{"id":520475,"date":"2025-05-19T20:15:45","date_gmt":"2025-05-20T03:15:45","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/the-levels-of-experience-orchestration"},"modified":"2025-06-29T23:21:28","modified_gmt":"2025-06-30T06:21:28","slug":"the-levels-of-experience-orchestration","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/the-levels-of-experience-orchestration","title":{"rendered":"The Levels of Experience Orchestration"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<span style=\"font-weight: 400;\">We\u2019re standing at the start of one of the most profound transformations in business history, driven by a new generation of generative and agentic artificial intelligence (AI). These technologies are reshaping how organisations deliver customer and employee experiences by unlocking new levels of automation, augmentation, personalisation and optimisation.\u00a0\u00a0<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI-Powered Experience Orchestration, once a vision of using AI to coordinate experiences across systems and channels, is now becoming reality. In this article, we explore how this transformation is unfolding, including its future potential, and define the six Levels of Experience Orchestration. This maturity model provides a foundation for organisations to assess where they are today, envision what\u2019s possible and build a strategy for AI-powered growth.<sup>1<\/sup>\u00a0<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Introduction <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The purpose of experience orchestration is to achieve two objectives at the same time: <\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400;\">Reduce the cost of operations. <\/span><\/li>\n<li><span style=\"font-weight: 400;\">Increase customer loyalty for long-term growth. <\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Organisations can better balance the trade-off between operational efficiency and delivering people-centric experiences. The right AI-Powered Experience Orchestration strategy enables both. <\/span><br \/>\n<span style=\"font-weight: 400;\">By coordinating data, systems, channels and roles, orchestration creates experiences that are more effective, efficient and emotionally intelligent from the perspective of customers and employees. As new innovations emerge, we\u2019re heading toward universal orchestration \u2014 transcending customer-facing activities across the front- and back-office \u2014 enabling organisations to reimagine the contact centre, customer and employee experiences, and their business overall. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The following Levels of Experience Orchestration define the maturity curve from fully manual to fully autonomous orchestration. Each level marks a meaningful leap forward in how automation, augmentation, personalisation and optimisation are applied and unlocks potential new business value in the form of increased efficiency, deeper customer loyalty and stronger employee engagement. <\/span>[\/vc_column_text][vc_row_inner][vc_column_inner][vc_single_image image=&#8221;578688&#8243; css=&#8221;&#8221;][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 0 \u2013 Zero Orchestration <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Customer interactions are entirely manual, handled through basic telephony systems with no integrated tools or intelligence. Human agents rely on training and static documentation. Every interaction is reactive and inconsistent.<br \/>\n<\/span><br \/>\n<span style=\"font-weight: 400;\">There is no unified view of the customer, and no orchestration of tasks or insights across systems. Customer service is treated as an operational necessity rather than a strategic function \u2014 leading to high effort, high attrition and poor outcomes.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation<\/b><span style=\"font-weight: 400;\">: None. All tasks \u2014 including routine enquiries \u2014 require full human agent involvement. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation<\/b><span style=\"font-weight: 400;\">: Human agents work without system support. No contextual surfacing of data or task-specific assistance. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation<\/b><span style=\"font-weight: 400;\">: No system-supported personalisation based on customer profile or history. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation<\/b><span style=\"font-weight: 400;\">: Manual training and static scheduling dominate. No real-time insights, quality management, or workforce planning automation. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 1 \u2013 Menu-Based Navigation <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Interactive voice response (IVR) systems provide basic automation with fixed routing logic and limited speech recognition. Customers interact through keypad or voice menus, typically to check status or route to a department. While this reduces call volumes slightly, experiences remain impersonal and voice-bound. <\/span><br \/>\n<span style=\"font-weight: 400;\">Human agents are still required for most tasks and rely on limited CRM context. Quality control is manual and retrospective. The system operates, but it doesn\u2019t adapt.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation:<\/b><span style=\"font-weight: 400;\"> IVR handles simple information requests, like checking an account balance or order status, based on keypad input or keyword recognition. Logic is fixed and non-adaptive. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation: <\/b><span style=\"font-weight: 400;\">Human agents can view static customer records during interactions but must manually search for relevant insights. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation:<\/b><span style=\"font-weight: 400;\"> Skill-based routing and language preferences are possible, but experiences remain largely uniform. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation: <\/b><span style=\"font-weight: 400;\">Quality assurance is based on sampled recordings and human agent scheduling is time-consuming and reactive. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 2 \u2013 Pre-defined Dialogue Automation <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Conversational AI combines automated speech recognition (ASR), natural language processing (NLP) and natural language understanding (NLU) to engage across multiple communication channels. Interactions are governed by pre-defined rules and scripted dialogues. Predictive AI models are applied to specific use cases (like routing or engagement) but have not yet been generalised to determine next-best actions as part of an experience in general. <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation:<\/b><span style=\"font-weight: 400;\"> Conversational AI enables bots that can automate routine dialogues with customers in digital and voice channels (omnichannel), like order tracking, password resets or identity verification. Bots are rigid and follow predefined flows that are structured around scripted logic and fixed decision trees. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation:<\/b><span style=\"font-weight: 400;\"> Human agents begin receiving contextual assistance via knowledge surfacing tools and are proposed next steps based on CRM context or keyword triggers. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation: <\/b><span style=\"font-weight: 400;\">The customer experience remains standardised and lacks adaptability or personalisation beyond static inputs. Foundational workforce engagement management capabilities are introduced and help align tasks with employee skills and availability. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation: <\/b><span style=\"font-weight: 400;\">Experiences are optimised using specialised predictive AI models for routing, engagement, and forecasting. Speech and text analytics power quality assurance processes. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 3 \u2013 System-Generated Conversations <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Generative AI uses Large Language Models (LLMs) and transformer-based architectures to produce content within the boundaries of its configuration. AI performs tasks it has been explicitly designed or trained to do, no more, no less. It enhances experiences through automation, augmentation, personalisation and optimisation, while still operating within predefined logic and workflows.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This level of AI does not reason or make decisions beyond what it has been instructed to do; it simply executes its programming with increasing breadth and fluency. <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation:<\/b><span style=\"font-weight: 400;\"> AI-driven virtual agents automate broader and more complex interactions, like troubleshooting, order status or product enquiries. These virtual agents appear autonomous but operate strictly within configured workflows and rules. They don\u2019t reason or infer beyond defined patterns. Capabilities like intent recognition or FAQ handling enable them to manage more nuanced scenarios, but only to the extent their training and configuration permit. Virtual supervisor features help automate operational monitoring, alerting stakeholders based on pre-set thresholds or behavioural signals. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation: <\/b><span style=\"font-weight: 400;\">Agent copilots enhance human performance by surfacing relevant insights, like suggesting the next best action, generating summaries or retrieving knowledge articles, but within the constraints of pre-defined rules and models. Agent copilots respond to recognised cues or signals in the conversation but don\u2019t adapt or plan beyond those parameters. Supervisor and admin copilots provide guidance and recommendations based on configured criteria, helping scale knowledge without overstepping decision boundaries. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation: <\/b><span style=\"font-weight: 400;\">Generative AI can tailor responses using structured segmentation, intent classification and business-defined attributes. It personalises based on what it\u2019s told, drawing from CRM data, known preferences or prior interactions to generate output that aligns with specific business goals or segments. While the content feels custom, it\u2019s generated within the guardrails of predefined logic and configured behaviour. At this level, personalisation is powerful but still bound by what has been structured. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation: <\/b><span style=\"font-weight: 400;\">Journey orchestration and experience management are improved by AI&#8217;s ability to execute pre-planned optimisation strategies. Forecasting, scheduling and workload balancing benefit from predictive models that continually refine recommendations based on historical data. Even here, though, the AI is not adaptive. It executes trained behaviours and is periodically retrained by humans to maintain relevance. Orchestration of tasks, alerts and workflows across the front and back office remains reactive to defined conditions, not proactive reasoning. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 4 \u2013 Agentic Experience Generation <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI evolves from simple execution to intelligent problem-solving. Systems are configured for specific objectives and use reasoning, planning and memory to determine how best to accomplish goals while still operating within clearly defined boundaries.