Return to flip book view

SAUGAN AI POINT-OF-VIEW FORMEMBERSARTIFICIAL INTELLIGENCE

AI in Context Framing the Issue: The World of AI The Big Picture: AI in Business Implications: More than Functionality AI for Business: A Strategic ExplorationSAUG ContextThe SAUG View from Our MembersAddressing the CollectiveSegmentation: A Spectrum of ProgressHuman Lens: A Multi-Faceted PerspectiveA Collective Inquiry & JourneyAn Industry Flavour (Global & RegionalComparisons)Industry View: A Global PerspectiveIndustry View: ANZ PerspectiveANZ: A Region in TransformationConclusion: A Dynamic and Diverse ViewBusiness Opportunities & Risks: AComprehensive ExplorationBusiness OpportunitiesBusiness Risks & ConcernsData Privacy and SecurityEthical ConsiderationsTalent and Skill GapsDependence on TechnologyRegulatory ComplianceBusiness Risk Management ChallengesAbout AI Technology for BusinessSome Common Terms: The differencebetween ML, DL, LLM and AIConsumer Tools vs. Corporate Tools:Purpose-Driven DesignPrivate LLMs (Large Language Models):Specialized Linguistic IntelligenceEmerging Technologies and TrendsIdentifying the Right Partners: Key Considerations Major Players vs. Niche Specialists: A BalancedView Collaborating with Universities and GovernmentA Thoughtful and Informed Approach Some Tips and Best PracticesWhat is Explainable AI (XAI)? Why Explainability Matters The Human Factor: Emotions and HeuristicsAchieving XAI in Your OrganizationXAI: Not Just a Technology ChallengeBuilding AI CapabilityRedesign Work for AIAI Oversight and AssuranceManage Cultural ChangeCreate Stakeholder ValueEngage and EducateContinuous Learning and AdaptationAssembling Expertise to Build AI CapabilityRecruitmentTrainingKey Areas of Focus for AI CapabilityJustifying AI to StakeholdersQuantifying the Value of AIRealizing Value Over Time: Insight, Expected, andOutcomeInsight ValuePartners / Ecosystem: A Strategic andCollaborative LandscapeExplainability in Artificial Intelligence: A Guidefor Business LeadersBuilding a Successful AI Rollout ProgramBuilding Capability: Harnessing AI for BusinessSuccessA Value Perspective on AI for BusinessSome Thoughts: The Alchemy of AI in BusinessConclusion & Preliminary GuidanceWrapping it all up (For Now)

"If people trustartificial intelligence(AI) to drive a car,people will most likelytrust AI to do your job."Dave Waters,Prof. Univerity ofOxford

AI in Context Framing the Issue: The World of AI In an era marked by rapid technological advancement,artificial intelligence (AI) stands at the forefront ofinnovation, reshaping industries and redefining the waywe live and work. From automation to predictiveanalytics, AI's capabilities extend far beyond merecomputation, offering unprecedented opportunities forgrowth, efficiency, and value creation. However, the landscape of AI is not without complexity.As we navigate the intricate web of algorithms, data, andmachine learning models, we must recognize that AI isnot a monolithic entity but a multifaceted field withdiverse applications and implications. The Big Picture: AI in Business AI's promise is vast, but its realization requires a nuancedunderstanding of its potential and limitations. While themedia often highlights AI's role in solving "wickedproblems" such as cancer research and climate change,our focus here is on AI for business. We aim to explorehow AI can drive efficiency, effectiveness, and valuewithin the corporate world, transforming operations,decision-making, and customer engagement. The big picture of AI in business is not just abouttechnology; it's about aligning AI's capabilities withorganizational goals, ethical considerations, and humancollaboration. It's about leveraging AI not as a mere toolbut as a strategic partner that can enhance humancreativity and capability. "Artificial Intelligence is not aboutreplacing the human mind, butcomplementing it.."Ginni Rometty, former CEO ofIBM

Implications: More than FunctionalityThe implications of AI in business are profound andmultifaceted. From pioneering industries that lead theway in AI adoption to laggards that are yet to tap into itspotential, the spectrum of AI's impact is broad. It touchesupon various domains, including sales, marketing, HR,procurement, and finance, each with unique challengesand opportunities.But the implications go beyond mere functionality. AIraises questions about data privacy, ethics, jobtransformation, and the changing roles of employees andconsumers. It challenges us to think critically about howwe integrate AI into our organizational fabric withoutlosing sight of human values and social responsibilities.AI for Business: A Strategic ExplorationAs we embark on our exploration of AI in the businessrealm, our primary focus will be on achieving efficiency,effectiveness, and value. We will evaluate both thetrailblazers and those lagging behind, understand theTechnology Adoption Life Cycle (TALC), and offer insightsthat are tailored to the diverse capabilities and resourcesof our audience.Throughout this journey, we will adopt a structuredapproach that takes into account various factors,including opportunities and risks, the perspectives ofboards and executives, the viewpoints of managers andemployees, and the broader societal context. We will delveinto the intricate details of machine learning, AI, andgenerative AI, exploring how they can be applied acrossdifferent departments and functions.In the subsequent sections, we will navigate thecomplexities surrounding AI's transformative impact inthe business world. Our aim is to demystify its potential,acknowledge and address its challenges, and chart a paththat aligns with our collective vision and goals.

The SAUG View from Our MembersIn the dynamic landscape of AI, our members findthemselves at various stages of exploration, adoption, andmastery. From those who are pioneering new AI-drivensolutions to those grappling with the initial challenges ofimplementation, the SAUG community represents amicrocosm of the broader business world's AI journey.But what sets the SAUG context apart is our collectiveapproach to AI. We recognize that the path to AI successis often fraught with questions, uncertainties, and isolatedefforts. Our members may be wrestling with similarchallenges, seeking answers in isolation, and navigatingthe complexities of AI without a unified roadmap.Addressing the CollectiveAt SAUG, we strive to address these challenges at thecollective level. We believe in the power of collaboration,shared wisdom, and community support. By bringingtogether diverse perspectives, experiences, and expertise,we aim to provide a valuable service to our members,fostering a collaborative environment where AI innovationcan thrive.Our approach is not about prescribing one-size-fits-allsolutions but about facilitating dialogues, sharing bestpractices, and co-creating tailored strategies thatresonate with our members' unique needs andaspirations.SAUG ContextSegmentation: A Spectrum of ProgressWithin the SAUG community, we recognize that ourmembers are not a homogenous group. Some membershave advanced capabilities and resources and may bewell-progressed in their AI journey. They are thetrailblazers, setting benchmarks and inspiring others withtheir success stories.On the other hand, the majority may likely be struggling,facing hurdles in AI adoption, integration, and valuerealization. It is clear that our members are at differentstages of a complex journey, each with its uniquechallenges and opportunities.

