Introduction
In today’s rapidly evolving digital landscape, businesses are increasingly embarking on digital transformation initiatives to stay competitive and drive innovation. However, these initiatives come with their own set of challenges and uncertainties. Effective risk management is crucial to navigating these complexities and ensuring the success of digital transformation efforts.
The importance of risk management in digital transformation cannot be overstated. It helps organizations anticipate and prepare for potential issues, ensuring that projects stay on track and within budget. By proactively managing risks, businesses can avoid costly disruptions, safeguard their investments, and achieve their strategic objectives. Neglecting risk and resource management often leads to significant cost leakage, derailing projects before they deliver value. Considering this, let’s explore common risks in digital transformations and actionable strategies to mitigate them.
Common Risks in Digital Transformation
1. Technological Risks
- Risk #1 Legacy systems: Many organizations rely on older systems which are often incompatible with modern technologies. This creates challenges with integration, causing delays and complications. Additionally, legacy systems tend to be costly to maintain and upgrade, which can strain the allocated budget for new initiatives. These systems are frequently customized and modified over time, resulting in highly tailored, complex structures that make integration even more difficult. Such modifications often turn the system into a tangle of adaptations, complicating any attempts to connect or replace it. Another critical challenge is contract management. When nearing the end of a contractual term for a legacy system, organizations often face a hard deadline. Extending the contract is usually undesirable, as the system is already slated for replacement. This creates time pressure, limiting the available window to transition to a new system effectively. What are some measures you can take to minimize or completely erase this type of risk?
Mitigation plan: Modernizing Legacy Systems: Investing in the upgrade or replacement of legacy systems is essential to ensure compatibility and improve efficiency. This may involve adopting cloud-based solutions or seamlessly integrating new technologies with existing systems. As part of your digital transformation efforts, consider which legacy systems can be replaced simultaneously. While this increases project scope and complexity, it also offers significant long-term benefits if executed effectively. Given the risks associated with heavily customized legacy systems, a thorough system mapping is critical. This helps you understand the full impact of replacing the system and ensures the transition is handled optimally. From a contract management perspective, having a strong procurement team is invaluable. They can monitor upcoming contract expirations and negotiate extensions if necessary, providing additional time to implement changes without disrupting operations. - Risk #2 Security Risks: Implementing new technologies can open new security threats, including cyberattacks and data breaches. These risks can result in the loss of sensitive information, damage to the organization’s reputation, and significant financial losses. With cyberattacks rising in frequency, robust security measures are non-negotiable. Any company can be targeted, which puts a higher requirement on their cybersecurity and preparedness. This means that every organization should have measures to minimize this risk, here are some examples.
Mitigation plan – Security Measures: Implement robust security protocols and regular security audits to protect against cyber threats. This includes training staff in security awareness, using advanced security tools, and having an incident management plan. An important step is also allocating a security officer who should act as the owner of the project’s security and make sure there are no gaps present that could be exploited. Another step in this work is to conduct an information security screening where you identify and class information volumes in your systems and make sure that you have identified what information is located where and that you have the right measures in place to prevent any leaks and if leaks happen that you have measures to minimize the impact. Read more about what security measures you can implement in this article: Information Security: How Secure is Secure Enough?
2. Organizational Risks:
- Risk #1 Resistance to Change: Employees may be reluctant to adapt to new work methods and technologies, which can hinder progress. This resistance can stem from fear of the unknown, lack of understanding of the benefits of change, or concerns about job security. Poorly managed change initiatives often fail to address employee concerns, leading to disengagement. Some employees will then put up resistance, and the project will not have the desired effect. For more information on this topic, read our article: Tools for Managing Cultural Differences in Global Teams
Mitigation Plan – Active Change Management: In order to mitigate the resistance to change you should develop a clear change management plan. The plan should include communication, training, and support for employees to be effective. It is important to involve employees in the change process, listen to their concerns, and provide them with the necessary tools and resources to succeed. An often-overlooked part of a change plan is to include more senior stakeholders, as they are not always thought of as part of the project. Including and involving the senior stakeholders actively will help with getting other employees on board, as the stakeholders usually hold major influence and power, which can be a great tool to use. - Risk #2 Lack of Skills: Digital transformation requires specific skills that may not be present within the organization, leading to a skills gap. This can delay projects and increase costs for hiring or training staff. We can see that this risk is common when a project has begun without considering what specific skills will be needed but rather what roles are needed. The skill gap, if not addressed, can lead to big delays, a faulty solution, or failure to complete the initiative, leaving your company with a huge cost and nothing of value to show for it.
Mitigation Plan – Skills Development: Invest in training and recruitment to ensure the organization has the necessary skills to carry out the transformation. This may involve offering internal training programs, partnering with educational institutions, or hiring experts in relevant fields. Consider identifying important previous experiences when hiring new employees; it is important that the new hires have the right previous experience and not only the correct previous role. This is especially important when hiring for long-term and ongoing projects, as the new employee will need to adapt to and grasp the project without having been involved from the start. Therefore, prior experience and possessing the right skill set are essential, as they will help shorten the onboarding period and ensure a smoother transition for the new hire. - Risk #3 Scope Creep: As projects progress, there can be a tendency for the scope to expand beyond the original objectives. This can lead to increased costs, extended timelines, and resource strain. Scope creep often occurs due to unclear project requirements, stakeholder demands, or inadequate project management. Scope creep can occur during independent releases of a digital transformation initiative, creating complexity for coming releases or simply expanding the scope of a project.
Mitigation Plan – Strict Scope Governance: To mitigate the risk of scope creep, it is crucial to clearly define the project’s scope and objectives from the start. Implementing strict change control processes ensures that any adjustments are managed effectively, preventing the project from losing focus or expanding uncontrollably. A robust governance model is equally important. Establishing a clear, structured process for evaluating proposed scope changes helps determine their necessity and identify the best course of action if changes are warranted. This disciplined approach maintains project alignment with its goals while minimizing disruptions.
3. Economical Risks
- Risk #1 Cost Overruns: Projects can become more expensive than planned due to unexpected expenses or inaccurate budget estimates. This can lead to project cancellations or negatively impact other parts of the business. Cost overruns are common in projects where the buyer overlooked the complexity of the project, for example, the complexity or vastness of the scope or the complexity in the execution. When underestimating either of these will subsequently lead to insufficient budgeting, undervaluing the resources and the time needed.
An additional aspect is the lack of budget and cost review/follow up which causes a lack of insight into spend and therefore no control or proactivity will be possible.
Mitigation Plan – Careful Budgeting: Create a detailed budget with allowances for unexpected expenses and regular follow-ups to keep costs under control. It is also important to have a flexible budget that can adapt to changing conditions and needs. Take into consideration the complexity and take input from people with this kind of project experience as well as the starting project team, as they will have a more detailed view of the budget and potential costs and risks with it. This bottom-up approach will help you get a closer understanding of the actual cost instead of the top-down approach, where we estimate what we want the cost to be and not what we believe it will be. When the budget has been created, and the project has started it is very important to monitor the spending and have regular follow-ups regarding the budget. This is to not lose control of costs and to be able to be proactive when you identify any major deviations. - Risk #2 Return on Investment Risks: It may take longer than expected to see returns on investments in digital transformation. This can create frustration among stakeholders and affect the organization’s financial stability and support. This risk is usually caused by not keeping major stakeholders included in the information loop. This risk can also occur when stakeholders are not made aware of the gains the project has made even though it is progressing as intended. The root cause of this risk is usually that the stakeholders have not been informed or educated on the progress and gain of the project.
