Understanding ML & AI
First defined in 1959 by Arthur Samuel as a “field of study that gives computers the ability to learn without being explicitly programmed,” Machine Learning is quickly gaining traction as speed, agility, responsiveness, automation, and personalization emerge as ‘must haves’ in today’s hyper-competitive, disruption-driven business climate.
Broader in scope and including myriad technologies (including ML), the term “artificial intelligence” came to inception in 1956 by a group of researchers including Allen Newell and Herbert A. Simon. In contrast to machine learning, AI is a moving target, and its definition changes as its related technological advancements emerge and evolve.
ML and AI grew up and matured in the world of gaming, pitting computational power and algorithmic mastery against the best human players up for the challenge of taking on a machine. It may have taken decades, but machines eventually asserted their dominance.
In 1997, IBM’s Big Blue defeated chess master Garry Gasparov; In 2011, IBM’s Watson defeated two of Jeopardy’s greatest champions; and most recently, Google’s AlphaGo bested reigning champ Lee Sedol in the game of GO, a 2,500-year-old game that’s exponentially more complex than chess and, from the human perspective, requires intuition as well as calculation to execute a winning strategy.
Together, ML and AI are changing the world. Examples abound in today’s customer-centric world – from the recommendation engines built into Amazon and Netflix services, to the face recognition capabilities of Facebook, to the algorithms hedge funds use to make micro-trades that rake in millions of dollars, to personal digital assistants in smartphones… The list goes on…
ML & AI in business – The next digital imperative
ML and AI help businesses increase sales, detect fraud, improve customer experience, automate work processes, and provide predictive analysis. It is no exaggeration to say that in business today, survival hinges on embedding ML and AI into enterprise applications.
Check out these expert predictions and insights into the impact machine learning and artificial intelligence will have on business:
- Tractica forecasts the market for AI systems for enterprise applications will increase from $202.5 million in 2015 to $11.1 billion by 2024, expanding at a compound annual growth rate (CAGR) of 56.1%.
- As reported in WSJ, IDC predicts the worldwide market for cognitive software platforms and applications to grow to $16.5 billion in 2019, from $1.6 billion in 2015, with a CAGR of 65.2%.
- Deloitte Global predicted that by the end of 2016, more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products, a 25% increase from the prior year. By 2020, that number will rise to about 95 of the top 100.
- Since 2011, Deloitte reports, US-based start-ups that develop or apply cognitive technologies to enterprise applications have raised nearly $2.5 billion, suggesting that the biggest near-term opportunity for cognitive technologies is in using them to enhance business practices. The cognitive technologies that will be the most important in the enterprise software market will be: machine learning, natural language processing (NLP), and speech recognition.
- As reported in Forbes, IDC predicts that by 2020, 50% of all business analytics software will include prescriptive analytics built on cognitive computing functionality, and that cognitive services will be embedded in new apps. Embedded data analytics will provide U.S. enterprises $60+ billion in annual savings by 2020.
- According to a recent MIT Sloan Management Review article, a survey of enterprises with at least $500M in sales found that 76% say they are targeting higher sales growth with machine learning; at least 40% of companies surveyed are already using machine learning to improve sales and marketing performance; and 38% credited machine learning for improvements in sales performance metrics.
- Deloitte estimates that US$57.6 billion will be spent on AI and machine learning by 2021—almost five times as much as in 2017.
- McKinsey Global Institute points to the possibility of US$3.5 to $5.8 trillion in potential annual AI-derived business value across 19 industries.
- More than 80% of executives gained a financial return from AI investments, according to a Deloitte survey of 1,100 IT and line of business executives. Across sectors, enhancing current products, optimizing internal operations, and making better decisions are the leading benefits driving AI adoption.
Unleashing the power of automation
Powered by big data and fueled by ML, AI, and analytics, automation is the digital workhorse powering transformation in businesses today. Across industries and business operations, enterprises are spending big on automation:
- According to KPMG, enterprises will increase their spending on intelligent automation almost 19x over the next seven years, from today’s $12.4 billion to $232 billion in 2024.
- By 2020, Gartner estimates that customers will manage 85% of their relationship with the enterprise without interacting with a human.
- By 2026, the global market for business workflow automation is projected to bring in $5.2 billion (FactMR).
Transform the back office today!
Improving customer experience is the primary driver behind digital transformation. Accordingly, many, probably too many, organizations focus the lion’s share of their transformation effort on customer-facing initiatives. While there is nothing wrong with this, per say, gains made on the front end will be lessened (if not negated) if back office operations are not in sync.
Back office processes are essential for any organization to function properly. Order-to-Cash, Procure-to-Pay, Plan-to-Produce, Request-to-Service, as well as processes driving CRM, HR/HCM, and Finance must be in place for a business to achieve its growth objectives and service its customers.
In most businesses, the back office is where customer needs and requests are actually fulfilled. It’s where you bring customers onboard, bill them, and update customer records. How well you meet customer expectations depends on processes beyond the customer contact center.
Digitally connecting customer-facing operations to back-end processes is often the missing link to a successful digital initiative. Digital interconnectedness is required to make key data and intelligence residing in core applications—related to pricing, product availability, logistics, quality, financials, and more—available to customer-centric operations.
IDC predicts a future of “intelligent ERP”, in which ERP integrates with the Internet-of-Things (IoT), blockchain, and cognitive computing to automate large-scale processes, shorten execution times, and perform tasks without errors. IDC forecasts that by 2020, 20% of Global 2000 manufacturers will be active users of intelligent ERP.
ML and AI certainly aren’t new concepts. Robots and automation have been commonplace and, indeed, necessary on manufacturing floors for decades, efficiently executing repetitive tasks and processes, reducing costs, and freeing resources.
ML and AI’s largely untapped potential, however, lays in back office operations – not as much in complex problem-solving work environments, but in repetitive B2C or B2B transactional environments, where chat-bots can answer common customer questions on a company website, or call-center systems can guide customer interactions through automated responses.
By incorporating available ML and AI technologies, redeploying employees, and reimagining processes, companies can dramatically increase performance and greatly reduce costs.
McKinsey Global Institute (MGI) research suggests that companies can automate at least 30% of the activities in about 60% of all occupations by using technologies available today. For back office operations, McKinsey reports that about 20% of the tasks of a typical finance unit’s record-to-report (R2R) processes are fully automatable (requiring no human intervention) and nearly 50% are mostly so (with technology undertaking most of the work). Similarly, in the HR hire-to-retire (H2R) process, about 30% of all tasks can be fully automated and another 30% mostly automated.
As reported in Forbes, by 2018, 67% of the CEOs of Global 2000 enterprises will have digital transformation at the center of their corporate strategies. However, according to a recent survey, only 50% of companies are successfully executing on their digital transformation strategies, despite demonstrated efforts and investments.
Perhaps this disconnect means organizations should focus more on transforming their back office operations.
Contact AST today to learn how our Oracle Cloud experts can help your organization transform back office operations, incorporating various Cloud offerings and AST pre-built, accelerated solutions.