"Artificial Intelligenceis not about replacingthe human mind, butcomplementing it.."Ginni Rometty,former CEO of IBM

Human Lens: A Multi-Faceted PerspectiveThe integration of AI within the business landscape is not merely atechnological transformation; it's a human-centric evolution that impactsvarious stakeholders. Understanding the questions and concerns of thesestakeholders is vital to navigating the complexities of AI adoption.How does AI align with our strategic vision and long-term goals?What are the potential risks and ethical considerations associated with AIadoption?How can we ensure responsible governance and compliance in our AIinitiatives?How will AI impact our daily workflows and responsibilities?What training and support will be needed to adapt to AI-driven changes?How can we leverage AI to enhance creativity, collaboration, and efficiency?How is AI being used to personalize my experience?What measures are in place to protect my data and privacy?How is AI enhancing the quality and value of the products and services Iconsume?How can we align our AI strategies to create synergies and mutual value?What are the standards and best practices for collaboration in AI-drivenprojects?How can we ensure ethical and responsible conduct in our joint AIinitiatives?Board / ExecutiveStrategic decision-makers are evaluating AI's potential, risks, andalignment with organizational goals. They might be asking:Managers & EmployeesOperational leaders and team members adapting to AI-driven changes inroles, processes, and collaboration might be pondering:Consumers/CustomersEnd-users experiencing the impact of AI on products, services, andcustomer engagement might be questioning:PartnersCollaborators and vendors navigating the evolving AI ecosystem might beinquiring:The SAUG context is not just about technology; it's about people,collaboration, and a shared journey towards AI excellence. It's aboutrecognizing the diversity of our members' experiences and forging a paththat honours individuality while embracing collective wisdom.A Collective Inquiry & Journey

Industry View: A Global PerspectiveThe adoption of AI across industries is as diverse as the technologies themselves. From pioneersleading the way to laggards cautiously approaching AI, the landscape is a rich tapestry of innovation,experimentation, and transformation. Below, we explore various industry sectors and highlightspecific companies, placing them within the TALC cycle.Manufacturing: General Motors enhancing qualitycontrol with AI.Traditional Retail: Walmart adopting AI for inventorymanagement.Education: Integration of AI in personalized learningand administrative tasks.Energy Sector: ExxonMobil exploring AI applicationsin exploration and drilling.Agriculture: Gradual adoption of AI for cropmonitoring and predictive analytics.Late Majority: Cautious ExplorationLaggards: A Considered ApproachAn Industry Flavour(Global & Regional Comparisons)Technology Sector: Google and Microsoft leading in AIinnovations.Automotive Industry: Tesla and Waymo pioneeringautonomous driving technologies.Pharmaceuticals: Firms like Pfizer are leveraging AI fordrug discovery and personalized medicine, acceleratingresearch and development.Fintech: Innovative fintech companies like Square areusing AI to disrupt traditional banking, offeringpersonalized financial services.Retail: Amazon's use of AI for recommendationengines, logistics, and customer insights sets abenchmark for the industry.Finance: JPMorgan Chase utilizing AI for frauddetection and investment strategies.Entertainment: Netflix employing AI for contentpersonalization and optimization.Hospitals: Healthcare providers are beginning to utilizeAI for diagnostics, patient care, and administrativeefficiency.Innovators: Pioneering the FutureEarly Adopters: Embracing TransformationEarly Majority: Strategic Integration

Industry View: ANZ PerspectiveThe adoption of AI in Australia and New Zealand reflects a unique blend of global trends and localnuances. From thriving tech hubs to traditional industries, the ANZ region offers a diverse andvibrant landscape for AI innovation and implementation.E-Commerce (Early Majority): Local retailers like Kogan areadopting AI for personalized marketing, inventorymanagement, and customer service.Supply Chain Optimization (Late Majority): Traditionalretailers are exploring AI to streamline supply chains andenhance efficiency.Traditional Retail (Laggards): While e-commerce and majorretail chains might be leveraging AI for customer insights,inventory management, and other areas, smaller traditionalretailers are lagging in AI adoption.Mining (Late Majority): Mining giants like BHP and Rio Tintoare integrating AI for predictive maintenance, exploration,and safety measures.Agriculture (Late Majority): The agricultural sector isbeginning to explore AI for crop monitoring, yieldprediction, and sustainable farming practices.Australian Government (Early Majority): Initiatives like theAustralian Government's AI Ethics Framework demonstrate acommitment to responsible AI adoption.New Zealand Government (Early Majority): New Zealand's AIstrategy emphasizes collaboration, innovation, and ethicalconsiderations, fostering a supportive environment for AIgrowth.Whilst not technically an industry sector, it seems that SMEsacross various sectors are being slower to adopt AI. Perhapsdue to budget constraints, lack of expertise, or the perceivedcomplexity and risks of AI solutions.Retail: Embracing Digital TransformationManufacturing and Mining: Late MajorityGovernment: A Strategic ApproachSmall & Medium Enterprise (SME)ANZ Tech Innovators: Companies like Atlassian(Australia) and Xero (New Zealand) areembracing AI to enhance software development,collaboration tools, and financial services.Research Institutions: Universities and researchcenters in both countries are fostering AIinnovation, partnering with industry leaders andcontributing to global AI research.Telehealth (Early Adopters): The rise of telehealthin ANZ, driven by companies like Coviu, leveragesAI for remote diagnostics and patientengagement.Mental Health (Early Majority): AI-driven mentalhealth platforms like Woebot are gaining traction,offering personalized support and therapy.Rural Healthcare (Laggards): While there areadvancements in AI for diagnostics, patient care,and administrative tasks in the major populationcentres. The broader healthcare system,especially in rural areas, is facing challenges inadopting AI due to regulatory concerns, dataprivacy issues, and infrastructure limitations.Banks (Early Majority): Major banks like ANZ andWestpac are utilizing AI for customer insights,fraud prevention, and investment strategies.RegTech (Early Adopters): ANZ's growingregulatory technology sector is leveraging AI toenhance compliance and risk management.Technology: Leading the WayHealthcare: A Focus on Personalized CareFinance: A Hub for Fintech

ANZ: A Region in TransformationThe Technology Adoption Life Cycle (TALC) in ANZ reflects a region that is both embracing global AItrends and forging its path. From early adopters in fintech and healthcare to late majority players intraditional industries like mining and agriculture, the ANZ AI landscape is diverse and dynamic."Building advanced AIis like launching arocket. The firstchallenge is tomaximize acceleration,but once it startspicking up speed, youalso need to focus onsteering..."Jaan TallinnFounder of SkypeConclusion: A Dynamic and Diverse ViewThe industry view of AI is both global and local, reflecting the complexities, opportunities, andchallenges of a technology that transcends boundaries. The comparison between the global landscapeand the ANZ context reveals fascinating contrasts and synergies, highlighting the importance ofunderstanding both the universal trends and regional specificities.As we continue to explore AI's impact, we recognize that its influence is not confined to a singlesector or region but resonates across the entire business ecosystem, shaping our future in ways thatare both shared and distinct.

