Artificial Intelligence in Business Management
Artificial Intelligence is changing the way companies do business. Powerful AI tools are helping organizations gain customer insights, optimize teams, streamline workflow and identify new business opportunities.
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Artificial intelligence is becoming a valuable differentiator in impactful leadership. With its capacity to process data, analyze patterns and provide insights and predictive guidance, AI is helping leaders accelerate the pace of product innovation, define strategies and boost results.
Research from PwC shows that there’s a widening AI divide between companies that have successfully embedded AI tools into their organizations and those that haven’t. And strong AI adoption and investment today will exponentially improve a company’s performance over the years.
Another study from Accenture suggests that 93% of executives say they know their industry will be disrupted at some point in the next five years, but only 20% feel they’re well prepared to address it.
According to the PwC AI Predictions 2021, widespread adoption of AI is up 18% from last year. About 55% of respondents are still experimenting with AI, 20% have not implemented AI and 25% have fully embraced AI.
Overall, 86% say that AI will be a “mainstream technology” at their company in 2021.
These business leaders have also increased their use of AI (57% vs. 34% overall), implemented more new AI use cases (40% vs. 34% overall) and invested more in AI development (48% vs. 42% overall).
Accenture classifies these companies in the following categories.
Are IT-led rather than headed by business leaders
Have buried analytics so not much value can be extracted from their data
Are siloed and they’re struggling to scale
Have low ROI with a high expected time requirement
Approximately 15-20% of companies belong to this category. Their AI projects are:
Led by the CEO with an advanced analytics and data team solving big problems
Backed by multi-disciplinary teams of specialists led by Chief AI, Data or Analytics Officer
Able to tune out data noise and focus on essentials
Experimental in their mindset of achieving scale and return
Less than 5% of companies are here. Their AI projects are characterized by:
Digital platform mindset, clear enterprise vision, accountability, metrics and governance breaking down silos
‘What if’ analysis enabling improved acquisition, service and satisfaction
Competitive differentiator and value creator driving higher price to earning multiples
Real-time insights drive business decisions
The report finds that the AI Strategic Scalers achieved nearly 3X the return from AI investments than companies in the Proof of Concept stage of their AI journey.
For business leaders who had the foresight to embed artificial intelligence into their organizations, AI is beginning to pay off in concrete ways. They are seeing benefits ranging from revenue growth to better decision-making and improved customer experience.
Co-founder and CEO at Tonkean Sagi Eliyahu says, “With AI, more routine functions can be automated, taking them off of our shoulders so we can be more action-oriented and creative in our workdays.”
Research also suggests that the companies currently implementing AI are making way for an even greater payoff that their lagging competitors may never be able to overtake. AI leaders are creating a virtuous cycle known as the flywheel, which is helping companies that have fully embraced AI realize far more benefits than those trying to catch up.
Put simply, more AI means more benefits.
According to PwC AI Predictions 2021, business leaders who implemented AI throughout their organization saw substantially more benefits than those just exploring AI tools:
Better customer experience (86% vs. 67%)
Improve decision-making (75% vs. 54%)
Innovate products and services (75% vs. 53%)
Cost savings (70% vs. 50%)
More efficient operation/increased productivity (64% vs. 52%)
Asset & Wealth Management
Banking & Capital Markets
Consumer Markets
Energy
Health Industries
Insurance
Industrial Products
Private Equity
Technology, Media & Telecommunications
Utilities & Mining
Leaders of the healthcare industries are leading the charge in investing the most heavily in AI (52% vs. 42% overall).
Managing risks, fraud and cybersecurity threats
Improving AI ethics, explainability and bias detection
Helping employees make better decisions
Analyzing scenarios using simulation modeling
Automating routine tasks
When AI systems have access to the correct data and models, it can help business leaders by predicting future market changes and supply chain risks. AI tools can think through options for investments, workforce and go-to-market strategies. It can also help business leaders with their decision-making.
