Artificial intelligence (AI) can help developers write code, create unit tests, understand codebases, and draft documentation. Yet faster coding and better software delivery are not the same thing.  For CTOs, CIOs, and IT managers, the real question is whether changes can be released reliably, securely, and in line with business requirements. AI Can Accelerate Coding Within Specific Contexts In a Microsoft Research study of GitHub Copilot, participants using Copilot completed a JavaScript HTTP server programming task 55.8% faster than the control group. This demonstrates a benefit for a specific task; it does not establish a comparable improvement across every enterprise development project. Enterprise developers still need to account for business logic, dependencies, architecture, security, and integration. Code generation is only one part of the software development lifecycle (SDLC). Code Quality and Maintainability Still Matter The GitClear AI Copilot Code Quality 2025 analysis of code changes from 2020–2024 reported an increase in the share of changes classified as copy/paste, from 8.3% in 2021 to 12.3% in 2024. This observational finding does not establish AI as the sole cause of the increase. Duplicated or difficult-to-maintain code can increase future maintenance work. AI-assisted development therefore still requires code reviews, engineering standards, and technical decisions informed by the actual system context. If your project requires specific skills or additional delivery capacity, discuss your IT resource needs with Indocyber. [Explore IT Professional Services] Four Challenges Beyond Code Generation 1. Code review still requires engineering judgment Syntactically correct code may still violate requirements, introduce security issues, or undermine maintainability. Reviewers must assess business logic, dependencies, security, and architectural fit. 2. Testing must keep pace with change As coding output increases, regression testing, integration testing, and automated quality checks must validate changes without becoming the next bottleneck. 3. Existing-system integration remains complex Enterprise applications depend on APIs, databases, legacy systems, and intricate data flows. Generating code does not replace knowledge of those systems. 4. Deployment and stability determine production readiness CI/CD, monitoring, rollback processes, and incident response help ensure that completed features remain reliable after release. Measuring Productivity Beyond Coding Speed AI-assisted development should be assessed through multiple indicators: task completion time, code review outcomes, defect rates, test effectiveness, change lead time, deployment frequency, and change failure rate. These measures should be interpreted in the context of each application and team. What Should IT Leaders Do? 1. Start with defined use cases such as documentation, boilerplate code, unit tests, or code analysis.2. Establish review, security, testing, and AI usage standards before scaling adoption.3. Maintain strong foundations in software engineering, debugging, and system design.4. Assess the skills and delivery capacity needed across the entire lifecycle not only during coding. Key Takeaways AI can accelerate coding in certain settings, but its value for software delivery depends on review quality, testing, integration, deployment, and maintenance. The goal is not simply to produce more code it is to deliver dependable software that creates business value. Assessing technical expertise for your next project with Indocyber. [ Consult with us about your needs! IT Professional Services ] Get the latest insights on IT workforce, AI, technology, and business transformation directly through our newsletter.  

Digital transformation in the multifinance industry is pushing the customer journey further into digital channels. Financing applications can start online, customer information can be processed digitally, and many interactions no longer depend entirely on face-to-face meetings. However, an online application is only the first part of that journey. When the process moves on to the transaction and document signing stage, companies need to build trust in two key things: the identity of the party making the transaction, and the signing activity carried out by that party. This is where multifinance digital transformation needs to move from a simple digital application toward a trusted digital transaction. A Digital Journey Needs Trust in Identity In physical interactions, identity can be checked through documents and direct contact. When the customer journey moves to digital channels, companies need a mechanism that builds a similar level of trust in the user's identity. Gartner positions identity verification as a technology organizations use to gain assurance that a person is genuinely the owner of the identity they claim. This capability is increasingly relevant across many customer use cases that require security, compliance, and trust. This need is highly relevant in financial services, including multifinance. In this sector, identity verification is important not only at the early stage of the customer journey. A verified identity also becomes the foundation when the customer continues to activities that require approval and document signing. Certified Identity Verification as the Foundation of Digital Transactions In a digital transaction, a name or data entered by a customer does not, by itself, build the level of trust needed in the user's identity. Certified Identity Verification helps ensure that the identity used in a digital process has gone through verification and validation. This matters because identity does not remain just customer information. It later relates to the many activities the customer performs in the digital journey. When the customer reaches the signing stage, the company needs a clear link between the verified identity and the party who performs the signing. This link is what separates transaction signing based on a verified identity from a process that only places a signature image on a digital document. From Verified Identity to Certified Transaction Signing Once the user's identity has been verified, the digital journey moves on to transaction activity. In a multifinance context, one such activity is signing documents electronically. Certified Digital Signature allows signing activity to be linked to a user identity that has gone through verification and validation. A Certified Electronic Signature (Tanda Tangan Elektronik Tersertifikasi) uses an Electronic Certificate that links the identity of the owner with the data used in the electronic signature process. With this approach, signing does not merely produce a digital document that carries a signature. Transaction signing has identity context behind it. The flow becomes simpler and more connected:  For multifinance companies, this approach helps extend the digital journey from simply offering online application access to digital transactions built on trust in the identity of the parties involved. Read also: Already Digital, So Why Is the Approval Process Still Manual? Identity Verification Is Becoming More Important in Digital Transactions Technology developments also bring new challenges in confirming user identity in digital channels.Gartner notes that financial institutions face the growth of fraud involving technologies such as deepfake, voice cloning, synthetic identity, and the use of AI across various scam methods. These developments make identity verification increasingly important for building digital trust. The focus is not simply adding more checks to the customer journey, but making sure that important digital activities are linked to a verified identity. In transaction signing, this principle becomes even more relevant. The more important the document or transaction being signed, the more important it is to be able to link the signing activity to the identity of the party performing it. From Digital Application to Trusted Digital Transaction A digital application and transaction signing should not be seen as two separate journeys. An online application opens access to services digitally, identity verification builds trust in the party behind it, and certified transaction signing carries that identity into the document signing stage. With this flow, digitalization is assessed not only by the channel the customer uses, but also by the level of trust built throughout the transaction journey. This is where digital experience and digital trust need to grow together. Customers need practical processes, while companies need assurance about the identity of the party making the transaction. Certified Identity Verification builds trust in the user's identity, and Certified Digital Signature carries that identity into the signing activity. This is how the digital journey moves forward:  Multifinance digital transformation is only complete when the transactions a customer makes can be linked to a verified identity. Key Takeaways Multifinance digital transformation is not enough if it only brings the application process to online channels. When a customer continues the journey through to document signing, companies also need to ensure that the transaction is performed by a verified identity and that the signing activity can be linked to the right party. As an implementation partner, Indocyber helps multifinance companies integrate identity verification and Certified Electronic Signature (Tanda Tangan Elektronik Tersertifikasi) from a registered electronic certificate provider into their digital journey. Want to see how identity verification and certified digital signatures can be applied in your multifinance processes? Discuss it with the Indocyber team. [ Consult with our team now!  Certified Digital Signature ] Get the latest technology and business transformation insights from Indocyber.  