Business OpportunitiesThere is no doubt that the landscape of AI presents a myriad ofopportunities. Some that we can anticipate. Perhaps many that we cannot.Simply looking around us for readily accessible insights from publications,research and case studies reveals a wealth of information.But this may be just the tip of the (growing) iceberg. Who knew just howsignificantly the humble spreadsheet would change the business world? AI islikely to be far more impactful, reshaping industries and organisations inways we may not yet fully comprehend.Here’s a “Top 10” list of opportunities to get us going:Business Opportunities & Risks:A Comprehensive Exploration1.Personalized Customer Experiences: Leveraging AI to tailor products,services, and interactions to individual customer preferences andbehaviours.2. Supply Chain Optimization: Utilizing AI algorithms to enhance supplychain efficiency, predict demand, manage inventory, and reduce costs.3. Business Risk Management: Employing AI to identify, assess, andprioritize business risks, enabling proactive risk mitigation and strategicalignment.4. Healthcare Innovation: Applying AI in diagnostics, treatment planning,and personalized medicine to improve patient outcomes and healthcaredelivery.5. Talent Management and HR: Using AI to streamline recruitment,enhance employee engagement, and personalize training and developmentprograms.6. Sustainable Practices: Employing AI to optimize energy usage, reducewaste, and support environmentally responsible business practices.7. Strategic Decision Making: Integrating AI into executive decision-makingprocesses to provide data-driven insights, forecasts, and scenario planning.8. Enhanced Collaboration and Productivity: Facilitating collaborationacross teams and geographies through AI-powered tools and platforms,boosting productivity.9. Market Expansion and Personalization: Utilizing AI to identify newmarket opportunities, segment customers, and tailor marketing strategies.10. Asset Management: Using AI to optimize asset utilization, maintenancescheduling, and lifecycle management, enhancing ROI and operationalefficiency.

The opportunities presented by AI are rich andmultifaceted. They encompass the obvious and theanticipated, but also the unexpected and theemergent. From global trends to local applications,from historical reflections to forward-thinkingpredictions, the landscape of opportunities is asdiverse as it is promising.Our collaborative approach, engaging with SAUGmembers and continuously evolving ourunderstanding, ensures that this exploration is nota solitary endeavour. It's a collective journey,guided by shared insights, curiosity, and acommitment to harnessing AI's transformativepotential for the greater good."Talent wins games,but teamwork andintelligence winchampionships." Michael Jordan

Business Risks & ConcernsWhile the opportunities are vast,the adoption of AI in the businesslandscape is not without its risksand concerns. There are criticalconsiderations that must beaddressed to ensure responsibleand effective integration of AItechnologies. Data Privacy and Security: Potentialbreaches, unauthorized access, and misuseof personal and sensitive data.Ethical Considerations: Bias in algorithms,discriminatory practices, and lack oftransparency in decision-making.Talent and Skill Gaps: Lack of expertise inAI technologies, challenges in training andupskilling employees.Dependence on Technology: Over-relianceon AI, potential loss of human judgment andintuition.Regulatory Compliance: Navigatingcomplex and evolving regulatory landscapes,potential legal liabilities.Business Risk Management Challenges:Inadequate risk assessment, failure to alignAI strategies with business goals and risks.1.2.3.4.5.6.The journey towards AI integration in businessis filled with both promise and peril. The risksand concerns are real and multifaceted, rangingfrom ethical dilemmas to talent challenges, fromregulatory complexities to potential over-dependence on technology.A balanced approach that recognizes theserisks, actively engages with them, and seeks tomitigate them through thoughtful strategies isessential. It's not merely about harnessing thepower of AI to gain business advantage, butdoing so in a manner that aligns with ourvalues, our laws, and our shared responsibilityto foster a future that is not only technologicallyadvanced but also ethically sound, sociallyresponsible, and human centric.

"Ethics must be theframework withinwhich we build anddeploy AI, not anafterthought." Fei-Fei Li, co-director ofStanfordUniversity'sHuman-CenteredAI Institute

The landscape of AI technology is diverse and multifaceted,encompassing various domains, tools, and applications. It is a domain full of often unfamiliar terms, alien concepts, and theusual array of bewildering acronyms.This section seeks to provide an overview of key aspects, tailored forthe business layman. We hope to shed light on some of thedistinctions, functions, purpose, and implications of differenttechnologies.About AI Technology forBusinessSome Common Terms: The difference between AI, ML, DL, LLMand GenAIAI (Artificial Intelligence) :A broad field of computer science focused on creating systemscapable of performing tasks that typically require humanintelligence.Encompasses various subfields, including machine learning (ML),robotics, computer vision, natural language processing (NLP),and more.AI systems can analyze large datasets, recognize patterns, makedecisions, and even learn from experience.Examples include virtual assistants like Siri or Alexa,recommendation systems on platforms like Netflix or Amazon,and autonomous vehicles.The goal of AI is not just to replicate human intelligence but toaugment it, often processing information and making decisionsat speeds and scales unattainable for humans.

ML Model (Machine Learning Model):A result of training a machine learning algorithm on data.Can make predictions or decisions based on the learned patterns from the data.Examples include decision trees, neural networks, support vector machines, and many others.

Deep learning is a technology inspired by how our brain works, specifically theinterconnected web of neurons.Think of it as a multi-layered net that can catch and process information. Each layer refinesand interprets the data a bit more than the previous one.Instead of being told specifically what to look for, deep learning systems learn from data andfigure out patterns on their own. It's like teaching a child to recognize animals not bydescribing them, but by showing pictures.Image and Voice Recognition. Deep learning is the reason why Facebook can recognize andtag your friends in photos or Siri can understand your voice commands.The more data you feed these systems, the better they get. It's like practicing a skill over andover.Deep learning excels in situations where data is vast and tasks are complex, like translatinglanguages in real-time or helping self-driving cars navigate.DL (Deep Learning)