Mitigation Plan – Measuring Return on Investment: Implement methods to continuously measure and evaluate the return on investments to adjust strategies as needed. This can include setting clear goals and key performance indicators (KPIs), conducting regular evaluations, and adjusting project plans based on collected data. Further, this risk can be mitigated by capturing “quick wins,” which shows the stakeholders the value the project adds early on and can create momentum for the project moving forward. Measuring the Impact of Digital Transformation Projects
Handling risks
From our perspective, a common approach to effective risk management is creating a RACI matrix. This tool clarifies roles by defining who is Responsible, Accountable, Consulted, and Informed. It helps ensure accountability in executing the risk mitigation plan, preventing tasks from being overlooked. Without a clear process, risk management can become inconsistent. It is crucial to work with risks continuously throughout the project, rather than relying on a single risk workshop at the beginning. Regular updates and ongoing risk management efforts help keep the project on track and reduce potential issues over time.
In resource-constrained projects, we have seen that prioritization is crucial to address the most critical risks without overextending capacity. A practical method is to evaluate risks based on their likelihood and impact. By scoring risks on these factors, you can focus on those with the highest scores, ensuring that resources are allocated where they will have the greatest effect. If you want to go a step further you could add a layer that covers the complexity of the risk, the bigger the circle the more complex the risk and it’s solution will be.
Conclusion
Digital transformation offers enormous opportunities for organizations to improve their operations and create long-term growth. However, to succeed, it is important to be aware of the risks that may arise and to have a clear plan to manage them. By modernizing legacy systems, implementing robust security measures, developing a strong change management plan, and carefully monitoring budget and return on investment, organizations can minimize risks and maximize the benefits of their digital transformation. With the right strategy and preparation, digital transformation can become a catalyst for success and innovation.
Effective risk management not only safeguards your digital transformation investments but also fosters a culture of resilience and adaptability. By staying aligned with stakeholders, maintaining clear communication, and being prepared to adapt to challenges, organizations can navigate their digital transformation journeys with confidence and achieve their strategic objectives.
Authors
Tobias Nilsson, and Natalie Mellin
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The current landscape
AI is transforming the current business landscape at an accelerated rate with capabilities like predictive analysis, process automation, and real-time, data-driven insight. AI-powered tools provide great potential on their own but when combined with ERP systems they can create a truly transformative force for businesses at scale.
ERP systems provide the digital infrastructure needed to industrialize AI capabilities at scale, enabling organizations to implement AI across their organization – resulting in commercially powerful solutions that drive efficiency and growth.
However, AI implementation within ERP still faces challenges such as deeply established workflows, poor data quality and lack of expertise, causing bottlenecks for implementation in most large organizations. Despite these challenges, AI-powered ERP systems are already addressing these gaps. For instance, predictive analytics can enhance demand forecasting, enabling organizations to optimize inventory levels and avoid stockouts. Similarly, AI-driven automated invoice processing can streamline accounts payable, reducing manual errors and administrative burdens.
AI-integrated ERP systems
Today’s traditional ERP systems can be used as a springboard for scaling AI solutions.
The road of AI implementation at scale requires infrastructure, standardized process, AI design and workforce empowerment. While proofs of concept (PoCs) don’t require full maturity in these areas, scaling AI sustainably demands all above mentioned dimensions. Without addressing these – AI won’t be able to scale and be a sustainable technology within large organizations.
For example, SAP has introduced its generative AI assistant Joule. This AI integrates across software environments to enhance processes like supply chain management and finance and exemplifies how AI can be embedded into ERP systems to drive comprehensive digital transformation.
What is required to make this AI transformation?
The first step in this transformation is to overcome the mentioned initial challenges in implementing AI capabilities.
The first challenge is that existing processes are often deeply ingrained within the organization, making it difficult to restructure them to fit these new technological possibilities:
- Integrating AI into established processes usually meets this type of challenge. Existing workflows may be deeply established, making it difficult to introduce new technologies without disrupting the old way of working – causing a need for a thorough approach to change management.
- Another challenge is the data quality. Poor data quality does not make it possible to feed the AI solution with the necessary inputs needed to enable its potential.
- The third main challenge is to find the right professionals and expertise within the AI area to solve some of the unique challenges that appear in such a project. Being able to bridge this AI-ERP skills gap is critical.
How can organizations meet these challenges?
a. Adopting a holistic approach
A holistic approach in this context would involve viewing processes not only as end-to-end but as connected rather than isolated tasks. This perspective encourages organizations to evaluate and improve the workflows themselves, not only separate tasks within the flows. To adopt a holistic approach in process design enables leveraging the most from AI capabilities. It’s advantageous to rethink the processes, end-to-end, to avoid the approach of trying to put in pieces of a puzzle where they don’t fit. By instead rethinking processes through an end-to-end perspective, the overall workflow can be designed effectively, leveraging AI capabilities and enhancing overall operational efficiency.
b. Data quality from the start
The foundation of any successful AI integration lies in high-quality data. Ensuring data quality means that the information collected is accurate, relevant, and timely. Prioritizing data integrity from the beginning will help to set a solid foundation for AI applications.
Vital dimensions to consider is firstly the importance of clean data. Data must be free from errors, duplicates, and inconsistencies to ensure that AI algorithms can process it effectively. Dirty data can skew results, leading to misguided strategies and wasted resources. To achieve this, it is important with regular data cleansing routines—such as deduplication and standardization.
Furthermore, implementing strong data governance practices is essential for managing data. This includes defining roles and responsibilities for data management, establishing policies for data usage, and ensuring compliance with relevant regulations.
c. Vital expertise
Integrating AI into established ERP systems requires professionals with specialized skills in both advanced technologies and business transformation. This presents a significant challenge as organizations often face skill gaps and difficulties in managing organizational change. Finding the right blend of both technical and business value understanding is essential for implementing AI technology with a high ROI – as up to 80% of AI initiatives fail.
Key takeaways
Integrating AI effectively into ERP systems ensures that organizations can move beyond isolated proofs of concept and truly scale their capabilities. By treating data with care, reimagining processes end-to-end, and bringing in the right expertise, companies can unlock sustainable growth and build a competitive edge for the future.
- End-to-end process thinking avoids fragmented solutions and drives efficiencies
- High-quality data and strong governance provide a solid foundation for AI
- Balancing technical skills with business acumen ensures that AI investments deliver lasting value
Interested in discussing the topic of integrating AI into your ERP further?
Contact UsAuthors
Johan Valeur & Mattias Lindeborg
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Previously, many Enterprise Resource Planning (ERP) solutions were customized for individual clients, however over the years this has shifted towards the development of standardized business processes that fits the needs of many verticals and/or industrial applications. But how easy is it to implement these standardized processes and what does it take to fit your business into a standardized ERP platform?
What is Fit-to-Standard?
The need for companies implementing ERP systems have been largely identified as similar across various industries. This similarity has enabled ERP providers to develop standardized processes per industry that are pre-configured within the system. These standardized processes serve as a foundational baseline when constructing the ERP setup for a company, where discussions focus more on “Do the standard solution fit, if not what adaptation of the business way of working is needed?”, rather than designing a customized process work-flow in the system from scratch.
