to interactive storytelling in which questions or alternate points of view can be explored live.

Generally speaking, data storytellers should be prepared for questions and challenges, regardless. Why wasn’t something else explored? If a particular variable were added or subtracted, what would the effect be? Of the X possibilities, which is the most likely to see and why?

How SaaS Strategies Are Evolving

Enterprises are subscribing to more SaaS services than ever, with considerable procurement happening at the departmental level. Specialized SaaS providers target problems that those departments want solved quickly. Because SaaS software tends to be easy to set up and use, there appears to be no need for IT’s involvement, until something goes wrong.

According to the Harvey Nash /KPMG 2017 CIO Survey, 91% of the nearly 4,500 CIO and IT leaders who responded expect to make moderate or significant SaaS investments, up from 82% in 2016. The report also states that 40% of SaaS product procurement now happens outside IT.

“IT needs a new operating model,” said Gianna D’Angelo, principal of KPMG CIO Advisory. “CIOs must respond by continuing to focus on operational excellence while adopting a new operating model for IT to drive innovation and value in these changing times.”

Some IT shops are reacting to shadow IT like they reacted to “bring your own device” (BYOD), meaning if you can’t stop it, you have to enable it with governance in mind. However, issues remain.

“In the last three years, we’ve put policies and some governance in place, but it doesn’t matter. You pull out your credit card, you buy an open source application and I have a virus on my network,” said Todd Reynolds, CTO of WEX Health, which provides a platform for benefit management and healthcare-related financial management. “I don’t even know about it until there’s an issue.”

How SaaS pricing is changing

KPMG’s D’Angelo said most SaaS pricing is based on users or by revenue, and that the contract timeframe is three to five years. There has been some movement to shorter timeframes as low as two years.

Sanjay Srivastava, chief digital officer of Genpact, a global professional services company, said his firm sees a shift from user-based pricing to usage-based pricing, which in Genpact’s case takes the form of a per-item charge for a document or balance sheet, for example.

Regardless of what the SaaS pricing model is, SaaS providers are facing downward pricing pressure. According to Gartner, “Vendors are becoming more creative with their SaaS business models to reflect a need to stand out in the fast-growing subscription economy.”

For its part, WEX Health is responding with new services that drive additional revenue. It has also put some usage-based pricing in place for customers that require elastic compute capabilities. “Mobile is killing us,” said Wex Health’s Reynolds. “You’ve given somebody an application to use on their phone 24/7, so they’re starting to leverage that usage so much more. It’s good people are using [our software] more often, but it requires us to have more storage.”

Longer-term thinking is wise

When departments purchase SaaS software, they usually are seeking relief from some sort of business problem, such as multichannel marketing attribution – studying the set of actions that users take in various environments. What business people often miss is the longer-term requirement to share data across disparate systems.

“If you have half on-premises and half in different clouds, you might have a private cloud, some in Azure and some in Amazon because the technology stack is beneficial to the apps,” said WEX Health’s Reynolds. “Pulling all of that together and making it safe and accessible is the biggest challenge from an operational perspective on the IT side.”

While SaaS systems tend to have APIs that help with data exchange, most enterprises have hybrid environments that include legacy systems, some of which do not have APIs. In the older systems, the data dictionaries may not be up-to-date and Master Data Management (MDM) may not have been maintained. So enterprises often face substantial data quality issues that negatively impact the value they’re getting from their investments.

“If you really want to get value out of [SaaS] — if you want Salesforce to run CRM and you want it to run sales, integrated, and it still has to be connected to ERP — each thing has to be connected,” said Genpact’s  Srivastava. “There’s a lot of back and forth. Planning for that back and forth, and planning well, is really critical.”

Part of that back-and-forth is ensuring that the right governance, compliance and security controls are in place.

Bottom line

There’s more to SaaS investments than may be obvious to the people procuring them. At the same time, IT departments can no longer be the sole gatekeepers of all things tech.

“The challenge for CIOs is enormous, the stakes are large and change efforts of this magnitude take years, but transforming the IT operating model can be done,” said KPMG’s D’Angelo. “Complicating the effort is that IT must continue to support the existing portfolios, including retained infrastructure and legacy applications, during the transformation.”