Deep learning has been making a significant impact across various business sectors by enhancingoperations, customer experiences, and creating new opportunities. Here are some examples:DL (Deep Learning) Some Examples:Example: PayPal uses machine learning to detect potentially fraudulent activity.Impact: Protects customer assets and maintains trust in the platform.Deep Learning Role: Analyzing transaction patterns and identifying unusual activities thatcould indicate fraud.Finance: Fraud DetectionExample: Google's DeepMind has developed algorithms that can spot eye diseases in scans.Impact: Aids in early detection and treatment planning.Deep Learning Role: Analyzing medical images to identify patterns and anomalies that areindicative of diseases.Healthcare: Disease Identification and DiagnosisExample: Siemens uses AI to predict and prevent machinery breakdowns.Impact: Reduces downtime and maintenance costs.Deep Learning Role: Analyzing machinery data to predict when a machine is likely to fail orneeds maintenance.Manufacturing: Predictive MaintenanceExample: Tesla uses deep learning for its Autopilot and Full Self-Driving features.Impact: Progressing towards reducing accidents and improving transportation efficiency.Deep Learning Role: Interpreting sensor data to make real-time driving decisions.Automotive: Autonomous VehiclesExample: John Deere uses AI to monitor crop health and optimize farming practices.Impact: Enhances crop yield and reduces resource usage.Deep Learning Role: Analyzing data from IoT devices in farms to monitor and predict crophealth and optimize farming practices.Agriculture: Crop Monitoring and Management

A type of deep learning model specifically designed to understand and generatehuman-like text.Trained on vast amounts of text data to capture nuances, idioms, facts, reasoningabilities, and even some level of common sense.OpenAI's GPT (Generative Pre-trained Transformer) series, which includes modelslike GPT-3 and GPT-4, are examples of LLMs.LLMs can answer questions, write essays, generate creative content, assist incoding, and much more.LLM (Large Language Model):Generative AI (GenAI) & LLM’sThat’s the power that a LLM like ChatGPT can bring to the table.It is useful to think of LLM’s as a specialized type of Gnerative AI (GenAI),specifically trained for understanding and producing human-like text.Just as an seasoned copywriter will draw upon years of expereince of reading,writing and creating documents, an LLM as been specifically trained on vastamounts of text to understand language and context deeply.Thus, when you ask it a question (present a prompt), it can generate coherentand contextually relevant responses. Very much as a knowledgeable humanwould.In the business world if you can imagine having an highly educated assistantwho has read almost every book, publication, article and report. If asked (prompted) skillfully it can provide insights, spark ideas, draftdocuments or answer questions based upon that vast remembered knowledge.

"Artificial Intelligence,deep learning, machinelearning — whateveryou’re doing if youdon’t understand it —learn it. Becauseotherwise you’re goingto be a dinosaur within3 years." Mark CubanFounder,Billionaire,Entrepeneur

Consumer Tools: Aimed at individual users, these tools prioritize user-friendliness and specific functionalities like personal assistants or homeautomation.Corporate Tools: Designed for businesses, these tools emphasizerobustness, security, scalability, and integration with existing systems,suitable for large-scale operations and sensitive data handling.Consumer Tools vs. Corporate Tools: Purpose-Driven DesignDepartmental Applicability: Broad Cross-Functional PotentialSales: AI in sales enhances lead generation, customer insights, and salesforecasting, driving revenue growth.Marketing: AI in marketing personalizes campaigns, optimizes content,and analyzes market trends, enhancing engagement.HR & Recruitment: AI in HR streamlines recruitment, fosters employeedevelopment, and predicts retention, building a thriving workforce.Sourcing & Procurement: AI in procurement optimizes supplierevaluation, contract management, and cost control, ensuring value-drivensourcing.Finance & Corporate Governance: AI in finance enhances riskassessment, financial forecasting, and compliance monitoring, ensuringfiscal responsibility.Private LLMs (Large Language Models): Specialized LinguisticIntelligenceDefinition and Scope: Private LLMs are customized language models,tailored to specific business needs, industry terminology, and dataprivacy requirements.Adoption and Impact: Increasingly prevalent in specialized fieldssuch as legal or medical, Private LLMs enhance accuracy, efficiency,and confidentiality.Implementation Considerations: Complexity, cost, and ongoingsupport necessitate collaboration between technical experts, domainspecialists, and strategic leadership.

Safeguard without Restricting: While it's essential to monitor andmanage AI usage, it's equally vital not to stifle the innovation andbenefits AI can bring.In the evolving landscape of AI, organizations face a choice:centralize AI deployment under strict IT control or democratize it,letting teams choose their tools. Both have merits:Centralised IT Deployment: Ensures consistency, security, and costsavings but can be slower to adapt.Democratized Usage: Offers agility and tailored solutions but canpose security risks and lead to inefficiencies.A blended, two-speed model is emerging, combining centralisedcontrol for core IT with democratized AI tools for specific needs. Thisapproach offers both stability and agility.For example, a retail company may employ different AI strategies forinventory mangement and customer service. The company would usea centralized AI system for inventory management, which iscontrolled and managed by the IT department. But have a moredemocratized approach to customer service chatbots where differentteams or departments (e.g., online sales, in-store customer service)deploy their own AI-powered chatbots tailored to their specificcustomer interactions and queries.However, as AI's role in the workplace inevitably grows, it's crucialfor organizations to:Educate Staff: Ensure employees have the knowledge to use AIresponsibly and effectively.Implement Policies: Set clear guidelines on AI tool usage tomaintain security and compliance.Centralised IT Deployment vs.Democratized Usage

Emerging Technologies and TrendsEdge AI: Edge AI refers to the processing of AI algorithms closer tothe location where data is generated (like IoT devices) rather than in acentralised cloud-based system. This enhances real-time decision-making capabilities, reduces latency, and cuts down on data transfercosts.The AI landscape is rapidly evolving, with recent technologiespromising to reshape how businesses operate and deliver value. Quantum Computing: Quantum computers use the principles ofquantum mechanics to process vast amounts of data simultaneously.They have the potential to revolutionize AI computations.For example, in drug or vaccine discovery, traditional computers mighttake years to analyze molecular combinations for a new medicine.Quantum computers could potentially do this in days or even hours.Traffic cameras, for example, equipped with Edge AI can process andanalyze traffic conditions in real-time, adjusting traffic light patterns tooptimize flow, without needing to send data to a central server.Explainable AI (XAI): As AI systems become more complex, there's agrowing need to make their decisions transparent, understandable, andjustifiable. Explainable AI aims to build trust among users, ensure AIsystems are accountable, and facilitates regulatory compliance, especially insectors like finance and healthcare.For example, in finance, should an AI system deny a loan application, XAIcan provide the applicant with a clear, understandable reason for thedecision, ensuring transparency and potentially reducing disputes.For business leaders, understanding the basics of AI technologies andterminology, from ML models to LLMs, is crucial. But more than that, it'sabout recognizing the broader implications of these technologies onorganizational strategy, culture, and operations. Whether it's the balance between centralised and democratized AIdeployment, or the potential of emerging trends like Quantum Computingand Edge AI, leaders must be equipped to navigate this landscape withclarity and confidence.