The need for Fit-to-Standard ERP systems:
The reasons companies frequently opt for standardized processes during ERP implementation can vary significantly among organizations. However, several factors are commonly observed across the market.
- Cost: One significant factor influencing the choice of standardized processes is cost, and more precisely, the total cost of ownership (TCO). By developing a standardized ERP system configuration for a specific market, ERP suppliers can avoid the need to construct unique architectural frameworks for each client. Moreover, employing an established standard process for discussions enhances efficiency, as it simplifies the task of identifying necessary changes compared to developing requirements from the ground up with regards to their architectural feasibility. Consequently, this methodology saves both time and cost for the supplier, which reflects positively on the price towards its customers.
- Best practice: Many organizations that opt to implement ERP systems and standardize their processes do so based on the assumption that the industry-wide requirements used as a foundation for the ERP frameworks are not only sufficient but optimal. This creates a form of trust for the organization that the preconstructed setup in the ERP system, often for business support processes, is best practice. Consequently, rather than adapting the ERP system to align with current business processes, the organizations reorganize their operations to fit the standardized business processes as structured in the ERP system.
- Continuous improvements: ERP systems continuously develop over time with improved functionality and addition of new features, leading to most providers offering regular system updates. The timing and frequency of these updates can vary depending on the specific ERP system, typically occurring annually or biannually. By adhering closely to standardized processes, system upgrades can be implemented with relative ease. However, substantial modifications to the system, such as aligning it with current existing business practices and deviating from standard configurations, may hinder the organization’s ability to run updates effectively. As a result, many installations of previous generation ERPs operates without support at high risk and frequent issues. This technical debt produces unfavorable impacts across the organization. By following established standards, one significantly reduces the likelihood of this issue.
The implementation process of standard ERP solutions
The process of implementing an ERP standard system will inevitably vary depending on the organization. However, there are several common steps that are specific for the standardized process that are typically observed across most ERP standard implementations. Below is a high-level summary of these steps;
- Walkthrough of standardized process: A common practice during an ERP implementation is for the implementation partner to conduct a walkthrough of the preconfigured standardized processes within the system. This is typically executed through workshops that include the implementation partner, system architects, and business representatives from the organization.
- Capturing business requirements: Although the standard ERP configuration is generally considered best practice, it must still be tailored to meet the specific needs of the organization. Consequently, the standard systems present configuration options, and the focus is to select and detail an option.
To achieve an optimal configuration design, it is essential to invest in educating the business representatives in the new system. This will enable them to critically assess existing business processes and collaborate with the implementation partner to identify opportunities for improvement. - Adaptation of system: ERP architects focus on making the required adjustments to the preconfigured ERP system based on the identified business requirements. These changes ensure that the system is properly aligned with the organization’s needs while maintaining the integrity of the standardized process.
The best-practices for success:
An ERP implementation is a challenging project for most companies. The ideal prerequisites with clear requirements, sufficient funding, top management support, capable implementation partner, and availability of experienced business resources are rarely met. To balance priorities and align with the given situation, and yet ensure an efficient program when building several new teams a common understanding for selected implementation method is crucial. Both system vendors and implementation partners offers implementation methods and these methodologies offer a structured framework, outlining key activities to be carried out by the business. However, several additional factors are critical to the success of the ERP standard implementation. Below, we highlight some of these key factors.
Essentials for ERP success
- Set a target state: It is crucial to establish a clear target state for the implementation. This clarity empowers business professionals to make informed decisions, as they will have a clear understanding of what way the organization is headed. A common target state and guideline for ERP standard implementations is for organizations to adhere to standards to the greatest extent possible. Exceptions to this rule should typically only be made due to legal requirements. This approach will reduce the need for extensive discussions about “who’s way of working” one should follow within the organization and creates a just change for all parties involved.
- Defining a roadmap: ERP implementations are often regarded as the most significant IT transformations an organization will undertake, and due to their scale, they are typically executed in multiple releases. To ensure successful implementation it is crucial to approach the implementation in a well-structured manner, carefully considering how to set up the scope for each release. This involves leveraging the standardized processes of the ERP system as a foundation while also considering the company’s unique characteristics, such as size, structure, and geographics. In addition, the existing landscape is a crucial element, and it is essential to comprehend how it can be effectively replaced.
One intermediate approach is to structure the roadmap based on capabilities. By mapping the capabilities utilized across different departments within the organization, a clear pattern and logical approach for the ERP implementation will emerge. The goal of this approach is to prioritize key capabilities during the initial implementation. Still, one must consider constraints when replacing existing solutions. This step-by-step approach is advantageous from a structural perspective, as it facilitates smoother integration of process components and ensures system coherence, ultimately optimizing operational efficiency as the implementation progresses. - Scope control: During a long implementation program business develops and new requirements and priorities evolve. Consequently, it is essential to regularly review and adjust the scope and roadmap to accommodate for these changes. The detailed plan for each release typically adheres to the predefined implementation process, with each activity dependent on the successful completion of the previous one. Consequently, the waterfall planning approach is often utilized for managing detailed activities during ERP implementations. However, an agile approach should be applied at a higher level, primarily for managing the scope of each release and overall roadmap, to ensure that business needs are continuously addressed and met throughout the implementation process.
While changes to the scope are inevitable and to a certain extent should be encouraged, it is concerning when the scope not only changes but expands significantly.
A recurring challenge that organizations often encounter when implementing ERP standard, frequently resulting in scope expansion, is the accurate identification of authentic business requirements. This difficulty often stems from business representatives challenges in distinguishing between descriptions of existing workflows and true requirements, particularly during the early stages of implementation. This is often derived from their unfamiliarity of the ERP system and its possibilities. To mitigate this risk, it is beneficial to establish structured forums that facilitate discussions between business representatives and the ERP architects. This collaborative environment facilitates the integration of diverse competencies, drawing from both business expertise and in-depth ERP knowledge, thereby ensuring the development of a good solution.
When implementing an ERP standard, organizations will adopt a single system to cover a broad spectrum of business processes, contrasting with the typical current situation where multiple systems are in use. As a result, any modifications made throughout the standard process will likely trigger additional adjustments. It is therefore important to continuously reassess and ensure that all aspects of the process are thoroughly accounted for during the business requirements gathering phase. - Minimum Viable Product – MVP: Even when requirements are articulated effectively, the sheer number of them can become overwhelming, leading to the critical issue of prioritization. To address this, the concept of a Minimum Viable Product (MVP) approach is commonly advocated. This entails that organizations should prioritize and protect their most valuable requirements throughout the implementation process. The MVP concept can therefore serve as a strategic framework, offering essential guidance and support in the decision-making process, particularly when it is necessary to prioritize and reduce the number of requirements.
Interested to discuss your ERP related goals or maximize your ERP investments?
Contact UsAuthors
Ida Svensson, Johan Saks and Mattias Lindeborg
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Introduction
Procurement departments are transforming their organization in response to emerging external impact factors in recent years, coupled with internal pressures from stakeholders and customers. Primary impact factors are geopolitically, socioeconomically, and macroeconomically including prospective regulations, technological advancements, sustainability imperatives, evolving customer requirements, and digitalization trends.