The IT roles needed in the AI era go beyond AI Engineers. When companies integrate AI into their existing applications, data, and infrastructure, talent needs usually spread across six roles: AI/ML Engineer, Software Developer with AI skills, Data Engineer, Cloud and DevOps Engineer, QA and Automation Engineer, and Cybersecurity Engineer. Which role takes priority depends on what you want to build. The shift is already visible in the job market. PwC's 2026 Global AI Jobs Barometer, which analyzed more than one billion job postings across 27 countries and territories, found that job postings requiring specific AI skills grew by around 69%, while the overall job market grew by 9%. The number of AI jobs has also nearly doubled compared to 2024. PwC also noted that skills in the jobs most exposed to AI are changing more than twice as fast as in the jobs least exposed to AI. This means the challenge for companies is not only finding new roles, but making sure existing roles have the right combination of skills. This article covers those six roles and how to define the talent requirements for your project. 1. AI / Machine Learning Engineer The AI Engineer is the most obvious role when discussing AI growth. This role focuses on developing, implementing, and integrating AI or machine learning systems, from model development and use of existing AI models, to evaluation and connecting AI with applications or business processes. The growth shows up in LinkedIn data. In Jobs on the Rise 2026 for the United States, AI Engineer ranked first among the 25 fastest-growing roles over the past three years. Note that this data is specific to the US market and does not necessarily reflect conditions in Indonesia. Not every company that wants to use AI automatically needs a large AI Engineer team. The role you need still depends on what you want to build. Developing a proprietary AI model has different requirements from integrating existing AI capabilities into internal applications. 2. Software Developer with AI Skills This is one of the changes we think deserves attention. AI does not remove the need for software developers, but the way developers work is starting to change. Developers can now use AI-assisted tools for code generation, debugging, documentation, code explanation, test generation, refactoring, and exploring solutions. Because of this, it is no longer enough for companies to check only whether a developer masters a particular programming language or technology stack. The ability to use AI productively, evaluate its output, understand security implications, and keep code maintainable is becoming a relevant additional competency. In other words, an AI-skilled developer does not have to be an AI Engineer. Developers can stay focused on Java, .NET, frontend, mobile, or other technology stacks, while the way they work is increasingly shaped by AI. 3. Data Engineer The more companies want to use AI, the more important the questions about data become. Models and AI applications need data that is accessible, processed, integrated, and of adequate quality. This is where the Data Engineer comes in. They build and maintain data pipelines, integrate multiple data sources, and make sure data can be used by both analytics and AI systems. Without an adequate data foundation, a company may have AI tools but struggle to produce output relevant to its business context. AI investment does not only create demand for people who build models. It also raises the importance of talent who ensure the data behind AI is consistent and reliable. 4. Cloud and DevOps Engineer AI applications still need infrastructure. When an AI proof-of-concept starts moving into production, companies need to consider deployment, scalability, performance, monitoring, availability, and cost. This role helps ensure that applications, including those using AI, can: 1. be deployed consistently;2. run on suitable infrastructure;3. be monitored after going into production;4. scale as usage grows;5. be integrated with the development pipeline. AI may change how software is built, but the need to run software reliably does not go away. The more complex the technology environment, the more important the infrastructure and delivery foundation behind it becomes. 5. QA and Automation Engineer AI can help developers produce code faster. However, more code does not automatically mean better software. Output still needs to be validated. QA and Automation Engineers make sure that changes, whether written by humans or assisted by AI, still meet functional requirements, performance expectations, and quality standards. AI can also be used in QA workflows, for example to help generate test scenarios, but using it does not remove the need for quality judgment. As development gets faster, testing and validation capabilities need to keep pace with the increase in output. 6. Cybersecurity Engineer AI opens new productivity opportunities while also expanding the area that needs to be secured. AI-powered applications raise new questions about data access, authentication, model interaction, third-party services, and how sensitive information is used. At the same time, developers who use AI-generated code still need to make sure the output meets the company's security standards. For that reason, cybersecurity is not a function separate from AI transformation. The more widely AI is used in the technology environment, the more important it is for organizations to keep its adoption within appropriate governance and security controls. Read Also: Why Companies Are Starting to Look for AI Engineering Skills, Not Just Programming Skills AI Engineer Is Not the Only Answer There is a tendency to view talent needs in the AI era through a single new role: the AI Engineer. In reality, company needs are far more contextual. 1. If the company wants to build machine learning capability, the AI/ML Engineer may be the main role.2. If AI will be integrated into an existing application, the company may need a Software Developer who understands AI integration more.3. If the problem lies in the data foundation, the Data Engineer may be the priority.4. If the AI application is heading to production, the need for Cloud, DevOps, QA, and Cybersecurity becomes relevant too. In other words: do not start from the role name. Start from what you want to build. AI Changes the Skills Within Roles, Not Just Create New Roles This may be the more important shift for workforce planning. Existing roles also need to evolve: 1. Software Developers need to understand how to use and evaluate AI-assisted development.2. QA needs to understand how AI supports testing, and also how AI-enabled features are validated.3. Data Engineers are moving closer to the data needs of AI.4. Cloud and DevOps need to support new workloads.5. Cybersecurity needs to deal with an expanding attack surface and governance requirements. For this reason, workforce strategy in the AI era should not stop at the question "How many AI Engineers should we hire?" A more useful question is: "Which roles in our project need AI-related skills, and how deep do those skills need to be?" Find out how IT Professional Services can help you meet your need for AI-skilled programmers. From AI Specialists to AI-Skilled IT Professionals Not all IT talent has to become an AI specialist. Companies still need developers, analysts, QA, data professionals, infrastructure specialists, and many other technical roles. What changes is the combination of skills within those roles. A Software Developer with strong programming fundamentals and the ability to use AI-assisted tools offers different value from a developer who relies only on AI to generate code. The same goes for a Data Engineer who understands AI workload requirements, a QA Engineer who can adapt testing for AI-enabled applications, or a DevOps Engineer who understands deploying AI workloads. AI skills are an additional layer of competency, not a replacement for technical foundations. What Does This Mean for IT Professional Services? The shift in role requirements means companies need to be more specific when looking for talent for a project. A request like "we need a developer" may no longer be enough. Companies need to consider: 1. the technology stack required;2. the type of application or system to be developed;3. whether the project involves AI;4. how AI will be used;5. the integration experience required;6. data and infrastructure needs;7. and the level of AI skill relevant to the role. Through IT Professional Services, Indocyber helps companies meet their needs for professional IT talent and technical expertise based on project requirements. As industry needs evolve, talent searches can also include programmers and IT professionals whose skills are increasingly relevant to AI-enabled workflows, without changing the primary focus on matching technical expertise to project needs. Because in the end, companies do not need the role that is most hyped. They need the right combination of roles and skills for what they want to build. Looking for IT Talent for Your Project in the AI Era? Start from your project needs and technology stack, all the way to the competencies required for each role. Discuss your IT Professional Services needs and IT talent requirements with our professional team. [ Consult Your Needs -> IT Professional Services ] Get the latest insights on IT workforce, AI, technology, and business transformation directly through our newsletter.  

Artificial Intelligence in CRM is entering a new phase. After several years in which AI was mainly used to generate content, summarize information, and provide recommendations, the next stage is moving toward AI that can take action and carry out work more autonomously. This shift is visible in the growth of Agentic AI in enterprise software. Gartner predicts that by 2028, 33% of enterprise software applications will include Agentic AI, up from less than 1% in 2024. However, rising adoption does not automatically mean every implementation will deliver business value. Gartner also predicts that more than 40% of Agentic AI projects will be canceled by the end of 2027, largely due to escalating costs, unclear business value, and inadequate risk controls. These two projections illustrate both sides of Agentic AI's growth: the potential is large, but implementation requires the right use case and governance. In CRM, this shift is becoming increasingly relevant. CRM is no longer just a place to store customer data and log activity. With AI Agents, CRM can now help understand context, determine the next step, and even carry out certain activities within a workflow. This approach is becoming known as Agentic CRM. Creatio is one of the platforms developing this approach by integrating AI Agents into its CRM and workflow platform. So what exactly is an AI Agent in CRM, how is it different from a chatbot or ordinary automation, and how does Creatio use it across sales, marketing, and customer service? What Is an AI Agent in CRM? Simply put, an AI Agent in CRM is an AI-based system that can understand context and goals, then determine or carry out actions to help complete work within a CRM process. While Generative AI is typically used to produce a specific output, such as drafting an email or summarizing