Here are a few ways that AI can become a strategic ally in impactful leadership:
AI can help leaders in scenario planning to prepare for the future. AI tools can model future conditions and their impact on an organization. It can easily assess the different factors in the workforce, supply chain or go-to-market. So business leaders will have access to data-driven strategic decisions, even in highly uncertain situations.
AI will make business strategies more fluid. Thanks to AI systems constantly processing data and delivering strategic forecasts and models, business leaders can fine-tune their business strategies nonstop, not annually.
Co-founder and CEO of ThroughPut.ai Ali Hasan Raza says, “A smart program will run constant algorithms to pinpoint exactly where inefficiencies crop up, enabling you to direct your resources where they’re most needed. Running these analyses manually can take years and millions of dollars that might be more wisely invested.”
AI will future-proof business operations. AI can continually sense new threats and opportunities and their impacts on businesses. By acting quickly, business leaders can leverage AI to mitigate disruptions and seize opportunities.
Raza says, “Leaders are constantly trying to answer the same fundamental question: “What do we do next?” AI has a unique ability to supply the answer – and be right.”
AI applications range from customer-focused applications such as chatbots and conversational systems, demand forecasting and customer targeting to back-office applications, including contract analysis, invoice processing and risk management. While many companies are using a few AI applications, those fully embedding AI into their business processes are seeing the best results.
But fully integrating AI across an enterprise is a significant challenge. In fact, a recent survey revealed that 58% of companies reported that it took 31 days or more to deploy AI models.
As businesses move from using standalone AI applications to using predictive AI tools to leveraging the full power of AI by automating and tracking operations, they need a range of capabilities, including:
Domain experts from different business verticals to clarify use cases
Data engineers and data scientists with a firm grasp on how information flows within the organization and adds value to machine-learning models
Systems analysts and software developers who can build software systems, along with machine-learning engineers
ModelOps, DataOps and DevOps specialists who can maintain embedded AI models
Governance and ethics champions to support initiatives to enable effective stewardship
A fully integrated AI system has access to sensitive data and is responsible for influencing business-critical decisions. Hence, business leaders must know that their AI system is explainable, bias-free, not violating anyone’s privacy and can be governed and monitored.
Building responsible AI is mandatory to mitigating AI risks.
The data, technology and talent responsible for AI tend to be highly decentralized, making it even more difficult to monitor. Since AI keeps learning and evolving itself, business leaders need to be actively involved from the beginning of the design phase through to development, deployment and ongoing adjustments to ensure they’re working with a responsible AI system.
Assess risks and define a plan to test and monitor how AI affects their business’ financial, operational and reputational risks. Accordingly, they should implement controls that cover every stage of the AI life cycle.
Establish a plan for AI governance that can keep up with the speed and the changing nature of AI. The performance of the AI model should be monitored for potential bias and new sources of risks.
Create frameworks that make sure AI is not only explainable and robust but also fair and ethical. The AI model needs to be updated or adjusted to prevent it from perpetuating any kind of bias.
The PwC AI Predictions 2021 report lists steps to advancing responsible AI for business leaders:
Ensure AI-driven decisions are interpretable and easily explainable by those who operate AI systems and those who are affected by the decisions
Ascertain AI is compliant with applicable regulations, including privacy
Protect AI systems from cyber threats and manipulations
Monitor and report on AI model performance
Develop and report on controls related to AI models and processes
Improve governance of AI systems and processes
Address the issues of fairness
Review to be sure third-party AI services meet company standards
Artificial intelligence is providing new ways for leaders to gain better insights, automating mundane tasks and ultimately optimizing the contributions of business leaders. By cutting out subjectivity from the decision-making process and relying on hard data and empirical analysis, leaders are better equipped to reach goals and navigate uncertain futures.
Here are a few instances that demonstrate exactly how AI is having an impact on business leadership:
High-end automotive tool maker Snap-on leveraged Predii, an AI platform designed for repair and maintenance services, to sift through millions of telemetry data points per second to analyze and forecast data in real-time.