Moreover, organizations are increasingly relying on Procurement for strategic advice and guidance to navigate the complexities of the evolving external impact factors. The procurement function develops into a strategic advisory role, encompassing the acquisition of new technologies and partnerships, adaptation to organizational-wide regulations, and the enhancement of strategic capabilities through organizational growth as well as mergers and acquisitions (M&A).
This initial article, drawn on Opticos’ expertise, will examine and exemplify the major impact factors influencing the role of Procurement. The subsequent article will explore the essential capabilities required to transition into a strategic procurement function.
Exhibit 1: Factors influencing role of procurement
Geopolitical, Socioeconomic and Macroeconomic Impact Factor: Planning for uncertainties and new ways of working
In recent years, the business environment has been associated with volatile economic conditions, geopolitical tensions, natural disasters, and global pandemics, which heavily disrupt global organizations and supply chain operations. The challenge for Procurement will be to ensure a stable risk management process and guiding the organization in strategic advisory dialogues. The risk management approach involves conducting assessments encompassing financial, new innovations, geopolitical impacts, and supply chain considerations for all suppliers and their sub-suppliers to ensure stable operations and managing daily uncertainties. Furthermore, global Procurement departments drive organization-wide advisory dialogues on strategic targets such as central vs local sourcing decisions as well as offshoring vs. nearshoring of operations to enable closer operations of supply chain and to limit sustainability impact of supplier chain.
The Procurement organizations’ way of working has also evolved as their work becomes more highly impacted by uncertainties and global risks. Uncertainties and flexibility towards global impact factors make business evaluations and fast adoption of business plans a key in the current business environment. Procurement leaders and employees need faster embrace new digital technology to become more effective, such as proactive supplier risk management solutions. The mindset of being a networker to collaborate with internal stakeholders and suppliers will strengthen the way of working with changing circumstances and new pre-requisites. Procurement leaders will need to advise on guidelines for overall processes, determine roles and responsibilities and policies for regulatory new areas. The way of working could change into empowerment of strategic procurement advisory functions to be able to cooperate with all changes and uncertainties at larger companies.
The positives of global networks, stable operations and digitalization will further enable Procurement to promote the value of moving beyond a transactional way of working. Strategic supplier relationships require effective supplier collaboration and communication, which can drive innovation and foster optimal outcomes for both parties during volatile macroeconomic conditions. Through stable risk management process based on technology and strategic relationships Procurement can bring hard facts to the decision-making forums.
Exhibit 2: Skillsets required for new age CPO
Future Regulations, Digitalization and Technical Break Point: Building Strategic Vision and Business Insights
The procurement function has gained more mandate and strategic advisory post-covid as of the crisis management need for strengthening M&A processes, rapidly changing organizational targets, and increased technology initiatives. During the macroeconomic volatility as well as new regulatory demands Procurement has established itself in a unique position as the bridge between the internal organization and external environment. This bridge within the company must quickly manage the cross-functional collaboration to investigate usage of new technology (internal and external), provide insights into policies on new regulations, investigating business benefits for the organization to enable strategic vision for the organization.
Digitalization of procurement processes is vital for building trust of fact-based decisions towards business insights. The growth in data management and its extract of key insights on base data for procurement are vital today as technologies for contract management, purchase to pay, and supplier relationship management are well mature technologies. The systematization of these processes into touchless automated purchase-to-pay, smart contracts, and supplier assessment for sustainability will be considerable for effective procurement organization. Adopting new technologies and automation play a key role in building and ensuring stable operations. Predictive intelligence can empower organizations to enhance resilience in the face of disruptions and enable Procurement to proactively identify alternative sources for business.
This unique position requires Procurement to deliver more market intelligence reports, engage on supplier consolidations and drive digitalization programs as well as organizational-wide regulatory data reporting. The availability of data and advanced analytics has transformed procurement into a data-driven function.
Procurement needs to quickly adapt to future legislations within sustainability, digital/technical product evolution and truly understand the impact in new business scenarios by establishing value beyond cost focus. New business scenarios will set new category management approaches allowing for tailoring the supplier strategies to be flexible and adopted. Some categories are experiencing downward product or service cost pressures, and fewer regulatory incentives, while other categories are facing tougher supply constraints and the high impact of regulatory legislations.
Customer Requirements and continuous improvement: Embedding Sustainability into Procurement
Customer demands drive increased requirements of embedding sustainability into Companies’ operation. Sustainability is today an integral part of Procurement ways of working, with the latest EU Green Deal, many initiatives steer organizations towards increased sustainability efforts. Procurement needs to drive with both internal and external processes to ensure transparency into supply chain operations and to comply with sustainability regulations. Furthermore, Procurement is often responsible by executive management to lead implementation of processes, follow up suppliers’ compliance as well as maintain supplier communication to align on reporting of sustainability efforts and targets. Incorporating new regulations will be part of continuous improvement work for procurement organizations.
Organizations are today, more than ever, under pressure from customers to also deliver a high degree of service simplicity as well as sustainable products. Procurement’s internal activities range from streamlining processes by automating manual tasks to become more effective, to utilizing analytics for identifying trends, developing sourcing strategies and managing supply chain visibility and resilience. Digitalization and availability of data has opened several new ways of working and taking care of rote procurement activities, leaving procurement teams with the more strategic and value-adding activities. Procurement must continue to capitalize of being a data-driven organization to empower as an advisory function that make informed decisions, ensuring sustainable supply chain, and long-term high-quality products/ services by continuous improvement.
Summary
In a world filled with uncertainty, important qualities for Procurement are resilience, adaptability, and innovation when transitioning towards a strategic advisory function. This sets the tone for Procurement’s transformation, evolving from a transactional function to a strategic powerhouse guiding organizations through challenges like geopolitical shifts, technological advancements, and sustainability imperatives.
The pace of change in the business landscape is never ending and to remain effective Procurement needs to commit to continuous learning and improvement. Organizations that excel in these areas can position themselves as strategic drivers of business success and contribute beyond cost savings by enhancing supplier relationships, mitigate risks, and support sustainability goals. An Autonomous Procurement way of working seems to be the next step in our digitalization, and it is probably some technological changes away. In our next article, we will present the future Procurement Capabilities to ensure a competitive edge in the dynamic world of modern business. Stay tuned for the next release.
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Mattias Johansson & Marie Lund
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Introduction
In today’s rapidly evolving business landscape, many organizations struggle to bridge the gap going from strategy to implementation. A useful framework to drive this transformation is the concept of business capabilities*. By defining what an organization must be able to do to execute its strategy, business capabilities provide a concrete, actionable bridge from strategic goals to implementation.
Capabilities as a Vehicle for Change
Business capabilities are in fact the essential building blocks of the organization’s business value streams. They define what the organization needs – in terms of processes, people/skills and technology – in order to deliver value and achieve its strategic objectives. Using capabilities as a framework for change helps focus the organization on what truly matters — aligning resources, technology, and processes toward achieving its strategic goals.
Business capabilities, like value streams, are stable, but how they are delivered evolves over time. This makes capabilities a powerful tool to manage transformation, as they provide a consistent lens through which both current and future needs can be viewed.