This means that, for a period of time, IT will have to use a hybrid model comprising both the project-oriented, plan-build-run approach and the next-generation, broker-integrate-orchestrate approach, D’Angelo added.

Tips for Ensuring Winning SaaS Strategies

SaaS software is not a one-size-fits-all proposition. Costs and benefits vary greatly, as do the short-term and long-term trade-offs. Following are a few things you can do along the way to ease the transition.

If you’re just starting out, chances are that most if not all of the software you procure will be SaaS because that’s the way things are going. In addition, SaaS allows for an economic shift to relatively low-cost subscriptions that include upgrades and maintenance (an operational expenditure). This is instead of substantial up-front, on-premises software investments that require subsequent maintenance investments and IT’s help (a capital expenditure). Regardless of what type of software you choose, though, it’s wise to think beyond today’s requirements so you have a better chance of avoiding unforeseen challenges and costs in the future.

If you’re piloting a new type of software, SaaS is probably the way to go because you can usually experiment without a long-term commitment. However, be mindful of the potential integration, security and governance challenges you may encounter as you attempt to connect different data sources.

If you’re in production, you’ll want to continuously assess your requirements in terms of software models, integration, compliance, governance and security. As you continue your move into the cloud, understand what’s holding you back. Finance and HR, for instance, may still hesitate to store their sensitive data anywhere but on-premises. For the foreseeable future, you’ll probably have a hybrid strategy that becomes more cloud-based with time.

At each stage, it’s wise to understand the potential risks and rewards beyond what’s obvious today.

Why Surveys Should Be Structured Differently

keyboard-417093_1280If you’re anything like me, you’re often asked to participate in surveys.  Some of them are short and simple.  Others are very long, very complicated, or both.

You may also design and implement surveys from time to time like I do.   If you want some insight into the effectiveness of your survey designs and their outcomes, pay attention to the responses you get.

Notice the Drop-off Points

Complicated surveys that take 15 or 20 minutes to complete tend to reflect drop off points at which the respondents decided that the time investment required wasn’t worth whatever incentive was offered.  After all, not everyone actually cares about survey results or a  1-in-1,000 chance of winning the latest iPad, for example.  If there’s no incentive whatsoever, long and complicated surveys may  be even less successful, even if you’re pinging your own  database.

A magazine publisher recently ran such a survey, and boy, was it hairy.  It started out like similar surveys, asking questions about the respondent’s title, affiliation, company revenue and size.  It also asked about purchasing habits – who approves, who specifies, who recommends, etc. for different kinds of technologies.  Then, what the respondent’s content preferences are for learning about tech (several drop-down menus), using tech (several drop-down menus), purchasing tech (several drop-down menus), and I can’t remember what else.  At that point, one was about 6% done with the survey.  So much for “10 – 15 minutes.”  It took about 10 or 15 minutes just to wade through the first single-digit percent of it.  One would really want a slim chance of winning the incentive to complete that survey.

In short, the quest to learn everything about everything in one very long and complex survey may end in more knowledge about who took the survey than how how people feel about important issues.

On the flip side are very simple surveys that take a minute or two to answer.  Those types of surveys tend to focus on whether a customer is satisfied or dissatisfied with customer service, rather than delving into the details of opinions about several complicated matters.

Survey design is really important.  Complex fishing expeditions can and often do reflect a lack of focus on the survey designer’s part.

Complex Surveys May Skew Results

Overly complicated surveys may also yield spurious results.  For example, let’s say 500 people agree to take a survey we just launched that happens to be very long and very complex.  Not all of the respondents will get past the who-are-you questions because those too are complicated.  Then, as the survey goes on, more people drop, then more.

The result is that  X% of of the survey responses at the end of the survey are not the same as X% earlier in the survey.  What I mean by that is 500 people started, maybe 400 get past the qualification portion, and the numbers continue to fall as yet more complicated questions arise but  the “progress bar” shows little forward movement.  By the end of the survey, far less than 500 have participated, maybe 200  or 100.

Of course, no one outside the survey team knows this, including the people in the company who are presented with the survey results.  They only know that 500 people participated in the survey and X% said this or that.