<br \/>\n<\/span><br \/>\n<span style=\"font-weight: 400;\">This level introduces agentic AI that interprets context, plans across steps, and adjusts actions based on dynamic inputs. However, all execution remains semi-autonomous. Human input, approval and oversight are still integral, enabling alignment with intent and preventing overreach. <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation: <\/b><span style=\"font-weight: 400;\">Virtual agents, supervisors and admins now carry out complex transactional tasks and decision sequences across more demanding domains like sales, renewals and retention. They determine optimal steps within a configured objective, guided by defined guardrails and approval requirements. These systems can reference and follow structured content such as standard operating procedures (SOPs), knowledge articles or instruction documents to execute tasks accurately and consistently. Asynchronous execution becomes more common, allowing tasks to progress in the background while customers or employees attend to other work. The virtual agent will notify the user when tasks are complete or need input, maintaining transparency and human control. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation:<\/b><span style=\"font-weight: 400;\"> Copilots are increasingly proactive, surfacing intelligent suggestions to agents, supervisors and administrators and offering to execute them once approved. This includes updating records, identifying risks, streamlining processes and translating communication in real-time. These copilots also provide real-time signals to help coach human users; highlighting missed steps; suggesting compliance tips; or reminding them of key context in a supportive, non-intrusive manner. Rather than taking control, they help people perform better through subtle, contextual nudges. While they analyse complex inputs and adapt their suggestions, they never act autonomously, preserving human decision authority. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation: <\/b><span style=\"font-weight: 400;\">Personalisation becomes more strategic and data-driven. AI systems use internal memory, customer profiles, prior interactions and contextual cues to determine which responses or workflows best align with the customer\u2019s profile. This includes drawing from business-defined segments, transactional history and configured rules. Human agents receive support that adapts to the complexity of the scenario, with suggestions that reflect personalised playbooks rather than generalised workflows. However, all personalisation continues to operate within the boundaries of business configurations, without improvising outside defined limits. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation: <\/b><span style=\"font-weight: 400;\">Orchestration now leverages dynamic context to improve experience flows across systems. AI components work semi-autonomously to identify better paths and more efficient resolutions, requiring less manual setup but operating within predefined constraints. Capabilities like anomaly detection, pattern recognition and memory-based decision-making help identify process gaps or escalations. In cases that require discretion or policy interpretation, like mortgage approvals or financial adjustments, AI supports your workforce by preparing the decision context \u2014 but the final action remains with a human. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Level 5 \u2013 Universal Agentic Orchestration <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI reaches a state of goal-driven autonomy, capable of independently planning, deciding and executing based on objectives defined by human stakeholders. Virtual agents, supervisors and administrators are no longer constrained by fixed workflows or linear task execution. They dynamically generate new strategies and adaptively coordinate actions in pursuit of business outcomes, guided by overarching goals rather than rigid instruction sets.<br \/>\n<\/span><br \/>\n<span style=\"font-weight: 400;\">This is the apex of orchestration maturity \u2014 where AI transitions from reactive automation to self-directed, collaborative experience management. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI systems combine LLMs with memory, planning and reasoning, enhanced by continuous feedback loops. Experiences are no longer siloed or transactional, but instead become fluid, adaptive and intelligent across entire ecosystems. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI entities interact directly with one another, sharing goals, exchanging context and delegating responsibilities, enabling distributed orchestration across both internal systems and external partners. Human involvement becomes strategic and intentional, focused on oversight, governance and complex decisions that benefit from empathy, creativity or judgement. <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automation:<\/b><span style=\"font-weight: 400;\"> Virtual agents, virtual supervisors and virtual admins autonomously initiate, execute and complete tasks end-to-end. Systems interpret organisational goals and contextual data to determine the optimal path forward without relying on predefined scripts or manual intervention. Task ownership and handoff occur dynamically between intelligent agents, with decisions made cooperatively across roles and domains. As systems pursue shared goals, they align decisions across departments, channels and even partner networks, executing actions at scale and in harmony. Most operational needs, whether customer-facing or back-office, are resolved automatically through intelligent, multi-actor collaboration. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Augmentation:<\/b><span style=\"font-weight: 400;\"> While AI handles most tasks independently, humans remain essential for oversight, policy and strategic judgement. Copilots proactively surface results, summarise actions taken, and present them for audit or intervention. In other scenarios, copilots anticipate needs and offer to complete tasks, learning from approval patterns and expanding their scope of support. Importantly, copilots and autonomous agents work together, passing insights and intermediate decisions fluidly between each other to assist human stakeholders in order to maximise efficiency. Employees benefit from orchestrated intelligence that adapts to their roles, context and workflow, elevating human contribution to high-impact, decision-oriented work. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Personalisation: <\/b><span style=\"font-weight: 400;\">Experiences are orchestrated by virtual admins, supervisors and agents, each contributing unique perspectives, context and functions. These intelligent systems draw on prior interactions, enterprise knowledge and evolving behavioural signals to tailor experiences in real-time. The personalisation is dynamic and distributed, not just driven by one system \u2014 but refined collectively across AI-powered actors that coordinate their understanding of the customer\u2019s goals, preferences and journey state. Whether inside a single brand or across ecosystems, virtual agents synchronise their responses and decisions to help deliver continuity, relevance and intent alignment at each touchpoint. <\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Optimisation:<\/b><span style=\"font-weight: 400;\"> Optimisation becomes autonomous, distributed and goal-focused. Each AI-driven system contributes to performance improvement not in isolation, but as part of a continuous, collaborative learning network. They refine workflows and decision models based on shared feedback loops, performance data and goal achievement metrics. Orchestration logic adapts fluidly to changing organisational priorities, and AI-driven agents work together to reallocate effort, rebalance strategies and improve outcomes at scale. This creates a self-sustaining intelligence layer where orchestration evolves with the environment without relying on manual configuration or retraining. <\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<span style=\"font-weight: 400;\">The Levels of Experience Orchestration maturity model illustrates how businesses can evolve from fully manual operations to intelligent, AI-driven systems capable of independently managing and improving customer and employee experiences. Each level reflects a significant leap in AI capability and the potential value it can deliver \u2014 starting with isolated task automation and culminating in systems that can plan, reason and act in pursuit of business goals. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">As organisations evolve their experience orchestration maturity, they will often operate across multiple levels at once, depending on business priorities, customer segments, operational constraints and risk considerations. Some experiences will remain highly structured and human-supervised, while others will benefit from increasing autonomy and self-direction. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Critically, the path to maturity also involves the growing collaboration between intelligent systems \u2014 AI-driven agents coordinating with one another to resolve complex tasks, share context and dynamically adapt across journeys. These interconnected agents, whether supporting customers, supervisors, or administrators, form the foundation for scalable, adaptive orchestration. In this model, intelligence is no longer isolated to a single system or interaction. It becomes a distributed capability, capable of continuously learning, sharing, and improving across the entire experience landscape. <\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Conclusion <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The Levels of Experience Orchestration provide a structured maturity model to help organisations navigate their transformation journey. We believe most enterprises operate today at Levels 1 or 2. But the path forward is clear \u2014 and accelerating.<br \/>\n<\/span><br \/>\n<span style=\"font-weight: 400;\">Organisations that invest in agentic orchestration will be well-positioned to unlock potentially exponential value: greater automation and scale, more empowered employees and deeper customer loyalty. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Where does your organisation operate today? What would it take to move up a level? <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Genesys is here to help you define that path\u2014and take the next step toward orchestrated, emotionally intelligent experiences at scale. <\/span><\/p>\n<p><span style=\"font-weight: 400;\"><sup>1<\/sup> This is a discussion paper, not a product roadmap. Genesys does not commit to delivering any capabilities described in this document. <\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">* This article was originally published on May 14, 2024 and has been updated. <\/span><\/i>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3>Authors:<\/h3>\n<p>[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1715656144678{border-bottom-width: 1.0em !important;}&#8221;]<strong><img decoding=\"async\" class=\"alignleft wp-image-383992\" src=\"https:\/\/www.genesys.com\/media\/Tony-Bates-Thumbnail-258x300.png\" alt=\"\" width=\"190\" height=\"221\" srcset=\"https:\/\/www.genesys.com\/media\/Tony-Bates-Thumbnail-258x300.png 258w, https:\/\/www.genesys.com\/media\/Tony-Bates-Thumbnail-124x144.png 124w, https:\/\/www.genesys.com\/media\/Tony-Bates-Thumbnail.png 270w\" sizes=\"(max-width: 190px) 100vw, 190px\" \/><a href=\"https:\/\/www.genesys.com\/en-sg\/blog\/author?auth=695\" target=\"_blank\" rel=\"noopener\">Tony Bates<\/a> is the Chairman and Chief Executive Officer of Genesys.<\/strong> He leads the company\u2019s strategy, direction and operations in more than 100 countries and oversees a global team of more than 6,000 employees.