Capabilities offer a clear focus for change, as they cut across traditional silos of people, processes, and technology, aligning these elements toward achieving strategic outcomes
Step 1: Conducting an As-Is Capability Analysis
The first step in any change or transformation is to understand the organization’s current capabilities, or the “as-is” state. Organizations often lack a detailed understanding of their current capabilities, which makes this analysis crucial.
The as-is analysis focuses on identifying the organization’s current capabilities in relation to the goals of the transformation. Rather than being an exhaustive assessment, it should prioritize areas critical to addressing the challenges or opportunities driving the transformation. This means concentrating on which capabilities are strong, which need improvement, and which may be missing—always with the overarching transformation objectives in mind.
This focused approach streamlines the analysis, avoiding unnecessary complexity, and provides a clear, actionable baseline that lays the groundwork for meaningful and goal-oriented transformation efforts.
Step 2: Developing a Target Capability Model
Once the current state has been assessed, the next step is to develop the “to-be” or target capability model. This model serves as a blueprint of the capabilities and value streams needed to achieve the organization’s strategic objectives in the future.
Each capability should be designed to meet future demands by refining its core components: aligning people (skills and roles), enhancing processes (workflows and practices), and upgrading tools and systems (technology and infrastructure), where applicable.
The target capability model is a powerful communication tool, clearly articulating the future vision and ensuring alignment among leadership, stakeholders, and teams. By detailing how capabilities will evolve, it provides a shared understanding of the transformation’s direction and steps required to achieve it.
Step 3: Identifying and Analyzing Gaps
The gap between the as-is and to-be capabilities becomes the basis for the transformation. These gaps highlight where the organization needs to improve, invest, or re-engineer processes, technologies, and structures.
The clear definition of these gaps enables the organization to break down the transformation into manageable work packages, ensuring that each effort is focused, actionable, and aligned with the broader strategic goals. Different gaps will require different levels of effort to close, and they won’t all be equally critical. Therefore, it’s essential to prioritize them based on the potential value they deliver and the cost of execution.
Step 4: Prioritizing Transformation Activities
The next step is to prioritize the gaps and the activities needed to close them. This prioritization should be based on two factors:
- Value Delivery: What is the expected impact on the organization’s ability to execute its strategy once the gap is closed?
- Cost of Execution: How complex or resource-intensive is it to close the gap?
By focusing on high-value/lower-cost activities first, organizations can start realizing value early in the transformation process while managing risk.
Step 5: Creating a Transformation Roadmap
With the gaps identified and prioritized, the next step is to create a transformation roadmap. This roadmap serves as a detailed guide, breaking down the work into actionable packages that address specific gaps. By structuring these work packages, the roadmap provides a clear sequence of activities required to enhance and evolve business capabilities to meet the transformation goals.
Each work package in the roadmap should include defined timelines, required resources, and key dependencies to ensure smooth execution. This granular approach not only makes the transformation more manageable but also helps stakeholders stay focused on achieving measurable outcomes while staying aligned with the organization’s strategic priorities.
The transformation roadmap should be structured in a way that maximizes value delivery early and reduces risks. This approach builds momentum, generates quick results, and lays a strong foundation for sustained progress throughout the transformation.
Step 6: Building a Business Case and Value Delivery Plan
The value of closing capability gaps should be quantified in a business case to justify the organization’s investment in the transformation. This business case should outline the anticipated returns, such as time or cost savings, improved efficiency, enhanced customer experiences, or faster time-to-market.
In parallel, a value delivery plan ensures that the transformation remains focused on delivering measurable outcomes, ensuring that each closed gap contributes to the overall strategic goals.
Conclusion
Transforming an organization’s capabilities is essential for successfully executing its strategy and achieving sustainable competitive advantage. By using the concept of business capabilities to guide this transformation, organizations can bridge the gap between strategy and execution, ensuring a continuous focus on strengthening the business value streams.
The step-by-step approach outlined — from conducting an as-is analysis to developing a target capability model, identifying gaps, prioritizing activities, creating a transformation roadmap, and building a business case — provides a clear and actionable path to transformation. Each step builds on the previous one, ensuring alignment with strategic goals while breaking down complex changes into manageable, prioritized work packages.
This structured process not only drives measurable value but also empowers organizations to adapt, innovate, and thrive in an ever-changing business environment, turning strategic vision into operational reality.
* A business capability is a high-level representation of what an organization is able to do to achieve its strategic goals and fulfill its mission. It defines the organization’s capacity to deliver specific value — whether internal or external — independent of how it is achieved. Capabilities are relatively stable over time, even as processes and technology evolve, and they are modular, often spanning organizational structures and crossing functional boundaries.
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Maria Nelsson, Mario Gonzalez Muñoz & Mikael Palm
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Generative AI is great, but it lacks precision and can be very wrong
Since ChatGPT was introduced in November 2022, the subject of AI and its possibilities has gained significant attention in business and technology sectors. Our previously published articles and insights have explored both the potential business value of various AI types and the challenges associated with generative AI.
Generative AI, particularly Large Language Models (LLMs), can assist organizations with daily tasks and enhance product offerings by producing human-like text and answering general knowledge questions. Having been trained on large sets of data written by humans, these models perform rather well at these tasks. However, challenges arise when LLMs are supposed to handle queries beyond publicly available information or capture events after the data cut-off date. In addition to that, the assertive tone in LLM-generated text can be convincing for users despite the generated text being false, this is usually called hallucination.
In this article, we will explore the concept of Retrieval-Augmented Generation (RAG) as a method to address these challenges with LLMs. We start with an overview of different methods to address the challenges with LLMs before looking at what RAG is and how it works. We conclude by looking at the benefits RAG can bring to an organization.
Which methods can be used to get more precise and reliable results?
Figure 1. Popular AI methods to improve accuracy of Generative AI and large language models
To improve the output of the LLM and address these challenges, several approaches can be employed. It’s worth bearing in mind that no single technique is universally superior; the best approach depends on the specific context.
A commonly suggested way of increasing the accuracy of an LLM would be prompt-engineering. For some cases the interactions and phrasing of the questions may result in better output, however the output still relies on the data it was trained on. As a tool prompt engineering is a key capability within generative AI, but it does not solve the issue related to non-public data or data that was created after the cut-off date.
Both custom model and fine-tuned models will perform better for queries requiring specific data. There are however two major drawbacks: they are costly and the time gap between training and user prompting remains.
Neither custom models, fine-tuned models nor unmodified LLM models can provide text that is based on information that has been produced since the latest update of the underlying model. In these models the issue of being limited by the cut-off date remains, but if accuracy of the output is not dependent on recent information or the information may change slowly, these models may provide value for the user.
Between prompt engineering and custom or fine-tuned models is an approach which does not rely on training a model but still provides output based on data for a specific use case. This approach is called retrieval augmented generation, RAG. It is a combination of different types of AI where one part consists of AI technologies to retrieve your data that is relevant to the prompt, the retriever, and the second part is generative AI, the generator.
What is RAG?
When feeding the LLM with data through the prompt, a basic approach would be to input a section of text into a standard prompt with some prompt engineering. This provides context for the question the user wants the AI-generated text to be based on.
The essence of RAG takes this basic approach a step further. In a RAG method, the prompt is prepared with the context that has been retrieved from your own data source. Afterwards, the LLM answers the user question based on this prompt.