Automotive vehicles are embedded with thousands of sensors to track temperature, pressure, speed, rotations per minute and more. Gaining real-time insights from AI tools makes diagnosis and resolving issues much more streamlined and efficient.
The French Tennis Federation leveraged cloud-based artificial intelligence in Roland-Garros to capture data and provide analytics and near-instant replays on and off the court. AI-powered analytics gave fans insights into the crucial steps of the game, and machine learning-based stats helped coaches and players evaluate the game. Access to instant insights also helped journalists create more factual and intelligent narratives.
The world's largest online-only grocery chain, Ocado, uses AI/ML and robots to handle 50,000+ products across three different temperature regimes. The store processes 1.7 million items a day across its four fulfillment centers.
Co-founder and CEO Tim Steiner says, "every human touch point is designed to one day be replaced by a robotic solution."
Managing director of Credit Suisse Stuart McGuire explains that margins for online grocery stores are so slim and logistics so complex that for any grocery chain to handle online delivery and be profitable, they’ll need to build up expertise in automation.
International transport and logistics companies probably have to deal with more invoices than others. Manually processing crucial documents such as airway bills, bills of lading, invoices, CMRs and import licenses is not only wasteful but leaves room for a lot of human error.
InData Labs helped one such transportation company capture data from invoices and cut the time to process documents to one-fourth of the manual. They captured data from 300,000 documents and estimatedly saved the company 15,000 person-hours per year.
New Zealand-based company Fine Wine Delivery teamed up with AI company Spacetime to implement IBM’s Watson to create a dashboard that helps customers search for products using ‘natural language.'
“Watson has basically ingested everything about the wines, including the tasting notes, which is really important,” says Alex Catt from Spacetime.
Even more importantly, AI-powered Watson understands the complexity of categorization. Each wine can fit into many categories and have an infinite number of descriptors, such as “full-bodied, fruity tasting, from Loire Valley.” The AI system can use all of these attributes to recommend the perfect bottle.
Modern AI applications require large data sets to function. In an organization, the data strategy drives value as much as AI.
Data strategy refers to how an organization collects, stores, documents and manages data and makes that data accessible throughout the company. The lack of a data strategy is the top reason business leaders abandon AI projects.
IBM’s Chairman and CEO Arvind Krishna says, “[Without a proper data strategy, businesses] run out of patience along the way. Because you spend your first year just collecting and cleansing the data, you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” A Forrester research report corroborates that data quality is one of the biggest challenges of AI projects.
On the other hand, studies from Accenture show that nearly 75% of the companies who are fully embedding AI into their organizations agree that a core data foundation is an important success factor for scaling AI.
Data strategy is the foundation for all analytics and reporting capability in any organization. When it comes to AI projects, businesses are struggling with a lack of data, incomplete data and limited or no access to data. These data-related problems affect AI projects by:
Exploratory data analysis can reveal what's possible or not with AI and act as a starting point for AI projects. It can surface potential issues with organizational data, such as imbalance and sparsity.
Stale predictions refer to predictions “learned” from outdated historical data or data that does not reflect current reality. Fresh data is required because customer behavior evolves, data distributions change and governmental regulations are updated.
Low-quality data models can make gross mistakes on prediction or recommendation tasks. Quality issues occur when data is not centralized, only a subset of the data is available and the volume is small.
A broken data setup can introduce bias in AI applications. When data scientists have access to limited or incomplete data, the data source itself becomes biased and this effect is perpetuated through machine learning models.
Lack of data can be a permanent setback to companies looking to adopt AI. If AI projects are crucial to remaining efficient and effective within an industry, then having a data strategy is even more critical.
In a world where data is proliferating at a warp speed, more data isn’t always better. A robust data strategy ascertains that businesses are curating the right data to fuel an AI strategy that delivers the right outcomes at speed and scale.
With the right data strategy, organizations can mine the right insights that help refine their strategy and the AI systems themselves. The constant flow of data needs a feedback loop that will fine-tune business decisions and adjust AI initiatives. This, in turn, gives rise to an agile, iterative approach to working and AI development with data at the core.