information, an AI Agent is designed to go further. In a CRM context, an AI Agent can be used for activities such as: 1. Analyzing customer and account data2. Gathering relevant information3. Recommending the next action4. Creating or updating CRM records5. Preparing customer communication6. Helping execute activities within a workflow7. Coordinating multiple stages of work8. Following up based on specific conditions In other words, AI is shifting from simply providing information to actively taking part in the work. This matters because most customer operations are not made up of a single activity. Sales, marketing, and customer service typically involve a combination of data, decisions, communication, and action. An AI Agent Is Not Just a Chatbot or Automation An AI Agent is sometimes equated with a chatbot, an AI assistant, or ordinary automation. In reality, each serves a different function. Rule-based automation executes an action when a certain condition is met. Example: Lead comes in → assigned to sales → notification sent. Generative AI can help produce something based on a prompt, such as drafting a follow-up email. An AI Assistant can provide context-based help, such as summarizing customer history before a meeting. An AI Agent can go further by understanding the goal, using available context, determining the necessary steps, and carrying out actions within the access it has been granted. The difference can be illustrated as follows: ApproachMain FocusExample in CRMRule-Based AutomationExecuting predefined rulesAssigning a lead when certain criteria are metGenerative AIGenerating content or insightDrafting a follow-up emailAI AssistantHelping users get work doneSummarizing customer history before a meetingAI AgentDetermining and executing actions within a workflowAnalyzing an account, preparing for a meeting, and helping carry out the next action The difference lies not only in how intelligent the AI is, but in how far it can participate in the business process. From a CRM That Stores Data to a CRM That Can Act Traditional CRM relies heavily on user activity. A sales representative, for example, needs to open the account record, read the interaction history, check the opportunity, decide on priorities, follow up, and then update the CRM again. The information may already be available, but people still have to do a lot of work to turn that information into action. With AI Agents, this pattern is starting to change. Traditional CRM - Data → User searches → User analyzes → User decides → User acts Agentic CRM - Data + Context → AI analyzes → AI recommends / executes action → Human oversight This means CRM is gradually evolving from just a system of record into a system that can also help drive action. However, the level of autonomy does not need to be the same for every process. High-risk decisions may still require human review or approval. Opportunity vs Implementation Reality Source: Gartner These two figures provide important context: adopting an AI Agent is not the end goal in itself. How Does Creatio Use AI Agents? Creatio has developed an AI CRM approach in which predictive, generative, and agentic AI sit within a single platform architecture. In Creatio's approach, AI is not simply added as an extra chatbot layered on top of CRM. Creatio states that its AI architecture can natively understand CRM objects, workflows, relationships, and business context. As a result, an AI Agent can work using the context of the process already in motion. Creatio also combines this capability with no-code tools, allowing organizations to use pre-built agents while also developing and customizing agents for specific workflows. In simple terms, how it works can be described as:  Creatio supports a human-in-the-loop approach, allowing organizations to set the AI's level of autonomy according to what each process requires. AI Agent for Sales Sales is one of the areas where the use of AI Agents is most directly visible. Sales representatives do not only spend time talking to customers. There is a lot of work behind the scenes: finding account information, preparing for meetings, updating the CRM, creating quotations, following up, and deciding which opportunities to prioritize. Creatio provides a number of AI Agents for sales activities, such as: Account Research Agent; Meeting Preparation Agent; Quote Generation Agent; Forecast Agent; Territory Management Agent; Next Best Action Agent; CRM Data Update Agent; Lead Scoring Agent. For example, before meeting a customer, the Meeting Preparation Agent can help prepare the context and information needed based on available data. The Account Research Agent can help enrich account information, while the Next Best Action Agent can help recommend the next step. The goal is not to replace a salesperson's ability to build relationships or negotiate. Instead, AI can take on part of the information-gathering and administrative work so sales can focus more on tasks that require judgment and human interaction. AI Agent for Marketing Marketing also has workflows that involve a lot of data, content, and repeated decisions. This ranges from defining the audience and creating communications to running campaigns and qualifying leads. In Creatio Marketing, some of the available AI Agents include: Marketing Content Agent; Email Generation Agent; Campaign Agent; Lead Scoring Agent; Lead Distribution Agent. Its use can include creating and personalizing content, scoring and qualifying leads, and helping distribute leads to sales. Creatio also integrates AI with customer profiles and segmentation, so customer data can be used as context within the marketing workflow. Here, an AI Agent does more than help produce content. Its greater potential lies in connecting data → decision → execution within the marketing workflow. AI Agent for Customer Service Customer service has different needs. Service agents need to understand customer history, identify the issue, find relevant knowledge, set priorities, and respond quickly. Creatio provides AI Agents for use cases such as: Customer Support Agent; Knowledge Base Agent; Case Classification Agent; Service Playbook Agent; Next Best Action Agent. For example, AI can help classify cases and provide context to service agents before they handle a customer. The Knowledge Base Agent can help use and manage the information needed to resolve a case. In scenarios like this, AI becomes the intelligence layer that connects customer data, knowledge, workflow, and the service team. Talk to us about your CRM needs with our Creatio AI CRM solution, free of charge! No-Code Makes AI Agents More Flexible One of the things that sets Creatio's approach apart is the combination of Agentic AI and no-code. Companies are not limited to relying on pre-built AI Agents. Through no-code capabilities and AI Studio, organizations can build, customize, test, and manage agents based on their process needs using visual tools and natural language. This matters because every organization's workflow is different. A B2B company's lead qualification process, for instance, can differ from that of a financial services company. The same applies to customer onboarding, approvals, service escalation, or other processes with their own business rules. With this approach, companies can adapt AI Agents to their existing workflows without turning every change into a traditional development project. That flexibility, however, still needs to be paired with governance. From Human Workflow to Human + AI Workflow The biggest implication of AI Agents is not really about any single feature. The more fundamental change is in how companies design their workflows. Previously, digital workflows generally took the form: Human → System → Human → System With AI Agents, a new participant emerges: Human → AI Agent → System → AI Agent → Human Some activities still require a human. Some can be assisted by AI. Certain routine activities, meanwhile, can be run more autonomously. For simple, predictable processes, rule-based automation may already be enough. An AI Agent becomes more relevant when the work requires a combination of context, reasoning, decision, and action. Governance Becomes as Important as Automation Gartner's prediction that more than 40% of Agentic AI projects could be canceled by the end of 2027 is a reminder that technological capability alone does not guarantee successful implementation. The further AI is able to take action, the more important it becomes for an organization to define the limits of its authority. There are at least five areas worth paying attention to: 1. Data Quality AI Agents need reliable data and context. Automation does not automatically fix incomplete or inaccurate data. 2. Access & Permission Organizations need to define what data each agent can access and what actions it is allowed to take. 3. Guardrails There must be clear limits on the decisions and actions AI is allowed to make. 4. Human-in-the-Loop Not every decision should be made autonomously. Certain activities still require human review or approval. 5. Monitoring Organizations need visibility into how AI is used, what agents are doing, and their impact on business processes. In Creatio, AI can be managed through the AI Command Center, which provides a centralized environment for managing AI Agents, access, usage, and deployment. Will AI Agents Replace Sales, Marketing, or Customer Service Teams? This question oversimplifies the change that is actually taking place. In many CRM workflows, an AI Agent is more accurately seen as a digital participant in the work process, not a replacement for an entire role. AI is relatively well suited to activities such as: Search → Analyze → Summarize → Recommend → Execute Routine Action Meanwhile, humans still play an important role in activities that require: Judgment → Relationship → Negotiation → Creativity → Accountability Because of this, the value of Agentic CRM does not have to come from removing people from the process. Value emerges when an organization can determine which parts should be done by people, which parts AI can help with, and which parts are worth automating. Key Takeaways AI Agents mark an important shift in the evolution of CRM. CRM is no longer just a place to store customer information or run predefined workflows. Agentic AI allows CRM to start understanding context, giving recommendations, and carrying out certain actions within a business process.   However, the development of Agentic AI also shows that more automation does not always mean more value. The success of implementation will depend heavily on choosing the right use case, data quality, workflow design, governance, and the right division of roles between humans and AI. Explore How AI Agents Can Be Used in Your CRM Every organization has a different customer journey, workflow, data, and governance needs. Not every process needs autonomous AI, and not every use case requires the same level of autonomy. Talk to Indocyber about your CRM and business workflow needs to explore how Creatio and AI Agents can be applied to fit your organization. [ Discuss Your Needs ->Creatio ] Follow the latest developments in CRM, AI, automation, enterprise technology, and digital transformation through the latest insights from Indocyber.  