The idea of a retriever is getting a section of relevant text from your own source data in text format. To achieve this the text is converted into numerical format, capturing its meaning. This type of format allows comparison of different text sections based on their meanings. Both reference text from source documents, and user question undergo this conversion. The purpose of the comparison procedure is to select the section(s) of text from the source documents that are closest in meaning to the text in the user question.
Figure 2. Basic RAG architecture
An example where we have seen RAG being used is in chatbots for product support. It is common for product user guides and manuals to be kept in a knowledge/data base. When a user needs support, through a RAG app, the user query would be used to search for relevant information in the user manuals or product documentation. Information would be retrieved first, then provided to the prompt for the LLM to give the user advice on how to use the product.
The architecture above and the example described represent a basic form of RAG. There are various methods of creating RAG applications for different purposes, these methods vary in complexity. Generally, the more complex methods of RAG will give more accuracy at the cost of effort to create and manage the RAG application. Conversely, the simpler approaches will be less accurate but easier to create and manage.
What does this mean in the world of utilizing generative AI?
More trusted AI applications
RAG would make it possible to get context-specific output from LLMs based on our non-public data and information, by providing the LLM with this data and information as context in the prompt. It lowers the risk of hallucinations, and we should also be able to trust the output from the AI model to a higher degree than we normally can. We should, if we manage the knowledge base, know where the input to the context for the query comes from.
Accuracy and precision from RAG
In the bigger picture, RAG offers a way to get more precise output from generative AI using our own data. It combines the strength of text generation in a human-like way from generative AI and other AI technologies to search for and retrieve relevant information. With this approach, RAG applications leverage different forms of AI specialized for different tasks, such as writing human like text or relevant finding text. This enables higher accuracy in the output than an LLM alone can produce.
Adaptability and flexibility from RAG
RAG offers a relatively flexible knowledge base where the model does not need retraining or fine-tuning to be able to adapt when the context changes. It is still a less flexible way to work with generative AI than prompt engineering, because RAG still requires a knowledge base and will generate answers related to information in the knowledge base. With proper management of the knowledge base, we should be able to maintain and update the databases that are used as knowledge sources. While this is something that requires effort, if we want to use generative AI when there is a requirement for information to be recent and provide insights on enterprise data, then having a RAG app with a maintained knowledge base is a possibility to get this up-to-date information.
Strengthen your data science and data management capabilities
RAG has existed as a methodology since the early days of generative AI. It offers a way to increase the accuracy and timeliness of information by leveraging the strengths of various AI models currently on the market. Additionally, this allows us to use enterprise or context specific documents to provide useful information to the generative AI model. However, the cost here is that in many cases there is a need for data scientists and data engineers to set up and maintain the RAG app, including the knowledge base and retriever. While this overview might give the impression that building a RAG application is easy, several considerations need to be carefully done to create a good RAG app. Once RAG is explored deeper, there are several ways to go about building the app and optimizing the performance for the specific use case. An organization starting with RAG needs to understand its use cases and data needs, have proper data management practices in place and establish and maintain AI and data science capabilities.
At Opticos we enable organizations to leverage the business benefits of new emerging technologies. Drawing from our extensive client experience and methodology in business strategy, change management, data management and AI governance, we’re here to support you in your AI journey from strategy to implementation. Through our strategic partnership with Algorithma, a company offering data science and hosting services for AI, we provide end-to-end AI capabilities to our clients.
Write to us to discuss your organizational AI goals.
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Eric Wallhoff, and Tatiana Schön
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Introduction:
In July 2024 the European Parliament made a historical decision to approve years of work going into the legislation named “Eco-design for Sustainable Products Regulation” (ESPR). This will have large-scale effects on the requirements put on by companies selling or producing products intended for the European market. One requirement that is part of ESPR is that products should have a so-called Digital Product Passport (DPP). In this viewpoint, we at Opticos describe ESPR, DPP, and the potential challenges it might be to a manufacturing company CIO.
In short, these CIO will enter a waiting game, where they can act as forerunners, risking going wrong or followers risking being late. So, the CIO will find themselves in a DPP Chicken Race.
Background on ESPR
The proposed ESPR legislation is a cornerstone in the European Commission’s strategy to establish environmentally sustainable and circular products as the new norm. This will be achieved by requiring companies to provide sustainability and circularity data on their products using a Digital Product Passport (DPP), leading to voluntary data sharing and expansion to other sustainable aspects under Union legislation.
Uncertainties remain about what product groups will initially be prioritized. Some groups are expected to be impacted as early as 2027, identified by the EU identifying as suitable first movers. Most industries and product categories, however, are expected to be subject to regulation by 2030. The legislation will be equally applied to products intended for consumers (B2C) as well as for professional (B2B) markets.
The first delegated act specifying the product groups is expected to come into force soon. These groups will likely include those highlighted in the Circular Economy Action Plan (CEAP), such as electronics and ICT, batteries and vehicles, textiles, plastics, construction, and buildings. Although packaging is mentioned in CEAP, it is not expected to be covered by a separate delegated act.[1]
Furthermore, even though the ESPR is European legislation, it is expected to have a global impact, given the inherently global nature of supply chains, it is also likely that other regions will follow. This is partly due to political ambitions on fighting climate change, but also due to making manufacturing companies competitive. Since European legislation is implemented in a way that if you want to still sell or supply the European market you will need to follow the ESPR. By introducing similar legislation, pressure will be put on manufacturers to level up their game and still be able to export to regions with legislation in place. Of the followers, the expected upcoming US legislation will most likely be the one with biggest impact.
As noted, the ESPR regulation is currently in development, implying several uncertainties such as:
- Scope: How should requirements vary based on company size, and what is the appropriate level for applying Digital Product Passports?
- Technology: Questions arise about the storage and management of data, the choice of carriers, and the methods for accessing data
- Data: What specific information will be required, and to what extent? Additionally, considerations include identifying who will be responsible for collecting, updating, and verifying the data
These aspects are still evolving and will be clarified as the ESPR regulation progresses. This makes it difficult to accurately balance your efforts now. As a CIO it’s essential to be prepared to act and understand potential challenges ahead.
Digital Product Passport Overview:
A Digital Product Passport (DPP) serves as a tool to gather and share information about a product throughout its entire lifecycle. It helps identify what product it is and showcases basic information as well as detailed data on sustainability, environmental impact, and recyclability of a product. The DPP records data from the entire supply chain, detailing where the raw materials come from and how the product was made. This information is then shared among different stakeholders, unlocking product insights and highlighting advantages and practical applications.
The data can be shared using different methods such as QR codes, barcodes, or NFC technology. The most likely option initially is using QR codes, given their widespread adoption across diverse industries. This preference is attributed to their common usage, ease of use, strength, and flexibility in connecting with smart devices. QR codes have potential security risks due to its simplistic nature with weblinks that can easily be exchanged by just adding an exchanged QR code on top of the original one, where users end up in fraudulent sites. It is therefore likely that more robust technologies will be developed over time.
All information requirements for a digital product passport should be relevant and serve a purpose. Each piece of information should have a clear scope and provide tangible user benefits throughout the product life cycle. At this stage, there are uncertainties regarding the information required for different industries and product groups. However, the European Commission will further elaborate on this in delegated acts [2], aiming to enhance the following product aspects:
- Product durability and reliability
- Product reusability
- Product upgradability, reparability, maintenance and refurbishment
- The presence of substances of concern in products
- Product energy and resource efficiency
- Recycled content in products
- Product remanufacturing and recycling
- The product’s carbon and environmental footprint
- The product’s expected generation of waste material
On top of this, producers might use the digital product passport for linking to relevant documentation on the products such as user manuals, support pages, software download pages, etc.