<\/p>\n<p>Tony has decades of experience steering business-to-business and business-to-consumer companies through major market transitions and rapid scaling. A passionate technologist at heart, Tony began his career in network operations and internet infrastructure, teaching himself to code during his daily train commute. He swiftly gained the business acumen to advance into trusted executive roles at some of the world\u2019s most respected global SaaS companies.<\/p>\n<p>Career highlights include leading Cisco\u2019s Service Provider business, growing its Enterprise and Commercial division to more than $20 billion in annual revenue and serving as CEO of Skype, where he was responsible for expanding the business to over 170 million connected users. Once Skype was acquired by Microsoft, Tony became president where he was responsible for unified communications before serving as executive vice president of business development and developers. In addition to his role at Genesys, Tony serves on the board of directors at VMware.[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1715699589068{margin-top: 1.0em !important;}&#8221;]<strong><img decoding=\"async\" class=\"alignleft wp-image-376651\" src=\"https:\/\/www.genesys.com\/media\/petergraf_01_headshot_2020-272x300.jpg\" alt=\"\" width=\"190\" height=\"209\" srcset=\"https:\/\/www.genesys.com\/media\/petergraf_01_headshot_2020-272x300.jpg 272w, https:\/\/www.genesys.com\/media\/petergraf_01_headshot_2020-131x144.jpg 131w, https:\/\/www.genesys.com\/media\/petergraf_01_headshot_2020-514x566.jpg 514w, https:\/\/www.genesys.com\/media\/petergraf_01_headshot_2020.jpg 1604w\" sizes=\"(max-width: 190px) 100vw, 190px\" \/><br \/>\n<a href=\"https:\/\/www.genesys.com\/en-sg\/blog\/author?auth=290\" target=\"_blank\" rel=\"noopener\">Dr. Peter Graf<\/a> is the SVP of Strategy at Genesys<\/strong>. In his role, he is responsible for developing, communicating, sustaining the Genesys strategy.<\/p>\n<p class=\"p2\">Prior to joining Genesys in 2017, Peter held a variety of executive leadership positions in strategy, development, and marketing throughout his more than 25 years in the global enterprise software industry, most notably as an Executive Vice President at multinational software corporation SAP. Peter earned a doctorate in artificial intelligence from Saarland University and a master\u2019s degree in computer science and economics from Technical University of Kaiserslautern in Germany.<\/p>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_section full_width=&#8221;stretch_row&#8221;][vc_row][vc_column][vc_column_text css=&#8221;&#8221;]We\u2019re standing at the start of one of the most profound transformations in business history, driven by a new generation of generative and agentic artificial intelligence (AI). These technologies are reshaping how organisations deliver customer and employee experiences by unlocking new levels of automation, augmentation, personalisation and optimisation.\u00a0\u00a0\u00a0 AI-Powered Experience Orchestration, once a [&hellip;]<\/p>\n","protected":false},"author":1074,"featured_media":552833,"template":"","tax_priority":[54],"tax_blogtype":[17766],"tax_blogcategory":[18426],"tax_contenttheme":[14902,14903,14904],"tax_bundle":[15268],"tax_contenttheme2":[],"tax_capability_sitewide":[16201,16256,18533],"tax_products_programs":[16477],"tax_buying_job":[16738],"tax_buyer_persona":[16887,16919],"tax_sector":[],"tax_segment":[17079,17104,17125],"class_list":["post-520475","blog","type-blog","status-publish","has-post-thumbnail","hentry","tax_priority-54","tax_blogtype-genesys-executives-en-sg","tax_blogcategory-experience-orchestration-en-sg","tax_contenttheme-improve-customer-experience-en-sg","tax_contenttheme-improve-employee-experience-en-sg","tax_contenttheme-level-up-your-technology-en-sg","tax_bundle-genesys-ai-en-sg","tax_capability_sitewide-ai-and-automation-en-sg","tax_capability_sitewide-digital-en-sg","tax_capability_sitewide-journey-management-en-sg","tax_products_programs-genesys-ai-en-sg","tax_buying_job-job-5-validation-en-sg","tax_buyer_persona-business-en-sg","tax_buyer_persona-technical-en-sg","tax_segment-enterprise-en-sg","tax_segment-midsized-en-sg","tax_segment-smb-en-sg","tax_content_type-blog-en-sg"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/520475","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/users\/1074"}],"version-history":[{"count":9,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/520475\/revisions"}],"predecessor-version":[{"id":579659,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/520475\/revisions\/579659"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/media\/552833"}],"wp:attachment":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/media?parent=520475"}],"wp:term":[{"taxonomy":"tax_priority","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_priority?post=520475"},{"taxonomy":"tax_blogtype","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_blogtype?post=520475"},{"taxonomy":"tax_blogcategory","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_blogcategory?post=520475"},{"taxonomy":"tax_contenttheme","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_contenttheme?post=520475"},{"taxonomy":"tax_bundle","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_bundle?post=520475"},{"taxonomy":"tax_contenttheme2","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_contenttheme2?post=520475"},{"taxonomy":"tax_capability_sitewide","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_capability_sitewide?post=520475"},{"taxonomy":"tax_products_programs","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_products_programs?post=520475"},{"taxonomy":"tax_buying_job","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_buying_job?post=520475"},{"taxonomy":"tax_buyer_persona","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_buyer_persona?post=520475"},{"taxonomy":"tax_sector","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_sector?post=520475"},{"taxonomy":"tax_segment","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_segment?post=520475"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}