The move to Cloud ERP doesn't always start from the same point. Some companies are adopting SAP ERP for the first time and want to build a more integrated system from the outset. Others have been running SAP ERP for years and need to modernize a complex landscape without disregarding the processes, data, and technology investments already in place. SAP provides two journeys for these needs: SAP GROW and RISE with SAP. Both lead to SAP Cloud ERP, but they have different starting points, implementation approaches, and transformation needs. What Is SAP GROW? SAP GROW is SAP's entry point to Cloud ERP, with SAP S/4HANA Cloud Public Edition as one of its main components. SAP GROW is designed mainly for companies that are new to SAP ERP and want to start with a modern cloud platform without having to build an ERP from scratch with a high degree of complexity. Its approach uses preconfigured industry best practices, so companies can adopt standardized processes and configure them to fit their business needs. SAP GROW also embeds AI into core business processes. Companies can start in areas such as finance, supply chain, or HR, then expand capability as business needs grow. Its main characteristics include: 1. Cloud-first with SAP S/4HANA Cloud Public Edition as the core ERP.2. Preconfigured business processes based on SAP best practices.3. Embedded AI within business processes.4. Defined scope and predictable cost to support a more measurable implementation.5. Scalable, so capability can be expanded without switching platforms. SAP GROW is therefore relevant for growing companies that are starting to hit the limits of spreadsheets, fragmented applications, entry-level ERP, or legacy systems, and need a single, more integrated platform. What Is RISE with SAP? RISE with SAP is a transformation journey aimed mainly at existing SAP customers who want to modernize their ERP landscape toward the cloud. Its starting point is different from SAP GROW's. Companies that already use SAP ERP typically have existing processes, integrations, customizations, historical data, and a far more complex technology landscape. The transformation is not just about implementing a new system, but also about determining how that existing landscape can be modernized. RISE with SAP provides the methodology, tools, and guidance to support that journey, including transformation toward SAP Cloud ERP Private. This approach allows companies to plan ERP modernization in a more structured way while managing the complexity of their existing environment. SAP GROW vs RISE with SAP: What's the Difference? The difference between the two is easiest to understand by looking at a company's starting point. This difference shows that choosing between SAP GROW and RISE with SAP is not simply a matter of comparing features. The context of the system a company currently uses is one of the main factors. AreaSAP GROWRISE with SAPPrimary customerCompanies primarily new to SAP ERPExisting SAP ERP customersStarting pointStarting a new Cloud ERPModernizing an existing SAP ERP landscapeCore approachStart fresh and fit-to-standardTransformation and modernizationERP environmentBased on SAP Cloud ERP, with SAP S/4HANA Cloud Public Edition as the core ERPCan involve SAP Cloud ERP or SAP Cloud ERP Private, depending on the transformation journeyImplementationPreconfigured scope and industry best practicesTransformation roadmap tailored to the existing landscapeComplexityDesigned to accelerate adoption and reduce implementation complexityDesigned to handle a more complex transformation landscapeTypical objectiveBuild a modern Cloud ERP foundation and scale with growthMove and modernize an existing SAP environment toward the cloud When Is SAP GROW More Relevant? SAP GROW can be a relevant choice when a company wants to begin its SAP ERP journey with a cloud-first approach. For example, a company may have grown to the point where spreadsheets or disconnected systems are making it harder for finance, supply chain, and other business functions to work from consistent information. Rather than carrying legacy structures into a new system, a company can start with standardized business processes and configure only the areas that genuinely need it. SAP GROW can also be considered when a company needs: 1. an implementation with a more clearly defined scope;2. predictable cost;3. readily available industry best practices;4. embedded AI within core business processes;5. a platform that can grow alongside business expansion. SAP also offers SAP GROW Fast, a structured implementation offering with a predefined scope, methodology, implementation tools, and transparent pricing delivered through certified SAP partners. When Is RISE with SAP More Relevant? RISE with SAP is more relevant when a company already has an existing SAP ERP environment and needs a transformation journey toward the cloud. In this situation, the challenge is usually broader than a new ERP implementation. Existing customers may have custom code, integrations, historical data, infrastructure dependencies, and business processes that have evolved over many years. All of these components need to be considered when a company defines its target architecture and transformation roadmap. RISE with SAP therefore provides an approach that is better suited to modernizing an existing SAP landscape, including the journey from legacy on-premise SAP ERP to a more modern cloud environment. It's Not About Which Is Better, But Where a Company Starts SAP GROW and RISE with SAP are not designed as two competing solutions for the same need. The difference lies mainly in the starting point and the transformation journey. Companies new to SAP may need an approach that lets them start with a standardized Cloud ERP quickly and predictably. Existing SAP customers, meanwhile, need to consider how their existing processes, customizations, integrations, and data can be transformed toward the target environment. Before deciding on a journey, a company therefore needs to understand a few things: Current ERP landscape. What system is in use today, and how complex are its integrations? Process requirements. Which processes can follow industry best practices, and which genuinely need differentiation? Existing customization. Does the existing customization still deliver business value, or does it add technical complexity instead? Transformation objective. Is the company building a new ERP foundation or modernizing an existing SAP landscape? The answers to these questions help a company determine the transformation path that best fits its business situation. Start Cloud ERP from the Right Starting Point A Cloud ERP decision should not start from a feature list alone. SAP GROW provides an entry point for companies that want to start and grow with Cloud ERP, while RISE with SAP provides a transformation journey for existing SAP customers who need to modernize their landscape toward the cloud. A different starting point calls for a different roadmap. As a SAP Partner, Indocyber helps companies assess their business needs and technology landscape to determine the SAP implementation approach that fits their situation and transformation goals. Considering SAP Cloud ERP for your company? Talk to the Indocyber team about your business needs to understand the starting point and transformation journey that fits you best. [ Consult with our experts -> SAP GROW Fast ]  

The deadline is getting closer, the backlog isn't shrinking, and the development team is already working at full capacity. In situations like this, adding more developers is often seen as the most logical choice. That decision can genuinely help. Additional developers can increase work capacity, speed up certain tasks, and bring in technical expertise that isn't yet available within the internal team. However, adding resources doesn't automatically speed up the project as a whole. Delivery speed is influenced by far more than the number of developers. Work distribution, system complexity, cross-team dependencies, approval processes, documentation quality, testing capacity, and deployment pipeline readiness all affect how fast a project gets done. Adding Developers Can Help, But the Impact Depends on the Project's Condition Not every project has the same resource needs. Common causes of project delays include: constantly changing requirements; dependencies between systems or teams; lengthy approval processes; technical debt; inadequate documentation; system knowledge held by only a few people; testing and deployment bottlenecks; development environments that aren't ready; and the time it takes to onboard new developers. If these are the main causes of delay, adding developers will only increase the number of people working inside the same constrained system. That's why the decision to add resources should start with identifying the bottleneck. Companies need to distinguish between: 1. a capacity shortage, where there's simply too much work for the available resources; and2. a delivery system constraint, where the project slows down because of processes, dependencies, or working infrastructure that isn't yet adequate. This distinction matters because the two problems call for different solutions. Data Shows That Team Size Isn't the Only Factor Behind Productivity DORA's State of DevOps report consistently uses metrics such as deployment frequency, lead time for changes, change failure rate, and time to restore service to measure software delivery performance. These metrics show that engineering team performance should be viewed as the capability of the whole system, not just the amount of code produced or the number of developers on staff. According to the DORA 2023 report, organizations with high software delivery performance were reported to have more frequent deployments and shorter lead times than lower-performing organizations. This finding shows that adding developers is more effective when supported by a mature delivery system. Without that foundation, more developers may produce more code changes, but not necessarily faster delivery. New Developers Don't Immediately Become 100% Additional Capacity When a developer joins a project, the company doesn't gain full additional capacity from day one. New developers need to understand: business requirements; architecture and technical decisions; codebase structure; development environment; coding standards; branching strategy; testing process; deployment workflow; dependencies between components; and how the team and stakeholders work. This process takes time, both from the new developer and from existing team members, who are responsible for providing context, doing reviews, answering questions, and making sure the new work doesn't break the system already in production. Read Also: From Headcount to Capability: A New Way to Build an IT Team Adding Developers Can Shift the Bottleneck Project delivery consists of several interconnected stages: requirement analysis; design and architecture; development; code review; testing; security validation; user acceptance testing; approval; deployment; monitoring and support. If development capacity increases while other stages stay at the same capacity, the bottleneck simply shifts to the next stage. For example, additional developers produce more pull requests. But if only one or two reviewers have approval authority, the code review queue only gets longer. That's why capacity evaluation should cover the entire value stream, not just the number of developers. Technical Foundations Determine How Effective Additional Resources Will Be Additional developers deliver results faster when the project has a supportive technical foundation. Some important elements to check include: 1. Version Control and Branching Strategy The team needs clear rules for the repository, branching, pull requests, merging, and release branches. Without these rules, adding developers raises the risk of conflicts and rework. 2. Automated Testing Automated tests give developers faster feedback on their changes. Without adequate automated testing, every change may need lengthy manual validation. 3. Continuous Integration Continuous integration helps the team catch conflicts and errors earlier, which matters more as the number of developers and the frequency of changes increase. 4. Development Environment New developers need to get their environment, access, credentials, test data, and required tools without a lengthy manual process. 5. Documentation Documentation for architecture, APIs, business rules, deployment, and troubleshooting reduces dependency on specific developers. 6. Observability Logging, monitoring, tracing, and alerting help the team understand the impact of changes after deployment, which matters even more as the volume of changes grows. 