Realizing the Growth Potential of AI

Business leaders and investors universally agree that Artificial Intelligence (AI) and Machine Learning (ML) will transform their businesses by reducing costs, managing risks, streamlining operations, accelerating growth, and fueling innovation.

The potential for AI to drive revenue and profit growth is enormous. Marketing, customer service, and sales were identified as the top three functions where AI can realize its full potential according to a survey of 1,093 executives by Forbes.

·        Sales organizations are dramatically improving sales performance by using algorithms to help with the basics of account and lead prioritization and qualification, recommending the content or sales action that will lead to success, and reallocating sales resources to the places they can have the most impact.

·        Marketers are looking for AI to fuel enormous efficiencies by targeting and optimizing the impact of huge investments in media, content, products, and digital channels.

·        And in customer service, AI is opening entire new frontiers in customer experience and success by applying NPL, sentiment analysis, automation, and personalization to customer relationship management. 90% of organizations are using AI to improve their customer journeys, revolutionize how they interact with customers and deliver them more compelling experiences.

To realize this potential to grow revenues, profits and firm value, businesses in every industry have announced AI focused initiatives. On average, investment in advanced analytics will exceed 11% of overall marketing budgets by 2022. Spending on AI software will top $125B by 2025 as organizations weave AI and Machine Learning tools into their business processes. In parallel, investors have poured more than $5 Billion into over 1,400 AI fueled sales and technology companies to meet this demand.

So far, the impact of these investments on growth and profits has not yet been transformational. Right now 70 % of AI initiatives are showing little or no return. And more businesses will struggle to realize the full potential of AI to grow firm value if their leaders don’t learn lessons from past transformations like the internet in the 1990s and cloud computing in the mid-2000s, according to Kartik Hosanagar, Professor of Technology, Digital Business and Marketing at the Wharton School and author of the influential book A Humans Guide to Machine Intelligence. 

What separates the AI projects that succeed from the ones that don’t often has more to do with the business strategies organizations follow when applying technologies than the ability of the technology itself to transform the business,” according to Professor Hosanagar. “Many of the problems are less about the tools and more about leadership. Most of the failures to harness the power of AI lies in human behavior, management understanding, and the failure to mesh algorithmic capabilities into organizations, business models and the culture of the business.”

Today most executives feel like the pace at which AI can be made successful has been overstated, and the challenges have been understated according to the Forbes survey. That is totally understandable based on the current level of acumen in the business community about AI and advanced analytics. But the perception of hype and speed is an education and skill problem. AI works today in many business applications. It’s more a matter of the managers tasked with harnessing the power of AI don’t have the experience and framework to understand it.  Just as a calculus class will move far too fast for a sixth grader to grasp, growth programs based on AI and ML will be far too advanced for the executives who define, direct, and fund their development and are ultimately accountable for the results they deliver.

“Algorithms are opaque to the average business executive and can often behave in ways that are (or appear to be) irrational, unpredictable, biased, or even potentially harmful,” continues Kartik. “It’s up to business leaders to shape the narrative, direction, and ways algorithms can -and cannot – impact work, customer relationships, and the way business creates value.”

Executives who allocate capital and the managers who will lead the AI transformation cannot afford to have a poor understanding of something so fundamental to business and the creation of value today. “Ignoring the problem because it’s complex is not really an option. AI-based algorithms are here to stay,” continues Professor Hosanagar. “To discard them now would be like Stone Age humans deciding to reject the use of fire because it can be tricky to understand and control”

To help bridge this knowledge gap, The Wharton School of the University of Pennsylvania announced yesterday the establishment of Wharton AI for Business (Artificial Intelligence for Business), which will inspire cutting-edge teaching and research in artificial intelligence, while joining with global business leaders to set a course for better understanding of this nascent discipline. The goal of AI for Business is to educate a new generation of business leaders with a deeper understanding of AI – its fundamentals, capabilities, use cases, risks and limitations – so they can align AI with their business strategies and effectively direct, prioritize and invest in applying AI in their unique business models.

A cornerstone of the launch is a 4 week Artificial Intelligence for Business online certification program for business leaders and professionals. The program is aimed at providing executives, managers, and business professionals in the fields of marketing, operations, automation, and analytics a competitive edge in the emerging field of AI analytics.