To ensure that their AI projects get to production and eventually can be scaled, business leaders need to ask these five questions:
A data-driven culture begins with buy-in from the top. Senior leaders should demonstrate to employees what is possible with data and invest in the right tools and resources to help employees achieve those goals.
Data quality refers to the completeness, accuracy, lack of bias, relevance and timeliness of data in relation to the insights the organization is trying to generate. If the use of data is dependent on the trust users put in it, it becomes crucial to build confidence in the quality of the data.
Business leaders need to establish effective data-quality processes and frameworks around storage, management and transfer. They should assign designated data owners who will act as custodians of data quality.
According to the Accenture research, “while culture and data quality are important to building a solid data strategy, platform innovation is essential to future-proof that strategy.” Data strategy requires new data sources, diversifying underlying technologies and applying new technical approaches to deliver much sharper insights. Unused, unstructured data, for instance, can generate more insights.
Businesses need to design a multi-cloud strategy from the beginning to have the right level of flexibility and modularity later. Leaders need to look beyond current applications of AI and analytics within the organization to consider how to exploit new datasets in new ways.
Without ethical and responsible use, data strategies and AI solutions will deliver biased outcomes. So, it becomes essential to have a team dedicated to the right policy, governance and accountability for data.
According to the World Economic Forum’s (WEF) The Future of Jobs Report 2020, AI will displace 85 million jobs and create 97 million new jobs across 26 countries by 2025.
The report estimates that AI will automate many repetitive and sometimes dangerous tasks like data entry and assembly-line manufacturing while changing the nature of many other jobs. These new and enhanced roles will require workers to focus on higher-value and higher-touch tasks that often require interpersonal interactions. Workers will have to be more creative, strategic and entrepreneurial to fit these new roles.
Concerted efforts between companies, workers and governments will be key in upskilling and reskilling the current workforce and preparing them for the future. WEF estimates 50% of all employees will need reskilling by 2025.
Develop a workforce plan that identifies new skills and roles needed as a result of AI
Implement upskilling and continual learning initiatives that include AI
Provide tools/opportunities for on-site and remote employees to apply newly acquired AI skills to their day-to-day work
Implement credentialing programs for data scientists and more advanced AI skills
Change performance and development frameworks to include AI skills such as using and managing AI systems
Expand their AI talent pipeline with internships and partnerships with community colleges and universities
Artificial intelligence is a difficult technology to implement and will lead to changes throughout the organization, from processes to the way employees think about work. It’s too sensitive, expensive and all-encompassing to let standalone departments or third parties take charge.
The problem is that although 84% of C-suite executives believe they must leverage artificial intelligence to achieve their growth objectives, 76% report they struggle with the “how.”
In light of this knowledge gap, Esme Learning has collaborated with the Massachusetts Institute of Technology to bring business leaders courses focused on artificial intelligence and leadership.
Our AI courses from MIT focus on leadership and organizational design, giving learners the knowledge and tools they need to upgrade their skills, accelerate their career and optimize their earning power.
Understand the fundamentals of AI and develop a new leadership mindset in keeping with the agility and outward-looking frameworks of artificial intelligence.
Explore how a data strategy goes hand in hand with artificial intelligence and gain insights into opportunities surrounding both for your business.
PwC, ‘Jumping onto the right side of the AI divide’
PwC, ‘AI Predictions 2021’
Towards Data Science, ‘AI leaders make the most of the COVID-19 crisis to increase the role of AI’
Towards Data Science, ‘Model Evolution: From Standalone Models to Model Factory (Part 3)’
Algorithmia, ‘The 2020 state of enterprise machine learning’
World Economic Forum, ‘Don't fear AI. It will lead to long-term job growth.’
Opinosis Analytics, ‘Why A Big Data Strategy is Critical For AI’
Accenture, ‘How to build a data strategy to scale AI’
Accenture, ‘Creating value with the right AI strategy’
Accenture, ‘AI: BUILT TO SCALE’