7. Clear Ownership Every service, module, or business area needs clear ownership so decisions and escalations can happen quickly. Without these foundations, adding developers can increase maintenance and coordination overhead before producing any positive impact on delivery. When Is Adding Developers the Right Decision? Adding developers tends to have a positive impact when the following conditions are met: 1. Scope and Priorities Are Clear The team understands which features need to be completed, their priority, and the acceptance criteria for each piece of work. 2. Work Can Be Parallelized Tasks can be split across multiple developers without creating excessive dependencies. 3. Architecture Supports Divided Ownership Components or services have clear enough boundaries that developers can work in parallel. 4. The Internal Team Has Capacity for Onboarding Existing developers can provide context and reviews without sacrificing all of their own delivery capacity. 5. Testing and Deployment Have Sufficient Capacity Increased development output can actually be processed by the following stages. 6. New Resources Have Relevant Skills The developers joining the project have experience with the relevant technology stack, domain, or project type. 7. Timeline and Targets Can Be Measured The company has a clear baseline to compare the impact of added resources against, such as lead time, backlog aging, throughput, or milestone completion. Under these conditions, adding developers can be an effective way to increase capacity without restructuring the entire project. How to Measure the Impact of Adding Developers Companies need to set indicators before and after adding resources. This measurement helps confirm that the additional developers are actually improving delivery, not just adding activity. Some indicators that can be used include: NoIndicatorDescription1Lead Time for ChangesMeasures the time from when code work starts until the change is available in production.2Deployment FrequencyMeasures how often the team can deploy safely.3Backlog AgingMeasures how long a task or issue sits in the backlog before it's resolved.4Cycle TimeMeasures the time from when a task starts until it's completed.5ThroughputMeasures how much work gets completed within a given period.6Change Failure RateMeasures the percentage of changes that cause an incident, rollback, or remediation.7Time to Restore ServiceMeasures the recovery time when a disruption occurs after deployment.8Review Waiting TimeMeasures how long a pull request waits for review.9Testing Waiting TimeMeasures the wait time before work can move into testing or UAT.10Milestone CompletionMeasures whether project milestones are achieved on target. Measurement should compare the baseline before adding resources with conditions after the new developers become productive. Companies also need to allow enough time for onboarding before drawing conclusions. Adding Resources Still Matters, But Timing and Fit Are What Determine the Outcome None of this means companies should avoid adding developers. When workload increases, deadlines get closer, or a project needs specific technical expertise, external resources can be a practical way to strengthen delivery capacity. The outcome is even better when the added resources have the right skills, understand the project's needs, and can be integrated into the existing workflow. Through IT Professional Services, Indocyber helps companies secure professional IT talent and technical expertise that match project needs, including developers across a wide range of technology stacks and development requirements. This approach is relevant for companies with ongoing projects that need additional capacity or competencies without waiting through a lengthy permanent hiring process. The value of additional developers ultimately isn't determined by headcount alone. The impact depends on: 1. how well their skills match the project's needs;2. clarity of scope and ownership;3. readiness of the technical foundation;4. the team's ability to onboard them effectively;5. testing and deployment capacity;6. and the organization's ability to measure changes in delivery performance. Does Your Project Need Additional Developers? Before deciding what resources you need, first identify the skills, workload, bottlenecks, and technical requirements of your project. Discuss your IT Professional Services and professional IT talent needs with the Indocyber team. [ Consult Your Needs -> IT Professional Services ] Get the latest insights on IT workforce, AI, technology, and business transformation directly through our newsletter.  

Many companies have digitized their documents. Contracts are created in electronic format, documents are shared through systems, and various administrative processes no longer depend on paper. However, a digital document does not automatically mean the entire process around it is already running digitally. Within the approval process, certain stages can still run separately. Documents move from one party to another, the signing itself happens through a different mechanism, and information about signing activity often has to be traced back manually whenever it's needed. This situation points to an important distinction between document digitization and process digitization. Digital transformation isn't only about changing a document's format. Companies also need to look at how that document gets approved, signed, verified, and managed throughout the business process. A Digital Document Doesn't Automatically Create a Digital Process Turning a physical document into a PDF is one step toward digitization. But the business process doesn't stop once a document has been created electronically. A contract, for example, still has to go through approval and signing before it can be used. This is the stage where companies need to check whether the digital process they've built already covers the signing activity, or whether signing still runs as a separate process. This matters because signing isn't just the act of placing a signature on a document. In a business process, companies also need to consider the identity of the signer, the integrity of the signed document, the signer's consent, and the ability to trace back the signing activity. In other words: Document digitization changes the format. Process digitization also has to account for how that document moves and gets approved. Signing Is a Critical Part of the Approval Process In a conventional process, signing is often placed as the last step, after a document has already received approval. When the process moves to digital, the same approach isn't enough if it just replaces a wet-ink signature with an image or a scanned copy of one. There's a difference between an image of a signature on a digital document and an Electronic Signature (TTE) process, which has a mechanism linking the act of signing to the identity of the signer and the electronic information being signed. In everyday business language, the term Digital Signature is more commonly used. In Indonesian regulation, however, the term used is Certified Electronic Signature (TTE). Government Regulation No. 71 of 2019 (PP No. 71/2019) states that Certified TTE functions as a tool to authenticate and verify: - The identity of the signer- The integrity and authenticity of the Electronic Information That regulation also sets requirements for TTE, including that any change to the Certified TTE or its related Electronic Information after the time of signing must be detectable. In other words, when a company digitizes its signing process, what matters isn't only whether a document carries a signature, but also whether the identity, document integrity, and approval behind it can be accounted for. Four Aspects to Consider in Digital Signing A Certified Electronic Signature uses an Electronic Certificate issued through an Indonesian Electronic Certification Authority (PSrE). In the context of the signing process, there are four aspects that can help a company strengthen its control over digital documents. 1. Identity: Verifying the Signer's Identity The signing process needs to be traceable to the party who actually performs the signing. Through identity verification and validation, a Certified Electronic Signature helps confirm the identity of the user performing the signing using their electronic certificate. This provides a stronger basis than simply placing a signature image on a file. 2. Integrity: Preserving Document Integrity A document can pass through several parties before and after it is signed. Because of that, a company needs to make sure the information that has been signed stays intact. An Electronic Signature includes a mechanism that makes any change to the every related electronic information, after signing, detectable. This gives a company stronger control over the integrity of documents that have already been signed. 3. Consent: Strengthening Proof of Approval A signature, in a business process, fundamentally represents a party's consent to a document. Because of that, the digital signing process also needs to connect the act of signing with the consent of the party involved. A Certified Electronic Signature can help strengthen the evidence that signing was carried out with the signer's consent. 4. Auditability: Making Signing Activity Traceable Digitization also needs to provide the ability to look back at what happened, whenever that's needed. Monitoring, tracking, and auditing within the signing process give a company traceable information about activity throughout the process and the certificate lifecycle. Auditability becomes important when a company needs to run an internal review, verify something, or trace back activity related to how a document was signed. Digital signing isn't just about moving a signature from paper to screen. Identity, integrity, consent, and auditability are just as much a part of the process. Integrating the Signing Process into a Digital Workflow Digitizing the approval process doesn't have to stop at using electronic documents. The signing stage also needs to be considered as part of a broader digital workflow. One capability that can support this approach is API integration. With API integration, an Certified Electronic Signature service can be connected to whatever application or digital system a company already uses, based on its implementation needs. This means a company can design its signing process to connect more closely with the systems it already uses, without treating the Electronic Signature as a system that replaces the entire approval workflow. This distinction matters. An Electronic Signature does not mean the entire approval process gets automated. Approval routing, business rules, approval stages, and other operational processes still depend on the system and workflow design a company uses. The Electronic Signature's role sits at the signing step, helping strengthen identity, document integrity, consent, and traceable activity. From Manual Approval to a More Integrated Process A company doesn't have to change its entire process at once. A good starting point is looking at the documents that most often require approval and signing, for example:  From there, a company can map out how its signing process actually runs. For instance, whether signing is already integrated with the applications in use, how the signer's identity is verified, how document integrity is maintained, and whether signing activity can be traced back. This approach helps a company see digitization not merely as a project to cut down on paper, but as an effort to build a more connected process with the level of control the business actually needs. Digitization Should Change the Process, Not Just the Document Format Turning a physical document into a digital one is the first step. But transforming the process requires attention to what happens after that document is created. How does the document get approved? How is the signing done? How is the signer's identity verified? How is document integrity maintained? And how can signing activity be traced back when it's needed? This is where a Certified Electronic Signature can become one component within a company's digital workflow. With a verified signer identity, preserved document integrity, provable consent, auditability, and the ability to integrate through an API, a company can build a signing process that is more connected to its digital business needs. Indocyber provides a PSrE-based Digital Signature solution to help companies strengthen their signing process and integrate it with their digital system needs. Learn About Our PSrE-Based Digital Signature Solution! Want to know how a Digital Signature can be applied to your company's existing process and systems? [ Consult with our team now! -> Certified Digital Signature ] Get the latest technology and business transformation insights from Indocyber.  