However, had all 500 people answered all the questions, the results of some of the questions would likely look slightly or considerably different, which may be very important.

Let’s say 150 people completed our  survey and the last question asked whether they planned to purchase an iPhone 7 within the next three months.  40% of them or 60 respondents said yes.  If all 500 survey respondents answered that same question, I can almost guarantee you the answer would not be 40% .  It might be close to 40% or it might not be even close to 40%.

So, if you genuinely care about divining some sort of “truth” from surveys, you need to be mindful about how to define and structure the survey and that the data you see may not be telling you the entire story, or even an accurate story.

The point about accuracy is very important and one that people without some kind of statistical background likely haven’t even considered because they’re viewing all aggregate numbers as having equal weight and equal accuracy.

I, for one, think that survey “best practices” are going to evolve in the coming years with the help of data science.  While the average business person knows little about data science now, in the future it will likely seem cavalier not to consider the quality of the data you’re getting and what you can do to improve the quality of that data.  Your credibility and perhaps your job may depend on it.

In the meantime, try not to shift the burden of thinking entirely to your survey audience because it won’t do either of you much good.  Think about what you want to achieve, structure your questions in a way that gives you insight into your audience and their motivations (avoid leading questions!), and be mindful that not all aggregate answers are equally accurate or representative, even within the same survey.

What Retailers Know About You

Retailers now have access to more information than ever. They’re using loyalty cards, cameras, POS transaction data, GPS data, and third-party data in an effort to get shoppers to visit more often and buy more. The focus is to provide better shopping experiences on a more personalized level. Operationally speaking, they’re trying to reduce waste, optimize inventory selection, and improve merchandising.

The barrier to personalized experiences is PII (personally identifiable information), of course.

“[What retailers know about you] is still largely transaction-based,” said Dave Harvey, VP of Thought Leadership, branding and retail services provider Daymon. “Information about lifestyles, behaviors and attitudes are hard for retailers to get themselves so they partner with companies and providers that have those kinds of panels, especially getting information about how people are reacting through social media and what they’re buying online.”

Transactions Are Driving Insights

The most powerful asset is a retailer’s transactional database. How they segment data is critical, whether it’s transaction-based reach, frequency, lifestyle behaviors, or product groupings. Retailers can identify how you live your life based on the products you buy.

“The biggest Achille’s heel of transaction data, no matter how much you’re segmenting it and how much you’re mining it, is you’re not seeing what your competitors are doing,” said Harvey. “Looking at transaction data across your competition becomes critical.”

As consumers we see the results of that in offers, which may show up in an app, email, flyer or coupons generated at the POS.

Social media scraping has also become popular, not only to gauge consumer sentiment about brands and products, but also to provide additional lifestyle insight.

Some retailers are using predictive and prescriptive analytics to optimize pricing, promotions and inventory. They also have a lot of information about where their customers are coming from, based on credit card transactions. In addition, they’re using third party data to understand customer demographics, including the median incomes of the zip codes in which customers live.

They’re Watching Your Buying Patterns

Retailers monitor what shoppers buy over time, including items they tend to buy together, such as shirts and ties or eggs and orange juice. The information helps them organize shelves, aisles, and end caps.

“There’s a lot of implications for meal solutions and category adjacencies, how people are shopping in the store, how that might lead a retailer test way to offer a right combination of products to create a solution somewhere in the store,” said Harvey. “You can’t be everything to everyone, so how can the information help you prioritize where to focus? The information you can mine from your transaction database, your loyalty card database can help you become more efficient.”

Information about buying patterns and price elasticity allows retailers to micro-target so effectively that shoppers visit the store more often and spend more money.

They May Know How You Shop the Store

Shopping carts and baskets are a necessary convenience for customers, although the latest ones include sensors that track customers’ paths as they navigate through the store.

“They can get the path data and purchase data about how much time you spend at stations, and they can use it to redesign the store or and get you move through the store much more because they know the more you move through the store the more you buy,” said PK Kannan, a professor of marketing science at the University of Maryland’s Robert H. Smith School of Business.

Retailers also use cameras to optimize merchandising to better understand customer behavior including where they go and how long they stay. Now they’re also analyzing facial expressions to determine one’s state of mind.