"By far, the greatestdanger of ArtificialIntelligence is thatpeople conclude tooearly that theyunderstand it." Eliezer YudkowskyAI researcher and writeron decision theory andethics

In the intricate world of AI, the ecosystem of partners is not a mere backdrop; it's a vital arena ofcollaboration, innovation, and strategic alignment. This section delves into the multifaceted aspectsof engaging and navigating this ecosystem, offering insights, considerations, and practical guidance.Partners / Ecosystem: A Strategic andCollaborative LandscapeIdentifying the Right Partners: Key ConsiderationsUnderstand Your Needs: Clearly define your business goals, AI requirements, and expectations beforeengaging with potential partners.Evaluate Expertise: Assess the technical expertise, industry experience, and proven track record ofpotential partners.Consider Cultural Fit: Ensure that the partner's values, communication style, and approach align withyour organizational culture.Collaborating with Universities and GovernmentMajor Players: Engaging with industry giants offers robust solutions and comprehensive support butmay lack customization.Niche Specialists: Collaborating with specialized firms or startups provides agility, innovation, andtailored solutions but may require careful evaluation of stability and scalability.Major Players vs. Niche Specialists: A Balanced ViewUniversities: Explore research collaboration, talent recruitment, and innovation partnerships withacademic institutions.Government: Engage with regulatory bodies for compliance alignment and explore fundingopportunities, especially in collaboration with universities.

The AI ecosystem is a complex and dynamic landscape, where the choice of partners,consultants, and collaborators shapes not only technological outcomes but also strategicsuccess. For senior business leaders, this journey is marked by thoughtfulconsiderations, critical evaluations, and practical insights.From understanding specific needs to evaluating potential partners, from balancing thestrengths of major players with the agility of niche specialists, from applying practicaltips to fostering long-term collaborations, the path is multifaceted and most definitelyconsequential.Some Tips and Best PracticesEmbrace Outsourcing: Outsourcing AI partners can be more cost-effective and flexiblethan an in-house team, offering global access to expertise.Collaborate on a Roadmap: Working together with an AI partner on a strategic roadmapensures alignment and clarity in project planning.Ensure Accessibility: Sharing data with an external team is essential for success, butclear agreements on data ownership must be established.Prioritize Communication: Clear and frequent communication, even to the point of over-communication, is vital to ensure alignment and avoid misunderstandings.Seek a Motivating Partner: Look for a partner that challenges and motivates you toevolve, rather than one that merely follows instructions.Data Security is Crucial: While data sharing is necessary, robust data security practicesmust be a part of the culture for both parties.Utilize Existing Solutions: An experienced AI partner will often combine custom solutionswith existing ones to save time and costs.Keep Technology Up-to-Date: Invest in the latest tools and technologies, and consult withyour partner on the best choices for data mining, extraction, cleansing, and science.Ask Questions: Don't hesitate to ask your AI partner for guidance on areas where youmay lack expertise.Focus on Problem-Solving: Ensure that the partner understands the specific problemyour product aims to solve, as this is the ultimate goal of the cooperation.Selecting an AI partner may feel a lot like selecting any other technology/consultingpartner. But in many ways this landscape remains largely uncharted. It is only right toapproach with some degree of appropriate caution and perhaps a slightly differentmental model.Here are some tips and best-practices for selecting an AI partner that you might finduseful:A Thoughtful and Informed Approach

"The ultimate paradoxis that this technology(AI) may become apowerful catalyst thatwe need to reclaim ourhumanity.” John Hagelco-chairman for DeloitteLLP's Center for the Edge

In the intricate world of AI, the ecosystem of partners is not a merebackdrop; it's a vital arena of collaboration, innovation, and strategicalignment. In the age of Artificial Intelligence (AI), transparency andunderstanding are paramount. As AI systems increasingly influencedecisions that impact our lives and businesses, the need tounderstand how these decisions are made becomes essential. This iswhere Explainable AI (XAI) comes into play.Explainability in ArtificialIntelligence: A Guide forBusiness LeadersXAI refers to methods that make the workings of AI technologyunderstandable to human experts. Unlike the "black box" approach,where decisions are made without clear reasoning, XAI aims to shedlight on how inputs lead to outputs.Trust and Adoption: Understanding how AI makes decisions buildstrust. If your team understands why an AI application made itsrecommendation, they're more likely to follow it.Compliance and Ethics: Explainability helps ensure that AI systemscomply with laws and regulations and align with company values.Challenges in Mimicking Human Decision-Making: AI modelsstriving for logical explanation may struggle to replicate nuancedhuman judgments.Emphasizing Interpretability: Since human decisions aren'talways logical, XAI might focus on making AI decisionsunderstandable rather than fully explainable.Designing for Trust: XAI can engage users on an emotional level,building trust through relatable narratives.Human decision-making often relies on emotions and intuitiveheuristics rather than pure logic. This complex reality impacts XAI inseveral ways:What is Explainable AI (XAI)?Why Explainability MattersThe Human Factor: Emotions and Heuristics

Engage with Stakeholders: Collaborate with domain experts, legalprofessionals, and other stakeholders to align AI operations with theirperspectives.Build User-Friendly Interfaces: Translate AI insights into graphicalrepresentations and actionable recommendations, making themaccessible and understandable.Invest in Training and Learning: Foster a culture of learning andinvest in training to ensure that employees can work effectively withAI systems.Balance Performance and Explainability: Avoid a one-sided pursuitof performance at the expense of explainability. Consider theimplications of AI systems and involve different stakeholders.Educate and Align Expectations: Educate stakeholders about thedifferences between human and machine decision-making to fosteracceptance.Explainable AI is not just a technological challenge; it's also a veryhuman problem that recognizes the complexity of both human andmachine decision-making. In a world where AI is becoming a vital part of business strategy,understanding the "why and how" behind AI decisions andrecommendations is as crucial as the decisions themselves. Embracing explainability is a big step towards a more transparent,trusted, and effective use of AI in your organization.XAI: Not Just a Technology ChallengeAchieving XAI in Your Organization

"The ultimate paradoxis that this technology(AI) may become apowerful catalyst thatwe need to reclaim ourhumanity.”John Hagelco-chairman for DeloitteLLP's Center for the Edge

The journey to AI adoption is clearly not just about technology; it's abouttransforming the entire organization to be AI-ready. It's about building the right capabilities, fostering a culture of innovation andtrust, and ensuring that the AI solutions align with the organization's goalsand values.The foundational step is perhaps to recognize that AI has sparked arevolution. AI is more than just a technological trend; it's a true paradigmshift that promises to redefine operations, enhance efficiency, and offerunparalleled insights. In this context, the successful adoption, and integration of AI into businesseshas become a strategic imperative.As we stand on the cusp of this revolution, it is important to approach AI inyour organisation with structure and clarity in a manner that recognises bothopportunities and challenges:Building a Successful AIRollout Programce: A Guidefor Business LeadersAI Framework: An Example