When workload increases or a new project kicks off, adding more people is often a company's first response. That approach can indeed add capacity. But headcount alone does not necessarily answer what a project actually needs. A team can have plenty of people and still hit a bottleneck when the required skill isn't available. This is even more true as AI, cloud, data, cybersecurity, and other technologies keep shifting what IT competencies are needed. Because of that, building an IT team needs to look at two things at once: how many resources are available, and whether the competencies inside it match what the project needs. Headcount Answers How Many, Capability Answers What Is Needed Headcount shows how many people are available on a team. Capability looks further, at the competencies that can actually be used to run the business and project needs. For example, a company can have an IT team with adequate headcount. But when a project needs technical expertise that isn't available internally, that headcount doesn't automatically make the team ready to run the project. The same thing can happen when: 1. The team has strong technical skills, but workload is already too high.2. A new project needs different competencies than the previous one.3. A certain skill is only available with a few team members.4. Technology needs change faster than internal competency development.5. The company needs additional expertise for a specific period or project. In situations like this, the challenge isn't always a shortage of people. What's missing may be the right skill at the right time. IT Skill Needs Are Becoming Increasingly Dynamic Companies used to be able to build a technical team based on a relatively stable technology stack. Now, that landscape moves much faster. Cloud, automation, data, cybersecurity, and especially AI keep influencing how applications are developed and how IT teams work. AI is the most visible example. Developers can now use AI to help with coding, debugging, documentation, testing, and even exploring solutions. But the ability to use AI alone isn't enough. A solid technical foundation is still needed to make sure the output is: 1. Aligned with the requirement.2. Built on correct logic.3. Safe to use.4. Able to integrate with existing systems.5. Maintainable in the long run. Because of that, what companies need hasn't shifted from technical skill to AI skill. What has happened is that the competency bar has risen. Companies still need programmers, engineers, analysts, QA, and various other IT competencies, but that talent also needs to become more adaptive to AI-enabled workflows and evolving technology. Building a Team Doesn't Always Have to Start with Adding Headcount When a skill isn't available yet, permanent hiring is certainly one option. But not every technology need should be immediately translated into new permanent headcount. Companies can look at a few conditions first: 1. Is this skill a strategic competency that will keep being needed?2. Is the competency actually already available, but the team lacks capacity?3. Did the need arise because of a specific project or workload?4. Can the internal team develop that competency through upskilling?5. Does the project need additional expertise to keep the timeline on track? From here, companies can decide on a more suitable workforce combination. Permanent hiring can be used to build long-term internal competency. Upskilling helps the existing team keep up with evolving technology and new ways of working. Meanwhile, IT Professional Services or IT Outsourcing can be used to fill resource and technical skill gaps as project needs require. These approaches don't have to replace one another. Companies can combine them based on business needs. —Read also: Mencari Talent IT di Era AI to see how AI is starting to reshape the skills and competency standards expected from IT talent.— The AI Era Makes Skill Relevance Even More Important AI makes the headcount versus capability discussion even more relevant. When some work can be done faster with AI's help, team productivity is no longer determined by headcount alone. The quality and relevance of skill matter more than ever. A programmer using AI, for example, still needs technical judgment to determine whether the generated code is fit to use. A QA team can use AI to help with some testing activities, but still needs an understanding of quality standards and business requirements. The same goes for other IT functions. AI can become part of the workflow, but humans still decide how the technology is used in the context of the project. Because of that, when looking for IT talent, companies can start paying attention to a combination of competencies such as: 1. Technical expertise that fits the project need.2. AI literacy, to understand how AI can be used within the workflow.3. Critical judgment, to evaluate the output of the technology.4. Adaptability toward new tools and ways of working.5. Business understanding, so technical execution stays aligned with project goals. AI skill does not replace technical skill. AI actually widens the combination of skills a company needs. IT Professional Services as Part of a Workforce Strategy Shifting skill needs mean companies need more flexibility in building their IT teams. This is where IT Professional Services can become one approach. IT Professional Services helps companies obtain resources with the technical expertise that matches project needs, especially when the internal team needs additional capacity or specific competencies. This approach can be relevant when a company: 1. Is facing excess workload on the internal team.2. Needs additional resources to support a project.3. Needs a specific technical skill that isn't yet available internally.4. Needs flexibility in adjusting resources to project needs.5. Wants to maintain productivity and project delivery as talent needs change. In the AI era, these considerations keep evolving further. Companies need not only talent skilled in specific technical areas, but also talent that can keep up with changing tools and workflows. Because of that, the need for AI-skilled programmers and IT professionals who are adaptive to AI is likely to become part of future IT Outsourcing and IT Professional Services needs. That doesn't mean every talent has to become an AI specialist. What matters more is the ability to combine technical expertise with readiness to use AI in relevant ways at work. From Adding People to Strengthening the Team Shifting focus from headcount to capability doesn't mean headcount no longer matters. Headcount still determines capacity. But capacity only delivers business impact when it is filled with relevant competencies. Permanent hiring can build the core team. Upskilling can keep internal competency relevant. Meanwhile, IT Professional Services or IT Outsourcing can help fill resource and technical expertise gaps as needed. With the right combination, companies don't have to choose between building an internal team or using external talent. The focus is making sure the project has the people and competencies it needs to keep moving forward. Strengthen Your IT Team to Match Project Needs Through IT Professional Services, Indocyber Global Teknologi helps companies meet resource and technical expertise needs to support IT projects, including skill needs that keep evolving alongside technology and AI. [ Consult Your Needs -> IT Professional Services ] Get the latest insights on IT workforce, AI, technology, and business transformation directly through our newsletter.  