Challenges for the CIO
In most companies the implications this new legislation will create will not be owned by the CIO or the IT organization. The ownership will likely land in R&D, Product management, Operations or the Legal or Sustainability departments. However, like what happened in the case of the GDPR legislation that was owned by HR and where many of the required actions landed on the IT organization to resolve, it is logical to assume this will be the case also with the ESPR/DPP legislation.
Business requirements on R&D, Operations and Legal will be broken down into IT requirements and will be put towards the IT department as change or transformation requests.
Like other major legislative agendas have done in the past (e.g., GDPR), the ESPR will significantly impact the IT landscape of large companies. It will affect applications that handle product data. In organizations focused on developing, manufacturing, and selling products, experience at Opticos indicates that this typically encompasses 30-40% of their total IT landscape [3]. In large manufacturing companies, this can equate to hundreds of applications. While not all applications will be affected, many may need to be adjusted to contribute data to the Digital Product Passport (DPP).
What kind of changes might be needed in those systems?
- Product Data Architecture: To keep and publish product data to end consumers as regulated in ESPR and DPP the ability to keep consolidated product data in one place and being able to publish this is essential. When multiple applications handle data to be used in a publication platform like this, establishing a consistent metadata structure and standardizing attributes, both in terms of descriptions and data content, become essential. This can pose a significant challenge for large companies, particularly those with competing silos of product information, often a result from historical factors or mergers
- Product Data Identifiers: To ensure accurate data retrieval, each product requires unique identifiers, such as model numbers, article numbers, and serial numbers. The combination of these identifiers should be unique within the company and, ideally, globally. Standards exist in this area, such as GTIN from GS1; however, it is worth noting that some of these standards are published by organizations with commercial interests
- Product Data Quality & Supply: Even after addressing the challenges related to product data architecture and unique product identifiers, the most significant hurdle remains; populating product data with high-quality information for critical attributes. While this may seem straightforward, many of these attributes likely consist of data not directly controlled internally, requiring input from suppliers. Ensuring both the delivery and quality of this data will necessitate updated contracts with suppliers, as obtaining this information can involve substantial efforts on their part. Most manufacturers and their suppliers may even need to trace several steps down in value chain to gather data on metrics such as CO₂ emissions or water usage
Even with strong efforts to address the challenges outlined above, implementation within existing IT systems can still encounter obstacles, particularly with older legacy applications that may be unable to handle necessary changes without disrupting other operational functions. Additionally, there is the question of how much to invest in outdated platforms, especially those that are already past their intended lifecycle.
Many companies will investigate mitigating solutions, building translating and aggregating data lakes / data fabrics instead of fixing the issues in the source systems. This might work for publishing the DPP data to end consumers, however it will introduce other issues. These data lakes / data fabrics might become business critical solutions for other departments that have direct access to the customers like product documentation and aftersales. These departments need to see the same information that the end-consumers see and suddenly, these solutions have become part of the day-to-day operational IT solutions creating transactional data like tickets and spare part sales. Getting these normalized and aggregated solutions to provide data in a non-normalized and de-aggregated structure to these operational solutions might be quite cumbersome. However, for many companies these aggregated solutions built on top of the old backbone will be needed to at all be able to meet the requirements put by DPP in time. When establishing these solutions, it is important to understand the long-term risk in those solutions.
Our recommendations for the CIO and initial actions
So how should you as the CIO go about taking on these challenges? Opticos has a 7-step approach that can give you a better understanding of the challenges ahead for your organization. Step 1 is understanding the stakeholder situation and step 2-7 is to understand the magnitude of the data and IT challenges. Even if the ownership of the data challenges most likely resides in the business it’s not likely that the business themselves will have the capability to conduct the analysis required in step 2-7. That’s why it’s likely that the IT department will be involved and as the CIO it is wise being prepared for this.
The 7-step approach:
- Investigate how and when ESPR will affect your company. As a CIO this might initially mean understanding who is responsible for this question and how your company’s products are affected.
- Get an understanding of what data you need to publish and what potential gaps you have in supplying them
- Understand if data is needed on the product model/article level or on the individual product level. The latter increases complexity and demands of traceability quite dramatically and if it’s possible to avoid this then big efforts can be avoided
- Understand and set the data architecture needed to achieve the requirements and understand if this architecture can be achieved with existing data sources, modified data sources or new data layers aggregating and transforming the data into the architecture you need
- For the data you already have available investigate if the format and granularity are consistent over different data sources and in line with expectations on the ESPR publishing formats
- For the data you don’t have available, investigate if this data can be supplied internally by your suppliers and if the latter start discussions and negotiations with them to get that data provided in the future
- Secure that the data collected and published is of equivalent quality, since it’s going to be visible to end-consumers and authorities and bad data will lead to a bad market reputation in the same way as bad product quality
The time these challenges will require depends on your as-is situation, as well as the complexity of your PLM/ERP landscape. It is also dependent on how big and complex your product portfolio is and lastly how big your network of component suppliers is. For a large-scale manufacturing company, selling complex and assembled products, we at Opticos estimate that this is years of work rather than months. Therefore, good advice is to start sooner rather than later to meet the regulation timeline.
Business Enablement and Risks
The legislators in the EU are mostly motivating this type of legislation with decreasing environmental impact and fighting climate change. However, there is also a competitive advantage for companies to adopt this in a timely and appropriate way. By adopting the legislation companies can protect their products from being banned on the European market. Furthermore, if other less serious competitors’ products are banned then there will be less competition in the market. So, less competition can be expected for those that do this right. Long term when governments or legislating bodies in other parts of the world eventually put similar legislation in place, the companies with this infrastructure in place will have an advantage over those that chose not to make these efforts up front.
The primary risk for companies starting early and attempting to be early adopters is that they might invest in solutions that do not ultimately fulfil the actual requirements, once clarified and formalized. Solutions in this field remain relatively immature, and there are opportunistic players aiming to be first on the market, often without offering effective, comprehensive solutions. Conducting thorough evaluations and relying on neutral, unbiased advice is essential to avoid missteps.
Being an early adopter also means risk overinvesting if legislation in the end has lower requirements than was originally being communicated. However, simply waiting for an industry standard to emerge also carries significant risk, as delays could ultimately lead to products being barred from the European market due to non-compliance. From Opticos we believe that our 7-steps approach as described above is a balanced way of preparing without making unnecessary commitments and investments early on.
Conclusion
Aligning with ESPR requirements is crucial for companies aiming for sustainability and market success. CIOs as well as other business leaders should strive to be well prepared for upcoming challenges. ESPR/DPP will for most manufacturing companies require substantial system upgrades or changes and enforced data management. It will also require a completely new level of supplying data from suppliers. Acting proactively as a CIO in accordance with Opticos recommendations in this viewpoint will lead to reduced risk of IT ending up on the critical path for future compliance. A proactive CIO will also gain trust in the business and strengthen the position as a strategic partner.