Driving Business Value from Analytics

Different kinds of analytics result in different ROI. If a retailer is just starting out, Kannaan recommends starting with loyalty cards since other types of data capture and analysis can be prohibitively expensive and the analysis can be cumbersome.

“The ROI on loyalty cards is pretty good,” said Kannaan. “The initial ROI is going to be high and then as you go into more of these cart or visual data, video data, your ROI is going to level off.”

Strategies also differ among types of retailers. For example, a specialty retailer will want data that provides deep insight into the category and shoppers of that category versus a store such as Walmart that carries items in many different categories.

“If you’re a retailer trying to sell a ton of categories you want to understand how people are talking about their shopping experience,” said Harvey. “There’s still a lot of untapped opportunity in understanding social media as it relates to doing better analysis with retailers.”

They’re Innovating

Retailers are working hard to understand their customers, so they can provide better shopping experiences. While personalization techniques are getting more sophisticated, there’s only so far they can go legally in many jurisdictions.

Kannan said a way of getting around this is to take all the informational content, remove any PII, and then extract the resulting information out of the data.

“It’s like I’m taking the kernel from this thing because I don’t have the space to store it and keeping it is not a good policy, so I am going to keep some of the sufficient statistics with me and as new data comes in, I’m going to combine the old data with new data and use it for targeting purposes,” said Kannan. That’s becoming more of a possibility now, and also it’s a reality because data volumes are increasing like crazy. That way I don’t have to store all the data in a data lake.”

AI Has a Foothold in Business, Now for the Next Steps

AI is seeping into different industries, slowly remolding the global competitive landscape. However, most business leaders still don’t know how machine intelligence will impact their businesses.

EY recently published a brief, which focuses the current state of AI. We interviewed Nigel Duffy, EY Global Innovation AI leader who co-authored the document with Chris Mazzei, EY Global Innovation Technologies Leader and Global Chief Analytics Officer.

The brief frames the current state of AI well: “Most organizations aren’t exploiting the potential of AI; they are just at the beginnings of their AI journeys. What should be holding companies back is a lack of talent, but it’s actually a lack of understanding of what’s possible – particularly at the top of large enterprises.”

Addressing the C-Suite disconnect

It’s often hard to imagine the impact new technologies will have on a business. Granted, AI is not new; however, due to recent research and developments, it’s finally at a point where more organizations are either using it in production or experimenting with it.

Some say AI is at an inflection point, namely, at the beginning stages of exponential “hockey stick” growth. If that’s true, the latecomers may find themselves blind-sided by competitors, simply because they didn’t think about and learn first-hand how AI would affect their own companies.

According to Duffy, part of the confusion stems from the fact that AI is a broad set of technologies as opposed to a single, coherent capability. Given the complexity of the landscape (machine learning, computer vision, natural language processing, deep learning, neural networks, etc.), it’s not surprising that business leaders don’t have a clear understanding of how it will transform their businesses.

Also, the hype about AI is skewed. When new technologies hit the scene, evangelists and the media tend to focus on the opportunities and disregard the potential challenges. These skewed views fuel silver-bullet belief systems when silver bullets do not actually exist. It takes hands-on experience, including successes and failures, to truly understand the potential and limitations of a technology as applied to a specific business.

“It goes without saying that AI has the potential to completely transform business. I recently spoke on a panel about this topic at Fortune Global Forum in China, and everyone there, from prime ministers to chairmen of Fortune 500 firms, discussed the transformational potential of AI,” said Duffy. “There is a broad understanding that it is going to be transformational, but the challenge is that it requires work and investment to develop the strategy [and] vision to realize that potential.”

Many organizations are in the early stages of AI adoption, so they have not yet invested sufficient time and money in the process. In order to bridge this gap, leaders need to start gaining experience now, developing initial use cases or proofs of concept. Duffy recommends investing in a big-picture strategy, and developing a vision for how this could transform a firm or sector.

AI is more than a technology

AI is a piece of the digital transformation puzzle. As with all things related to digital transformation, technology is only part of the picture. The most effective strategies focus on business problem-solving.