Customer Relationship Management (CRM) has evolved far beyond its original function as a database for storing customer information. Modern organizations need a CRM that connects customer data, sales activity, marketing engagement, customer service, and various business workflows within a single integrated environment. At the same time, companies also need the flexibility to adjust those processes as business needs and customer expectations change. Creatio takes a different approach to this need. Instead of positioning CRM merely as a business application, Creatio combines CRM, workflow automation, AI, and no-code application development into a single unified platform. This approach allows organizations not only to manage customer interactions, but also to build and adjust the business processes behind them. So, what exactly is Creatio CRM, and where can this platform deliver value for a business? From CRM as a Database to an Operational Platform At its core, Customer Relationship Management is a system for managing, organizing, and automating customer interactions and data throughout the customer lifecycle. CRM generally helps organizations to: 1. Centralize customer and account information2. Manage leads and opportunities3. Monitor sales activity and pipeline4. Manage marketing activities5. Handle customer requests and service6. Automate follow-ups and routine workflows7. Analyze customer data and business activity According to Creatio, the evolution of CRM today is increasingly tied to the use of AI and autonomous agents to support activities such as lead scoring, audience segmentation, content generation, recommendations, and workflow automation. Creatio takes this concept further through an AI-native CRM and workflow platform for marketing, sales, and service. The platform has three core CRM applications: 1. Creatio Sales - supports the sales process end-to-end.2. Creatio Marketing - supports the lead-to-revenue process and customer engagement.3. Creatio Service - supports customer service and service operations. All three run on Creatio's broader platform capabilities, allowing companies to build CRM processes beyond standard, out-of-the-box functionality. What Sets Creatio Apart? Creatio's difference becomes clearer when you look at the architecture behind its CRM. Instead of forcing companies to choose between using standard CRM functions or undertaking extensive custom development, Creatio provides a composable no-code architecture. Business applications can be built and extended using reusable components, blocks, applications, and workflows. This changes the way companies approach CRM customization. In the conventional approach, a business process change might follow this path: Business Requirement → IT Request → Development → Testing → Deployment With a no-code approach, more configuration can potentially happen closer to the business and process team: Business Requirement → Visual Configuration → Validation → Deployment However, no-code doesn't mean IT's role becomes unnecessary. Complex integrations, architecture, security, governance, and certain development needs can still require technical expertise. The difference lies in an organization's ability to reduce its dependence on development for changes that can actually be resolved through configuration. Visual: How Creatio Connects Customer Operations Business AreaCreatio CapabilityPotential Business ImpactMarketingCampaign, segmentation, lead management, customer journeyMore coordinated customer acquisition and engagementSalesLead & opportunity management, pipeline, sales workflowBetter visibility across the entire sales cycleServiceCase management, service workflow, customer interactionMore consistent customer service processesWorkflow AutomationVisual workflow and process automationReduced manual coordination between teamsNo-Code DevelopmentVisual designer and reusable componentsFaster application and process adaptationAIPredictive, generative, conversational, and agentic capabilitiesAutomation and decision support within customer workflowsCustomer DataCustomer information and Customer 360 capabilitiesCustomer context usable across functions The bottom line: Creatio doesn't just add automation to a CRM. Its architecture is designed to connect customer data, applications, workflows, people, and AI within a single environment. 1. No-Code as a New Way to Build Business Processes One of Creatio's key characteristics is its no-code development capability. Creatio provides visual tools that let organizations configure applications, interfaces, business rules, and workflows without relying entirely on traditional software development. Its composable architecture also allows various components to be reused across other applications and processes. This becomes relevant for organizations with business processes and customer journeys that change frequently. Imagine a financial services company wanting to implement a new customer onboarding process. That workflow could include: 1. Collecting customer informatio.2. Verification process3. Internal approval4. Creating follow-up activities5. Routing for exceptions6. Updating the customer record7. Sending notifications Rather than turning every change into a separate development project, these activities can be configured and orchestrated through visual workflow tools. This is why the strategic value of no-code isn't just about “building applications without coding.” The more important value is an organization's ability to change business processes without creating a development bottleneck that could otherwise be avoided. 2. Composable Architecture: A CRM That Can Follow Business Needs CRM implementations often present a trade-off. Companies can adapt their business processes to the available software, or undertake extensive custom development to make the software follow their processes. Both come with consequences. Too little flexibility can force teams to run inefficient workflows. On the other hand, too much custom development can increase complexity and make future changes harder. Creatio's composable architecture offers another approach. Applications can be built using reusable components and functional blocks, allowing organizations to adjust CRM capabilities incrementally as needed. For example, a company could start with standard opportunity management, then add: 1. Industry-specific approval process2. Custom account scoring3. Partner management workflow4. Contract approval5. Customer onboarding6. Compliance checks7. Custom dashboard With this approach, the CRM can evolve incrementally alongside changing business needs. 3. AI-Native CRM and Workflow Automation Another important development is Creatio's focus on AI-native CRM. Creatio integrates predictive, generative, and agentic automation capabilities into its platform. Depending on the use case and process applied, AI can support activities such as: 1. Lead qualification and prioritization2. Customer segmentation3. Content and email generation4. Recommendations and next-best action5. Meeting preparation6. Workflow assistance7. Customer service activities8. Automated CRM tasks This development points to an important shift in how CRM is used. Traditional CRM mainly helps users record and find information. AI-enabled CRM can help users understand information and determine the next action. Agentic CRM takes this concept further by allowing AI agents to carry out certain activities within a workflow. But for business leaders, the main question isn't simply whether a platform already has AI. The more important questions are: 1. Which decisions are safe to automate?2. Which activities still require human control?3. Is the available customer data reliable enough?4. How will AI actions be governed?5. Where can automation genuinely reduce cycle time or operational effort? AI delivers greater value when it is supported by reliable data and well-designed business processes. 4. One Customer Context for Marketing, Sales, and Service Customer experience is almost never the responsibility of a single department. A prospect might first interact with marketing, then move into the sales process, become a customer, and later interact with customer service. When these activities run on separate systems, customer context can become fragmented. CRM helps address this problem by creating a shared environment for customer information and interaction history. Creatio's Customer 360 approach is designed to consolidate customer and account context so it can be used across various processes. For example: Marketing → Lead → Sales Opportunity → Customer → Service Case → Retention / Cross-Sell As a result, each stage of the customer lifecycle doesn't have to operate as an isolated workflow. The business value isn't simply having more customer data, but ensuring that relevant customer context is available whenever a team or workflow needs to make a decision. When Does Creatio Become a Relevant Choice? Creatio can become relevant once a company's CRM needs have grown beyond basic contact management and pipeline management. Some scenarios worth considering are when an organization: 1. Has complex or frequently changing customer processes2. Needs cross-departmental workflows3. Needs more flexibility than a packaged CRM process can offer4. Relies heavily on IT for relatively simple workflow changes5. Has industry-specific CRM requirements6. Wants to integrate CRM and workflow automation7. Is starting to build AI-enabled customer operations8. Needs applications that can evolve alongside business change However, no-code doesn't mean no governance. As more teams gain the ability to build and change applications, organizations still need clear standards around: 1. Data architecture2. Access and permissions3. Integration governance4. Application lifecycle management5. Testing6. Security7. Process ownership Without proper governance, the ability to build applications faster can actually create complexity faster too. What Should You Evaluate Before Choosing Creatio? At least five dimensions should be evaluated: 1. Process Fit How well can the platform support the company's customer journey and operational workflow? 2. Adaptability How difficult is it to change that process as business needs evolve? 3. Integration How will the CRM connect with ERP, core business applications, communication channels, and existing data sources? 4. Governance Who can create, change, approve, and deploy workflows and applications? 5. AI Readiness Is the company's data and process architecture mature enough to use predictive, generative, or agentic AI responsibly? Choosing a CRM, therefore, shouldn't just be a feature-list comparison, but an evaluation of which platform can support the company's future operating model without creating complexity that becomes difficult to manage. Key Takeaways Creatio shows how the role of CRM is evolving. CRM no longer functions only as a system of record for storing customer information. Modern CRM platforms are increasingly becoming where customer data, workflow, automation, AI, and human decision-making meet. Creatio combines: CRM + No-Code + Workflow Automation + AI + Composable Architecture into a single unified platform. For organizations with complex customer journeys or continuously evolving business processes, this combination can deliver more flexibility than managing CRM and process automation as separate technology initiatives. Ultimately, the biggest opportunity isn't simply implementing a new CRM. It's building a customer operations platform that can adapt as fast as the business changes. Is Your CRM Ready for the Next Stage? Every organization has a different customer journey, workflow, integration requirements, and governance. Consult with Indocyber to learn more about how Creatio can support your CRM and business workflow transformation. [ Discuss Your Needs -> Creatio ] Follow the latest developments in CRM, AI, automation, enterprise technology, and digital transformation through the latest insights from Indocyber.  

