1 LOGIC ExecTalks Insights for Chairmen, Board Members & CEOs C O N S U L T I N G Building The Cognitive Enterprise: How To Become an AI-Powered Business? Which regions will gain the most from AI?* Around 85% of CEOs worldwide believe that Artificial Intelligence will significantly change the way they do business in the next five years (1).Today, market forces and consumer demands leave no room for inefficiencies. Egypt: AI contribution of US$42.7 bn in 2030 (7.7% of GDP) UAE: AI contribution of US$96.0 bn in 2030 (13.6% of GDP) Saudi Arabia: AI contribution of US$135.2 bn in 2030 (12.4% of GDP) GCC4: AI contribution of US$45.9 bn in 2030 (8.2% of GPD) Source: PwC* The question now is no longer whether AI will fit into the business, but how to realign the organization to fully capitalize on it. That is why many organizations, including in Egypt & Middle East, have rapidly shifted gears to exploit AI. Despite the promise of AI, only 8% of firms engage in core practices that support widespread AI adoption; they have run only ad -hoc pilots or are applying AI in just a single business process (2). Indeed, many organizations still lack the foundational practices to create value from AI at scale. So, How Can Companies Get Started? 1 Build a Data Ecosystem To realize the benefits of becoming an Ai-fueled organization, you will need to have a data-enabled environment with access to the right data sets while putting in place more dynamic data governance, storage, and architecture. Data could be the biggest bottleneck as data preparation for training AI could take up to 80% of the time in any AI solution development. Thus, the company's data architecture must be ready and scalable to support the influx of data that AI initiatives bring with it. Another consideration is having a proper data management function to ensure that data is procured legally, and compliance standards are met. There are new roles such as data stewards that help organizations understand the governance and discipline required to enable a data-driven culture. To ensure sustainability of AI transformation projects, leaders need to work on building a data-driven culture, and this starts by gaining early buy-in of relevant stakeholders as the quest for AI technology, specifically, can sometimes be puzzling for some internal stakeholders to grasp. Leaders need to clear up any confusion about the real purpose of the data, where the data is collected, how it is collected and by whom. 2 2 Identify Strategic AI Use Cases Strategy is of greatest importance to any AI endeavor, regardless of a company’s maturity or ambition. But still many companies are following the wrong approach by making their AI efforts with an IT-centric focus. Leaders must set an AI vision that is coordinated with the overall business strategy to ensure that AI initiatives get the right focus, resources, and budget across the organization. Apart from being a technology for the future, AI is already being used by leading companies to gain a strategic advantage. You just need to pick the right use case and get started. 3 Create a Roadmap for Yearly Analytics Projects Any company needs to plan their yearly analytics plan according to the budget, and this could be done by: a) Understanding business needs and generating ideas from each objective. b) Conducting meetings with department managers to determine project value and complexity (time, cost, and technical requirements) c) Determining data availability; companies need to define data features, data sources, format, extraction process and time to integrate the right data insights into the actual workflow. d) Drawing a Value-Complexity Matrix to standardize a set of decision-making business Value parameters and prioritize projects by evaluating each initiative based on how much value it will bring vs. its implementation complexity. High value Low complexity Easy wins Low value Low complexity Worth pursuing or revisiting later High value High complexity Strategic initiative High value High complexity Deprioritize Implementation Complexity 4 Choose The Right Domain The AI domain can be used for solving complex business problems, and for doing this, it is crucial to have complete knowledge about the latest tools and technologies associated. Also, business leaders need a clear understanding of which type of AI is best-suited for their organization’s goals and whether they have the capabilities and resources needed to support it. So, when choosing a specific domain, consider both the business and people. There are several AI domains that can create expertise for the company such as linear regression, classification, neural networks, natural language processing and computer vision. Some projects can be done through the current domain the company is working on, other projects will require companies to use a different domain to drive the needed impact. At the heart of all AI-driven companies are domain experts who are excellent at execution. So, if you don't have the internal competencies needed to run a specific domain, you might need to seek external domain expertise. 