According to Hosanager, one of the primary reasons Wharton launched the AI for Business initiative is because it can help managers avoid very common mistakes their peers make when they define, invest in, and deploy AI-led transformational initiatives. Specifically, managers leading AI transformation typically make the same set of mistakes:

·        They execute AI development in siloes isolated from the business, or outsource it entirely, instead of making it a core part of the business;

·        They treat AI led transformation as a separate strategy instead of using it to support their core business objectives and growth agenda;

·        They fall into a trust and transparency vortex in which they either trust AI tools blindly without truly understanding them, or not at all, because they don’t understand what is inside their “black box” algorithms.

Kartik is emphatic that today’s managers must learn from the mistakes of past transformations. “Today nobody denies the internet was transformational to businesses and created billions of dollars of shareholder value,” reminds Hosanagar. “But despite the huge hype and promise, it certainly did not start that way. If you look back at the dawn of the internet 20 years ago, almost every organization quickly set up an independent division to lead the transformation to digital. Most of these failed.”  Hosanagar cites the example of Kmart who in 1999 aggressively invested in – a separate division – ahead of most of their competitors, but failed because they did not stick with it long enough and did not integrate the digital division with the rest of their business. The company soon went bankrupt in 2002. “A siloed approach to transformation is a flawed strategy. Ask yourself how many businesses have independent divisions anymoreWhat eventually did succeed was to find ways to use the internet to augment and accelerate their core business strategy – simplifying ordering, improving customer services, and supporting omnichannel sales models.”

“In my 10 years of working with data science and AI strategies in business, I see executives tend to fall into two camps when it comes to applying AI to their business,” shares Professor Hosanagar. “They either don’t understand it but trust it. Or don’t understand it and do not trust it. Both are failed strategies. The key message here is leaders need to understand enough about how AI works to strategically align AI with value creation and make smart investment decisions.” Specifically, Professor Hosanagar advises managers leading AI transformation initiatives to:

·        View AI as a tool, not a strategic goal;

·        Take a portfolio approach to AI project that balances quick wins with fundamental process redesign;

·        Grow your talent base by both reskilling existing employees and hiring new talent;

·        Focus on the long term by sticking with AI through inevitable early failures;

·        Be aware of new risks AI can pose and manage them proactively.

Every executive must have a fundamental understanding of AI as companies increasingly rely on large data sets, cloud computing infrastructure, and open source software to scale their businesses,” according to Sajjad Jaffer, founder of Two Six Capital, a firm that pioneered data science for private equity. Jaffer, who is a Wharton Senior Fellow and serves on the board of Wharton Customer Analytics said “Investment committees and company boards need to bridge the widening chasm that exists between sound business judgement and AI skills across industries and asset classes.

Christine Cox, the VP of Marketing Operations and Demand Generation at Ricoh USA echoes this sentiment. “Based on my 20+ years leading marketing and sales teams across financial services, telecom and technology, AI is only just beginning to break into the Martech stack of traditional brands, enabling hyper-personalization of the Customer Experience,” reports Cox. “As large organizations develop greater AI capabilities for driving customer acquisition and retention, we will see these organizations innovate faster, engage with customers in new ways and start to compete with the digital-native companies. Holistically, AI has catapulted digital marketing and digital sales in the last five years, and I expect AI will exponentially accelerate the research and response process for marketing and sales teams to address evolving buyer needs in the future. However, this won’t happen with technology and data alone. In my experience, the business leaders who work to truly understand the nature and capabilities of AI and advanced analytics will be the ones who will realize the greatest impact and value from this transformation for their respective audiences.”

Executives make significant decisions about how they should invest capital, resources and talent to realize the full potential of AI and ML technologies to transform their businesses,” relays Saurabh Goorha, a Senior Fellow at The Wharton School. “These decisions should be an outcome of a grounded understanding of AI and ML starting with first principles: what are the business and functional problems that can be solved and measured with comprehensive data strategy

At the next level they must ensure their AI strategies are informed by a solid understanding of both the potential and risks of AI as well as the strengths and limitations of the underlying data fueling these programs.”

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