AI adoption is no longer new for businesses. The next challenge is far more complex: how can organizations move AI beyond tools and pilot projects and make it part of how the business operates end-to-end? Data from McKinsey illustrates the scale of this gap. In The State of AI in 2025, 88% of respondents said their organizations regularly use AI in at least one business function. However, only about one-third have begun scaling AI, and just 7% report that AI has been fully scaled across their organizations. Agentic AI shows a similar pattern. While 62% of organizations are at least experimenting with AI agents, only 23% are scaling agentic AI in at least one business function. Across every business function examined by McKinsey, no more than 10% of respondents reported that their organizations had scaled AI agents. In other words, AI adoption does not automatically translate into AI readiness. A company may have numerous AI use cases and still lack the processes, data, systems, and governance required for AI to operate in an integrated way at enterprise scale. This is where the concept of the Autonomous Enterprise becomes relevant. An Autonomous Enterprise Is More Than a Company That Uses AI SAP defines an Autonomous Enterprise as an organization capable of continuously sensing what is happening across its operations, reasoning based on business context and predefined rules, and acting across end-to-end processes quickly and at scale without relying on manual coordination at every stage. However, autonomous does not mean handing every business decision over to AI. In this model, people define objectives, policies, constraints, and decisions that require judgment. AI agents help execute work, coordinate processes, and take action within predefined boundaries. SAP summarizes this through three principles: 1. People set the direction, and AI executes.2. When conditions change, the business can respond as one.3. Governance enables the business to move faster rather than simply acting as an additional layer of control. The difference from more conventional AI implementation becomes clearer when we look at the operating model. AI Layered on the BusinessAI Embedded in the BusinessAssistants support individual usersAgents execute end-to-end processesActions are initiated by peopleExecution can be event-drivenAutomation is fragmented by task or functionWorkflows are orchestrated across domainsData remains distributed across multiple systemsData shares a common semantic business contextGovernance is added after executionGovernance is embedded into execution This comparison is adapted from SAP's framework distinguishing AI that is simply layered on top of business processes from AI that is embedded into the operational core of the enterprise. Why Do So Many AI Initiatives Remain Stuck in the Pilot Stage? The gap between adoption and scale is not simply a question of AI model capabilities. SAP points to a more structural challenge. AI is often implemented on top of operating models and system landscapes that were never designed to support coordinated AI execution at enterprise scale. As a result, an individual use case may perform well on its own but become difficult to scale when it has to interact with real-world processes, data, systems, and risks. SAP identifies three major gaps. 1. Lack of Business and Process Context Generic AI can read enterprise data, but it does not automatically understand how the business actually operates. Consider a purchase order. To an AI model, a purchase order may appear to be a collection of fields and transactional data. Within an actual enterprise, however, a single purchase order may be connected to approvals, compliance checks, goods receipts, accounting entries, supplier payments, and audit requirements. Each stage may also be governed by policies that determine which actions are permitted next. Without this business and process context, AI may be able to generate insights or recommendations, but it may not be able to execute decisions safely within the organization's operational constraints. Readiness questions for businesses: 1. Are core processes documented and standardized?2. Are decision logic and approval rules clearly defined?3. Can dependencies between processes be identified?4. Is it clear which conditions require human intervention? If the process itself is unclear, giving AI greater autonomy may simply amplify uncertainty. 2. Disconnected Data and Systems AI can only make decisions based on the context available to it. The challenge is that enterprise data is often distributed across multiple applications with different definitions, semantic models, and ownership structures. Finance, procurement, supply chain, and other functions may even interpret the same entity or metric differently. When AI has to reason across these systems, it may effectively be looking at only a partial view of the business. An output may appear reasonable within one context but create risk or rework when the decision moves downstream into another process. For this reason, data readiness for an Autonomous Enterprise is not simply about having large volumes of data. Organizations need to ensure that their data is: 1. Accessible - AI can access the data it actually needs.2. Consistent - data definitions and structures do not conflict.3. Contextual - data carries relevant business meaning.4. Connected - information can be used across systems and processes.5. Governed - data access and usage are subject to clear controls. SAP positions shared process models, unified business data semantics, and enterprise-wide AI governance as important components of an operating model in which AI is embedded into the business. 3. Governance Cannot Be an Afterthought SAP emphasizes that an Autonomous Enterprise requires governance to be embedded directly into execution. This includes: 1. Policies encoded into execution - agents operate according to the rules, constraints, and objectives defined by the organization.2. Identity and access control - every action has a clearly defined identity and permission structure.3. Auditability by design - activities are recorded as part of execution, including who or which agent acted, what authority was used, which data informed the action, and what outcome resulted.4. Exception handling - when conditions fall outside predefined parameters, decisions are escalated back to people with the context needed to act. In this context, governance is not an obstacle to autonomy. Governance determines how far autonomy can safely extend. From AI Assistants to AI Agents Most enterprise AI usage today still operates within an assistance model. AI helps people find information, create drafts, analyze data, or summarize reports. These applications remain valuable because they can improve individual productivity. However, an Autonomous Enterprise requires a shift from AI that helps people perform work to agentic AI that can execute work across processes. SAP distinguishes the two roles: Assistants as collaborators Assistants serve as an interaction layer between people and AI. They help surface insights, understand situational context, coordinate agents toward specific outcomes, and allow people to provide direction or conduct reviews. Agents as executors Agents perform specific multi-step tasks using available skills and tools. They can initiate workflows, apply business rules, execute downstream actions, and escalate exceptions when human judgment is required. This transition matters because processes no longer have to move forward only when someone remembers to initiate the next step. Execution can become event-driven: conditions change, a signal is detected, a decision is made based on context, and the relevant action is executed. Sense, Reason, Act: The Operating Loop of an Autonomous Enterprise SAP uses three capabilities to explain how an Autonomous Enterprise operates. 1. Sense - Continuous Awareness An organization needs to detect changes as they happen. Signals may come from transactions, financial positions, inventory, demand, supplier events, workforce data, or external factors. The key difference from traditional reporting is timing. Signals do not have to wait for a weekly review or monthly reporting cycle before triggering a response. 2. Reason - Contextual Decisioning A signal alone is not enough. AI needs to determine whether the change requires action and which action is most appropriate. Reasoning requires business context: organizational policies, historical knowledge, objectives, cost structures, customer commitments, compliance obligations, and risk tolerance. This is what separates a recommendation that merely appears reasonable from a decision that is genuinely appropriate for the enterprise context. 3. Act - Coordinated Execution Once a signal has been understood and a decision made, agents execute the relevant actions through connected systems. This may involve executing a transaction, initiating a workflow, adjusting a plan, or escalating an exception to a person. When sense, reason, and act operate as a continuous loop, an organization can respond to change as one system rather than as a collection of functions operating independently. Is Your Business Ready? McKinsey's findings suggest that the challenge facing organizations is no longer simply getting started with AI. 88% of organizations already use AI in at least one function, but only 7% report that AI has been fully scaled. For AI agents, interest also significantly exceeds scale: 62% are at least experimenting, while 23% are scaling agentic AI in at least one function. This does not mean every organization should pursue full autonomy as quickly as possible. Quite the opposite. Organizations need to understand which parts of their foundation are not yet ready before expanding autonomy. AreaReadiness QuestionBusiness ProcessAre core processes standardized with clearly defined decision logic?DataIs data high-quality, consistent, connected, and supported by relevant business context?IntegrationCan processes operate across applications and functions?GovernanceHave policies, permissions, auditability, monitoring, and human oversight been defined?AI ExecutionIs it clear which activities agents are permitted to execute and when people need to intervene?Operating ModelAre the respective roles of people and AI in processes and decision-making clearly defined? This checklist is a practical synthesis of SAP's Autonomous Enterprise framework, not an official SAP maturity model. The Priority Is Not “More AI,” but a Stronger Foundation McKinsey found that 80% of respondents said efficiency is an objective of their organizations' AI initiatives. However, organizations generating the most value from AI are more likely to also pursue growth or innovation as objectives. Overall, only 39% of respondents reported EBIT impact from AI at the enterprise level. These findings demonstrate why an AI strategy should not stop at automation or the number of AI use cases deployed. An Autonomous Enterprise requires a more fundamental question: Which processes offer enough business value to transform while also having sufficiently mature process context, data, integration, and governance to support AI execution? Organizations can assess potential use cases across four dimensions: DimensionQuestionBusiness ValueHow significant is the potential impact on revenue, cost, productivity, customer experience, or risk?Process ReadinessIs the process sufficiently stable and standardized?Data & Integration ReadinessDoes AI have the context and cross-system access required to operate effectively?Risk & GovernanceWhat are the consequences if AI takes the wrong action, and are the necessary controls already in place? From there, organizations can distinguish between use cases that are ready to scale, those that require foundational improvements first, and processes that are not yet appropriate for greater autonomy. An Autonomous Enterprise Starts with Readiness The journey toward an Autonomous Enterprise is not a race to deploy as many AI agents as possible. The more fundamental transformation is building an enterprise that can sense, reason, and act as an integrated system with AI that understands process context, works with connected business data, executes actions across systems, and remains within the governance defined by the organization. McKinsey's data reinforces the urgency of this transition: AI adoption is already high, but enterprise-scale deployment remains far more limited. The gap suggests that the next challenge is not simply acquiring AI technology, but building the operating foundation that allows AI to generate value consistently at enterprise scale. As an SAP Partner, Indocyber Global Teknologi helps organizations build and develop the enterprise technology foundation required for SAP-driven business transformation. For organizations evaluating ERP modernization and their readiness to move toward a more intelligent and connected enterprise, you can learn more and discuss your requirements with our team. [ Consult with our experts -> SAP GROW Fast ] To stay updated on AI, enterprise technology, SAP, and business transformation, subscribe to the Indocyber email newsletter for our latest insights and updates.