3 5 Build an Internal Capacity or Outsource Once a company has settled on which domain to work with, many companies face a dilemma of: should we build or buy? This is rarely an either-or choice. According to Deloitte’s 2020 State of AI in the Enterprise survey, more than 50% of adopters are buying capabilities instead of building them, and another 30% are employing a balanced blend of buying and building from scratch (3). Yet, organizations need to go through a period of internal learning and experimentation before finding out what is necessary. Companies may decide to buy an AI solution or build it in-house or even both by augmenting their existing resources through investing in AI startups or collaborating with vendors and other industry partners. When it comes to talent, the availability of technical talent is one of the biggest bottlenecks as competition for expertise is increasing with more organizations becoming AI-fueled. According to McKinsey 2018 Global Survey, respondents say their organizations are taking an “all of the above” approach: hiring external talent and building capabilities in-house (4). The core is once you have determined where AI fits into your business processes, you can evaluate your existing technology, talent, and expertise to determine where the gaps are and how you could fill them. Success Factors for Building AI Products Develop AI Proof of Concept (PoC) Any business considering AI should undergo a proof of concept (PoC) approach to validate how well a particular business problem can be solved by AI. The role of the PoC is to enable decision makers to maximize value and minimize risk through re-scaling the full project into a pilot to test specific assumptions and validate the viability and feasibility of a business case, both technically and economically. So, after identifying a business opportunity that AI could address, get your data in order, bring together the right people, and develop your PoC. While AI is still in its early days, getting stuck in “pilot purgatory” is a real risk (5). Nearly, 80% of the AI projects typically do not scale beyond a PoC or lab environment. Thus, if the metrics show that your PoC is delivering value, companies should start scaling the technology across the organization and embed it in core business processes and workflows. A PoC is considered the most cost-effective path as it helps in: Testing the technologies and methodologies to be used Delivering more immediate and concrete value 4 Comparing quickly different solutions and approaches Gaining experience, skills, and confidence in AI Highlighting impacts on IT infrastructure and the wider business Identifying and resolving potential data bottlenecks Set Up an AI Center of Excellence With scattered data science teams, resources, and legacy systems, it becomes hard to know where to start. At many businesses, learning typically gets stuck in the mind of one individual, team, business unit, rather than contributing to the company as a whole. To make the most of AI, leaders should build a central repository for best practices and knowledge by having a center of excellence (CoE). AI has the biggest impact when it is developed by cross-functional teams with a mix of skills and perspectives; thus, companies need to strategically place their technical teams within different departments to streamline their business model and nurture agility and speed. AI CoE is like a core knowledge platform in the company that has all the accumulative learning from past AI initiatives and a clear vision for use of AI in business strategy. It brings together the AI talent, knowledge, and resources required to address challenges of AI adoption, prioritize AI investments, and enable AI-based transformation project. Have a Realistic View of AI Capabilities vs. its Limitations Almost 40% of all practitioners who have not yet invested in AI do not know what AI can be used for in their business. Companies need to separate the buzz around AI and think of it as a technology that will help them solve business cases efficiently. AI could have a significant impact across multiple industries and functions, but leaders just need to think about where exactly the potential for AI is. It is useful for companies to look at AI through the lens of business capabilities rather than technologies (6). So, leaders need to identify the right use cases to leverage AI in their business. For example, if you are operating in consumer goods, then demand forecasting is important to you, also that’s where AI can create the most value. Yet, companies need to be aware of the big limitations of AI today. Limitations could be practical such as obtaining large data sets and labelling and categorizing the underlying data; or technical such as knowing the “hows” behind the workings of algorithms; or application limitations such as applying experiences of one AI model on other use cases. 5 Have a Technical expert/Subject Matter Expert As stated by The New York Times in 2017, there are fewer than 10,000 specialists who have enough competence and expertise to solve serious AI challenges (7). To be an AI-driven company, you need to have people with both technical and business acumen to connect the dots between technology and business impact. But companies still find difficulty in having AI talent in house due to current supply-and-demand imbalance, that’s why external expertise can provide an effective gateway to bring in AI expertise quickly. Companies need their own AI experts to develop and customize algorithms, ensure that they invest in the right AI applications and services, have access to data sources and draw realistic AI roadmap given their current levels of talent, data access, and strategies. Such “Reality Checks” from technical experts are increasingly important as companies fundamentally transform the way the business conducts its day-to-day operations using AI solutions. Reality Checks from technical experts are increasingly important as companies integrate AI solutions into their workflows & processes. Sources: 1- https://www.pwc.com/gx/en/ceo-survey/2019/report/pwc-22nd-annual-global-ceo-survey.pdf 2- https://hbr.org/201907//building-the-ai-powered-organization 3- https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/aireadyaiwhat youneed-to-jump-start-and-scale-ai.pdf 4- https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoptionadvancesbutfoundational-barriers-remain 5- https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoptionadvancesbutfoundational barriers-remain 6- https://www2.deloitte.com/eg/en/pages/financial-services/articles/artificialintelligenceinsurance industry.html 7- https://www.nytimes.com/201722/10//technology/artificial-intelligence-expertssalaries.html