Data: Ensure that you have representative and unbiased training datasets.The quality and diversity of data can significantly impact the performance andfairness of AI models.Platforms: Invest in robust architectures and scalable technologies that cansupport the demands of AI computations and data processing.Talent: Attract and retain data science talent. The success of your AIinitiatives will largely depend on the expertise of the people behind it. Giventhe competition for AI talent, consider strategies for nurturing in-house talentand collaborating with educational institutions.Building AI Capability:As AI systems take on more tasks, there will be a need to redesign workflowsand roles. Ensure that employees remain in control, especially for criticaldecisions, and that AI systems are used to augment human capabilities.Knowledge Sharing: Promote a culture of knowledge sharing. AI is a rapidlyevolving field, and continuous learning is crucial.Task Division: As AI systems take on more tasks, there will be a need toredesign workflows and roles. The resulting process will reflect the relativestrengths of the AI technology and the human insight.Human in Control: Ensure that employees remain in control, especially forcritical decisions, and that AI systems are used to augment humancapabilities.Redesigning Work for AI:Establish robust oversight mechanisms to ensure that AI systems aretransparent, reliable, and seen to be fair. This includes setting up guidelinesthat align with ethical standards and societal values.Explainability: Ensure transparency in AI operations and actively engage withemployees to address concerns and gather feedback. The ability to explainresults increases stakeholder trust.Fairness and Reliability: Mitigate bias from the outset of the design andensure ongoing oversight to minimise model drift. Domain insights are criticalto regularly assess model performance and outcomes.Legislation and Guidelines: Establish robust mechanisms to ensure that AIsystems are transparent, reliable, and seen to be fair. This includes setting upguidelines that align with ethical standards and societal values.AI Oversight and Assurance:

AI adoption will bring about cultural shifts in the organization. It's essential toprepare your workforce for this change, emphasizing the benefits of AI andaddressing any fears or misconceptions.End User Acceptance: AI adoption will bring about cultural shifts in theorganization. It's essential to prepare your workforce for this change,emphasizing the benefits of AI and addressing any fears or misconceptions.Continuous Learning and Adaptation: Engagement and education are keyfor all organisations’ as gaining trust is crucial. The field of AI is rapidlyevolving. Stay updated with the latest advancements and be ready to adaptyour strategies and solutions as needed.Manage Cultural Change:In many ways, embarking on the AI journey is like captaining a shipthrough uncharted waters. It may feel like just another IT project, butthe rewards and the challenges may see you travelling through choppywaters before the treasure is found.By following the guidelines outlines above, business leaders can chart theway for successful AI adoption and harness its full potential.AI should, of course be used to create tangible value for stakeholders. But it isalso critical that it is seen to have positive workplace and social impacts.Ensure that the AI projects align with stakeholders' interests, includingemployees and customers and that there's a clear mechanism to measure andtrack the value delivered.Social Outcomes: AI should, of course be used to create tangible value forstakeholders. But it is also critical that it is seen to have positive workplaceand social impacts. Ensure that the AI projects align with stakeholders'interests, including employees and customers. Economic Outcomes: Ensure that there's a clear mechanism to measure andtrack the value delivered. This will be a combination or quantitative but alsoqualitative metrics.Create Stakeholder Value:

”A lot of the game of AItoday is finding theappropriate businesscontext to fit it into.” Andrew NgDirector StanfordUniveristy AI Laboratory

Organisations are expected to adopt new transformative technologies. They areexpected to increase effectiveness, reduce costs, and provide ever better products andservices.AI is a powerful tool with the potential to help achieve all those things. But it isn’t amagic bullet or a quick fix. While initial forays can yield immediate benefits, realizingthe full potential will demand sustained dedication and strategic investment.Here we will explore some of the essential elements for harnessing AI’s capabilities andensuring that it becomes a lasting, valuable asset.Building Capability: HarnessingAI for Business SuccessAssembling Expertise to Build AI Capability1.Skills: Technical Proficiency: Ensure your team possesses the technical know-how, from datapreprocessing to advanced machine learning algorithms.Understanding Data: It's about knowing what information you have and how to use it.Data Literacy: It's not just about having data; it's about weaving insights from it.Business Acumen: Beyond technical skills, understanding the business context inwhich AI operates is crucial. Ultimately it is your people who will set the business goalsand generate tangible value.Recruitment:Understand the AI Skill Spectrum: AI isn't a monolithic field. It encompasses a rangeof specialties from data engineering to ethics and business acumen. Recognize thespecific skills your organization needs and tailor your recruitment strategy accordingly.Talent Scouting: Identify and attract top talent in the AI field. Consider partneringwith universities, attending AI conferences, and leveraging professional networks.Look Beyond Traditional Credentials: While degrees from top institutions can indicateexpertise, the rapidly evolving nature of AI means that great talent might come fromunconventional backgrounds. Online courses, bootcamps, and self-taught enthusiastscan bring fresh, innovative perspectives.Prioritize Soft Skills: Technical prowess is important, but so are communication,collaboration, and adaptability. Your team will need to work closely with otherdepartments, explain complex concepts in simple terms, and adapt to the ever-changing tech landscape.Diverse Teams: Aim for diversity in your AI teams. Diverse teams bring variedperspectives, leading to more innovative and robust AI solutions.

Continuous Learning: The AI field is not standing still. Invest in regular trainingprograms to keep your team updated with the latest advancements.Cross-functional Training: Promote interdisciplinary knowledge sharing. Forinstance, train your technical team in business aspects and vice versa.Empowering Your Whole Organisation: Just as everyone knows how to use aspreadsheet, AI is becoming an essential professional skill. Equip your team withthe essentials of AI. Ensure they appreciate its value and wield it withresponsibility.Assembling the right team is about more than just securing top talent. It's aboutbuilding a forward-thinking, adaptable, and collaborative team ready to harnessthe transformative power of AI for your organization's success.Training: Studies have pointed to five distinct capability areas: model development, domainunderstanding, model explanation, model integration, and model assurance.These key capability areas reflect the nature of AI systems, in that at their coresits a “model”. Be it a machine learning (ML) model or a Large Language Model(LLM). These models must be built, trained and maintained.The five capability areas discussed here are inter-related and mastering all is thekey for successful and meaningful use of AI .2. Key Areas of Focus for AI Capability

Craft models that are both accurate and dependable for various tasks.Use relevant training data to teach these models.Continuously evaluate their performance, keeping not just accuracy but alsoethical standards in mind.At the heart of AI lies the model. It's essential for organizations to:1. Model Development:Harnessing Existing Knowledge: Use the insights and experiences of experts toguide and teach algorithms based on their expertise.Challenging Old Ways: Don't just rely on traditional decision-making. Embracenew insights provided by AI and be open to changing existing assumptions.Understanding your business area deeply is crucial. This involves:2. Domain Understanding:3. Model Explanation:Offer clear explanations of how they work.Use visual tools to show how decisions are made, making it easier for expertsin the field to understand and trust the AI.It's not enough to just have a working model; people need to trust it. While someadvanced models might be complex, it's vital to:4. Model Integration:Collaborating with developers to embed AI models into intuitive interfaces.Ensuring the end product is easy for users to interact with and understand.Once you have a model, it needs to be broadly accessible and usable. This means:5. Model Assurance:Regularly update models with new data.Foster collaboration between the teams creating the models and those usingthem.Always be prepared for unexpected outcomes and have a plan to address anyissues that arise.AI isn't a set-it-and-forget-it tool. To ensure its continued relevance:By focusing on these areas, organizations can harness the full potential of AI,ensuring it's not just powerful but also trusted and integrated seamlessly intodaily operations.