“I believe companies will have the most impact with a business-first, value-led approach,” said Duffy. “The best way to approach AI is to focus on how to add value to a business beyond just cost efficiencies. Businesses must think now about AI from a strategic perspective and ask themselves how much more value they can deliver through more intelligent use of AI.”

Of course, there are some barriers to adoption that are technology-related. As is typical in the early adoption stages of a technology, the initial tools tend to be targeted at a narrow, technical audience that is capable of using them. However, as the technology matures, easier-to-use tools follow and abstract the some of the complexity. Usually those tools are aimed at “power users.”

Finally, becomes easy enough for the masses to take advantage of, such as analytics dashboards in the enterprise. Already, AI is built into and will be embedded in many kinds of devices and software, to the point where it is transparent to the user. For example, one does not have to be an AI expert to use Amazon Echo.

AI will create winners and losers in every industry,” said Duffy. “AI is here today and can provide significant value now. Can you really afford to be slower to adopt it than your competitors?”

Business leaders and organizations should get familiar with AI technology now, because it will make it easier to determine where AI can be used as an effective problem-solving solution, Duffy said.

Business leaders and technologists need to work together. EY does this internally to meld cultures and disrupt traditional ways of thinking.

Set reasonable ROI expectations

In the AI brief, Duffy and Mazzei say, “Many early projects will have low ROI and a limited impact.”  So, at what point, then, should businesses invest in AI?  On one hand, the early adopters gain insight and experience that those sitting on the sidelines miss. On the other hand, those who are later to the game have the luxury of using more mature toolsets and learning from others’ mistakes.

“Early adoption doesn’t necessarily have a low ROI. [To clarify,] the early adopters are often focused on the technology rather than the business problem – this can lead to low ROI,” Duffy said. “However, [early adoption] does lead to invaluable learning.”

Early technology-led projects may also have low ROI because they are (and should be) as much about learning as about value. Rather than limiting the scope to only technology-led projects; businesses should identify projects based on their business value and have them led by business stakeholders.

“Because of the transformational potential of AI, if you wait and your competitors don’t, you will be at a disadvantage. AI will differentiate between winners and losers, and the pace at which that is happening is only accelerating,” said Duffy. “Most people are early in their AI journey and the actual investment can be small relative to the potential. It’s a smart decision to make a relatively small investment to start.”

Overconfidence can be dangerous

The immense interest in AI is creating career opportunities and with it overstatements about qualifications. Duffy said it’s important to get the right talent.

“The Dunning–Kruger Effect is of significant concern in this space, that is, people can be unskilled and unaware of it,” said Duffy. “The field has grown so rapidly that there are many people who can solve technical problems, but they have a lack of deep experience.”

EY conducted a survey of 200 senior AI professionals, 56% of which said that a lack of talent is the greatest barrier to implementation within business operations. If companies don’t have competent AI professionals, they face three big risks that are easily preventable if business leaders think about them in advance and do something about them proactively.

The first is testing. It’s much easier to get AI testing wrong compared to other technologies. Getting it right requires a certain amount of sophistication, as there are many subtle statistical issues. According to Duffy, AI testing requires talent deep expertise, working with this type of technology.

[Maybe it’s time for your organization to make a real investment and commitment to AI. Read more here.]

The second challenge is that machine learning can amplify bias, which was another one of the key takeaways from the recent EY AI survey. Forty-one percent of the survey participants said they see the gender diversity of existing AI talent influencing machine biases. Researchers need to be especially mindful of bias, specifically, racial, gender or other cultural biases. To proactively avoid those types of bias, organizations will need to ensure that they’re hiring from a diverse talent pool when hiring AI talent.

Finally, AI tools are making automated decisions, quickly. A sophisticated monitoring system needs to be in place to ensure that anomalies are caught quickly, Duffy said.

How to ask the right questions

Business leaders who lack experience with AI may wonder how it’s possible to know whether they’re asking the right questions in the first place.

“Some of this is about building up experience over time, which reflects back to my point about how it’s better to be an early adopter. You start asking the questions, seeing the answers, and seeing the outcomes that lead to asking better questions,”‘ said Duffy. “By starting soon, leaders can get experience in determining how AI can have the most meaningful impact on their business.”