”Technology, like art,is a soaring exercise ofthe humanimagination.” Daniel BellProfessor of Sociology -Harvard University

The journey to harnessing AI's potential is riddled with questions, especiallyaround its value proposition. Business leaders often grapple with justifying AIinvestments to stakeholders, given the perceived risks and unclear rewards. Here we aim to provide clarity on the value perspective of AI, offering insightsinto its potential benefits over time from both a qualitative and quantitative angle.A Value Perspective onAI for BusinessQuantifying the Value of AI:Qualitative vs. Quantitative Value Drivers: While some AI benefits can bequantified, such as cost savings or increased sales, others, like improvedcustomer satisfaction or enhanced brand reputation, are qualitative. Both arecrucial for a holistic understanding of AI's value.The most significant benefits from new forms ofai will come when firms entirely reorganisethemselves around the new technology; byadapting ai models for in-house data, for example.That will take time, money and, crucially, acompetitive drive. Gathering data is tiresome andrunning the best models expensive—a singlecomplex query on the latest version of Chatgptcan cost $1-2. Run 20 in an hour and you havepassed the median hourly American wage.“The Economist

Introducing the “Lifecycle of Value”. The value derived from AI evolves overtime. Initially, businesses might experience learning curves and teething issues.However, as the system matures, the value proposition becomes clearer. Evenfailed projects offer valuable insights, setting the stage for future successes.Let’s explore three horizon categories that can help to form a mental model ofwhere in the project, and what type of benefits might be anticipated.Realizing Value Over Time: Insight, Expected, andOutcomeImplicit Knowledge Codification. AI can capture and codify tacit knowledge,turning subjective insights from domain experts into actionable data points.Data-Driven Decision Making: With AI-driven insights, businesses can makemore informed decisions, reducing reliance on gut feelings or intuition.Predictive Analysis: AI can forecast future trends based on historical data,allowing businesses to be proactive rather than reactive.Insight value refers to the deeper understanding and knowledge gained from AIprocesses and analyses. It's the value derived from uncovering hidden patterns,trends, and correlations in data that were previously inaccessible or unnoticed.Insight value may include such things as:For example, a retail business using AI to analyze customer purchase patternsmight discover that certain products are frequently bought together, leading toeffective bundling strategies.In healthcare, AI-driven insights might reveal correlations between specificsymptoms and rare diseases, aiding early diagnosis.1. Insight Value:

Cost Savings: AI can automate repetitive tasks, leading to significant labour costsavings.Revenue Growth: AI-driven strategies, like personalized marketing, can boost salesand customer retention.Operational Efficiency: AI can streamline operations, reducing waste and enhancingproductivity.This is the anticipated value that businesses expect to derive from their AI projects, oftenquantified during the project's planning phase. It's the tangible ROI that stakeholdersexpect based on initial projections and business cases.Expect value is often identical to what might be anticipated and used to justify any ITproject. Such things as:2. Expected (Project) Value:Innovation: AI can lead to the development of new products, services, or businessmodels.Competitive Advantage: Early and effective AI adoption can position a business aheadof its competitors.Enhanced Stakeholder Experience: AI can improve experiences for customers,employees, and other stakeholders, leading to increased loyalty and brand value.A logistics company, after implementing AI for route optimization, might discover anopportunity to offer real-time delivery tracking as a new service.A media company using AI to recommend content might find that user engagementand session durations have unexpectedly increased, leading to higher advertisingrevenues.This is where magic can happen! This is the broader, often unanticipated value that AIbrings to the entire enterprise. It encompasses unexpected positive outcomes, newopportunities, and the strategic advantages that AI adoption brings.These can be entirely unexpected and clearly hard to predict. But you may realisticallyanticipate benefits in areas such as:Possible Examples:3. Outcome (Enterprise) Value:Whilst traditional quantitative measures will remain a part of the corporateprocess, they only tell part of the story. Incorporating non-traditional qualitativejustifications, and even more emotional levers provides a more comprehensive,nuanced, and forward-looking perspective on the value and implications of AIprojects and where significant value may be realized, often unexpectedly overtime.

”The value of AI isn'tabout thinkingmachines, but aboutaugmenting humanthought." Kevin Kelly, co-founder ofWired magazine.”

Justifying an AI project to an approval board is obviouslycritical. It can however be a bit of a minefield as traditionalapproval metrics may be inappropriate and may notcapture the full scope of benefits or potential risks.Also, AI projects often involve intricate algorithms andprocesses that may not yield immediate or easilyquantifiable results.Here are four thought areas that may help to achieve a“green light” for your project.Risk vs. Reward: The balance between risk and rewardremains a primary concern. While the potential rewards ofAI are vast, they come with inherent risks, especially in theinitial stages. A risk/reward diagram can serve as a guide,helping businesses visualize potential outcomes.The Fear of Being Left Behind: In the competitive businesslandscape, not embracing AI can be a strategic misstep.The pressure to innovate and stay ahead often pushesbusinesses to explore AI, even if the exact path remainsunclear.Investing in AI Innovation: The potential value of AI isundeniable. Businesses that strategically invest in AI standto gain a competitive edge, possibly reaping vast rewardsin the long run.The Unfair Advantage: Embracing AI might providebusinesses with an 'unfair advantage,' allowing them tooperate more efficiently, make informed decisions, andoffer unparalleled customer experiences.Justifying AI to Stakeholders:

”Price is what you pay.Value is what you get." Warren Buffett

Artificial Intelligence, at its core, isn't really about the technology. Throughouthistory, humans have been innate tool builders, and AI stands as a testament toour evolutionary journey in this realm.It's perhaps the most transformative business tool since the advent of thespreadsheet.From a strategic standpoint, AI has the potential to address monumentalchallenges and help to realize opportunities right in the boardroom, serving as alinchpin for corporate decision-making. On the other hand, every employee can harness the power of generative AI toenhance daily operations, making tasks more efficient and insightful.We're already witnessing AI being seamlessly integrated into many applicationsand solutions. From the smartphones in our pockets, the GPS systems guidingour journeys, to the voice-activated assistants in our homes like GoogleAssistant, Alexa, and Siri, AI is becoming an intrinsic part of our daily lives. As its ubiquity grows, the novelty will fade, and conversations around AI willbecome as commonplace as those about any other technology.However, it's undeniable that there's a palpable apprehension surrounding AI. Hollywood, with its dystopian narratives from HAL in "2001: A Space Odyssey"to the menacing Terminators and the eerily human-like Ava in "Ex Machina",has often painted AI in a cautionary light. Positive portrayals, like Star Trek's Lt.Commander Data, are unfortunately fewer. This, combined with the inherenthuman fear of the unknown, has contributed to the trepidation. Yet, just as initial fears about cloud security in business eventually dissipated,apprehensions about AI will also wane.So, what does AI signify for the business world and society at large? We see ablend of human experience and breathtaking technology. A unique alchemy thathas the potential to amplify human capability. When humans, armed with data and powered by AI, come together, the resultscan be truly magical.We, as business leaders have a responsibility to ensure that AI tools are usedadeptly, that the foundational data aligns with the intended purpose, and thatthe AI itself is trained appropriately. If we do this, we can unlock unprecedentedpotential.Much like how the spreadsheet, mobile phone, and the internet revolutionizedour work and personal lives, AI, in all its manifestations, is poised to usher in anew era of innovation and progress. The future beckons, and it's one where AI will undoubtedly play a starring role.Some Thoughts: The Alchemy ofAI in Business

”It's not AI that isgoing to take your job,but someone whoknows how to use AImight." Richard Baldwin Professor of internationaleconomics at the IMDBusiness School. Presidentof the Centre forEconomic Policy Research

In this document, we’ve delved into the fascinating and often confusingworld of Artificial Intelligence in the business context.We’ve journeyed across various industries exploring its transformativepotential and undoubted challenges.Our narrative underscores the importance of understanding AI from multipleperspectives including executive/board views, managerial insights, employeeaspirations, and broader societal implications.The content emphasises the SAUG community's collective approach to AI,emphasizing collaboration, shared experiences, and perhaps a unifiedroadmap.The global and regional (ANZ) perspectives on AI adoption are explored,highlighting the dynamic and diverse nature of AI's influence across sectors. We also offer a comprehensive exploration of business opportunities andrisks associated with AI.Conclusion & Preliminary GuidanceAdvice for Business LeadersThis document explores many aspects of AI in the business context. We trustthat you’ve have had several “ah-ha” moments as you moved through thetext.As we have been researching and writing this document, three broadcategories or action have emerged which may serve to guide us as we plotthe course that lies ahead of us.These are: Invest, Educate and Adapt.Invest: Embrace AI as a Tool, not a Threat: Understand that AI is another toolin the arsenal of business technologies. It's not about replacing humanroles but augmenting them to achieve better results.Focus on Value, Not Hype: While AI has a lot of potentials, there arelikely to be plenty of dead-ends and fly-by-night tools that quicklydisappear. It's essential to focus on tangible value. Look for areas in yourbusiness where AI can provide real, measurable benefits.Infrastructure Investment: Ensure you have the necessaryinfrastructure, both in terms of technology and human resources, tosupport AI initiatives.Pilot Projects: Before a full-scale rollout, start with pilot projects to testthe waters, gather data, and understand the implications. Leave room for experimentation: There way well be new frontiers to bediscovered. Areas where unexpected benefits may be found. Be sure toleave room for exploration in new areas.Ethical Framework: Develop an ethical framework for AI usage in yourorganization. This should address data usage, transparency, andpotential biases.1.2.3.4.5.6.

Educate:Demystify AI: Invest time in understanding the basicsof AI. This will help in making informed decisions anddispelling any Hollywood-induced fears.Invest in Education: Ensure that both leadership andstaff are educated about AI's potential and limitations.This will foster a culture of innovation and responsibleAI use.Stay Updated on Emerging Trends: AI is a rapidlyevolving field. Stay updated on new technologies andtrends to leverage the latest advancements.Engage with the Broader Community: Participate inforums, conferences, and workshops on AI. This willprovide insights into global trends and best practices.1.2.3.4.Adapt:Collaborate: Engage with AI experts, industry peers, andeven competitors to share knowledge and best practices.Feedback Loop: Once AI systems are in place, establish afeedback loop. This will help in continuously refining thesystems based on real-world performance.Stay Agile: The world of AI is dynamic. Be prepared tochange your plans quickly based on new advancements orlearnings from your AI initiatives.Review and Refine: Regularly review the outcomes ofyour AI initiatives. Refine strategies based on thesereviews to ensure you're always moving towardsachieving the maximum value from AI.Plan for the Long-Term: While there might be quickwins with AI, true transformational value will be realizedwith a long-term commitment and strategy. Embrace unanticipated gains: There may be unexpectedgains from your AI projects. Don’t be so certain of yourplanned outcomes that you miss something even better.1.2.3.4.5.6.

”The rise of AI is not astory of machinestaking over. It's a storyof humans usingmachines to doamazing things." Sebastian Thrun, founderof Google X

AI's role in the business landscape is undeniable, offering aplethora of opportunities while also presenting challengesthat need careful navigation. As industries globally and within the ANZ region grapplewith AI's implications, it's evident that collaboration,understanding, and a collective approach are paramount. The SAUG community exemplifies this spirit, emphasizingthe importance of shared experiences and a unifiedroadmap. Over the coming months as we venture deeper into the AI-driven future, businesses must remain agile, informed, andopen to the transformative potential that AI promises.By gathering a diverse range of voices, insights, andexpertise, we at SAUG are committed to offering a well-rounded and thoroughly researched viewpoint. Our aim isto empower our members with the knowledge andguidance they need to confidently navigate their AIjourneys. We believe that by working collaboratively, sharingexperiences, and leveraging collective wisdom, we canensure that our members not only harness the power of AIbut also shape its future.Together, we will chart a course towards a future where AIamplifies our potential, drives innovation, and deliversvalue for all.Wrapping it all up (For Now)

SAUGAN AI POINT-OF-VIEW FORMEMBERSARTIFICIAL INTELLIGENCE

© [2023] SAUG. All rights reserved.No part of this document may be reproduced, distributed,or transmitted in any form or by any means, includingphotocopying, recording, or other electronic or mechanicalmethods, without the prior written permission of thepublisher, except in the case of brief quotations embodiedin critical reviews and certain other noncommercial usespermitted by copyright law. For permission requests, writeto the publisher, addressed “Attention: PermissionsCoordinator,” at the address below.PO Box 6277North Sydney NSW 2060Email: admin@saug.com.au