For CIOs or business owners seeking guidance in streamlining this process, Opticos offers expertise to navigate ESPR compliance effectively. Please reach out to see how we can support your organization in this sustainable production transition.
[1] The proposal for the ESPR regulation encompasses all industries except for food, feed, medicinal products, veterinary medicinal products, living plants, animals and micro-organisms, products of human origin, and products of plants and animals directly related to their future reproduction.
[2] Delegated acts are a type of legal instrument that allows the Commission to make specific changes or clarifications to existing legislation without requiring a full legislative process (like passing a new law through the European Parliament and Council each time)
[3] Figures are based on Opticos experience from at least 3 major Swedish companies in the global manufacturing segment. All of those have more than 40 % of applications containing product data. It’s mainly within R&D, Operations/ERP, SCM, Purchase, Sales, e-commerce and Aftersales.
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Hans Bergström, Teodor Danielsson and Nils Andersson
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Summary
For organizations looking to gain the benefits and flexibility of agile development, while still maintaining predictability and cost control, shifting to a risk-sharing contract model is a key strategy when sourcing application maintenance and development services from external suppliers.
Introduction
In the evolving landscape of digitalization, agile development has become a preferred approach for delivering high-quality solutions. The flexible and iterative nature of agile makes it an attractive approach in an environment where requirements often evolve over time. However, this flexibility can also introduce uncertainty in delivery timelines, costs, and outcomes for an organization.
Challenges
When sourcing Application Maintenance and Development (AM/AD) services in an agile context, through external suppliers, a common challenge arises. Agile teams and development services are generally sourced on a time and material basis – often without a common supplier strategy. Time and material contracts allow for a great deal of flexibility as suppliers provide resources based on the number of hours worked, and customers adjust priorities as new insights emerge during development. However, this approach places the bulk of the risk on the customer, as they are paying for the supplier’s time, regardless of the quality or functionality delivered. Without a common supplier strategy there is also a risk of misalignment between the suppliers (in situations where they may have different objectives) as well as with the customer’s objective.
Risk-sharing through commitment to results
A more effective approach to outsourcing agile AM/AD services is through contracts focused on risk sharing, clear responsibilities as well as commitment to common ways of working.
Risk should be distributed between the customer and the suppliers, where the supplier should be committed to support the customer’s strategic objectives and incentivized to deliver tangible and predictable results.
These risk-sharing focused contracts can include outcome-based milestones or other mechanisms that align the supplier’s performance with delivery success.
Mechanisms to ensure commitment, shared risk and predictability
Below are some examples of mechanisms that could be introduced to ensure that both predictability and shared risk are built into the agile sourcing agreements:
- Preferred suppliers with a formal responsibility to enable and support the customer’s transformation or innovation agenda
- Agreed team size and cost: The customer and supplier agrees on a baseline for the configuration and size of the agile team(s) – and thus a fixed running cost for the team(s). Changes to team configuration or size, and thus running cost, should be agreed upon between both parties.
- Handshake agreements at the sprint level: At the beginning of each sprint, both the customer and supplier agree on the scope of functionality to be delivered. These sprint-level handshakes act as micro-call offs, ensuring the supplier commits to delivering within the agreed scope and timeframe
- Increment-level follow-ups: At the end of each increment (typically four to six sprints), both parties conduct a formal follow-up on sprint delivery. These follow-ups serve as checkpoints where committed delivery within the increment is compared with actual delivery. By tying payments to the performance over an increment, the customer can better control costs and ensure predictability.
- Increment delivery precision: An increment delivery precision is a powerful risk-sharing tool. It holds the supplier accountable for delivering to, at least, a pre-agreed percent of the functionality in the sprint handshakes and pre-agreed quality level, as a condition to full payment. If the delivery falls short, a part of the payment could be deducted. The size of the deducted payment could be dependent on both the quality and functionality delivered. e.g quality and delivery performance. On concurrent increment under-performance, an even larger amount could be deducted.
- Earn-back mechanism: the supplier could be given the possibility to recover part of the deducted payment if the supplier’s performance significantly exceeds the committed delivery during an agreed number of increment follow-ups after one follow-up resulting in payment deduction due to under-performance.
- Fixed price bundling of base-services: To achieve a high degree of predictability, base-services such as upgrades, incident management, planning and reporting, could be bundled together as a fixed price commitment for the supplier. This would also put a pressure on the supplier to automate and simplify services within the fixed price scope.
The ultimate goal of these mechanisms is to enhance cost efficiency in agile AM/AD sourcing by ensuring predictable delivery and creating a fair distribution of risk between the customer and suppliers. By focusing on agreed team and scope, performance-based payments, and rigorous follow-up mechanisms, customers can limit the financial risk of delays or scope creep while still benefiting from agile’s adaptability.
This shared risk approach motivates suppliers to deliver value while safeguarding the customer from overpaying for underperformance. Additionally, by linking full payment to outcome, organizations can ensure that their investments yield tangible results, promoting both cost efficiency and high-quality results.
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Maria Nelsson, Maria Wennerberg
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In today’s global business environment, companies increasingly partner across borders, forming diverse teams that bring together different nationalities and cultural perspectives. While these collaborations offer significant opportunities for innovation, they also present challenges, particularly when it comes to managing change. Ensuring the success of change initiatives in culturally diverse teams requires businesses to address the unique hurdles posed by cultural differences, communication styles, and geographically dispersed teams.
From our consulting experience, we’ve seen that managing these challenges demands structured approaches that address cultural and communication barriers. It’s not about prescribing a one-size-fits-all solution but finding the right tools to adapt to the specific cultural contexts of the teams involved.
Common Challenges in Global Teams
Global organizations often face recurring challenges when working with culturally diverse teams. Some of them are:
Managing Cultural Differences in Practice
Here are some practical tools and strategies that we’ve found effective in managing cultural diversity:
- Cultural Awareness Workshops
Cultural awareness is more than a formality—it’s an essential starting point for collaboration. We’ve found that offering targeted cultural awareness training helps team members understand diverse perspectives and workplace behaviours. For example, in hierarchical cultures, employees may defer to authority, while in more egalitarian cultures, decision-making tends to be participatory. By educating employees on these differences, workshops enable better communication across hierarchical and cultural divides. - Social Engagement and Team-Building Activities
Building trust and rapport is crucial for change initiatives to succeed, particularly in collectivist cultures where group harmony is prioritized. Organizing social activities or team-building exercises helps break down barriers and fosters a culture of openness. These activities also contribute to work-life balance in more indulgent cultures, improving team satisfaction and cohesion. - Establishing Ground Rules and A Common Vision
Establishing clear ground rules—such as prioritizing the company’s success, encouraging accountability, and fostering collaboration—ensures that all stakeholders are aligned and working towards the same goal. This helps streamline interactions and creates a structured framework for effective teamwork across cultural boundaries. - Stakeholder Mapping and Engagement Plans
Identifying key stakeholders and understanding their influence is vital in high power distance cultures, where leadership approval may hold significant weight. In cultures with high uncertainty avoidance, early and frequent stakeholder engagement reduces resistance to change. Effective engagement plans ensure that stakeholders are kept informed and that their feedback is integrated throughout the change process. - Unified Communication Platforms and Escalation Processes
Clear communication is a cornerstone of managing change. A unified communication platform allows teams to share updates, raise concerns, and provide feedback, no matter their location. In high uncertainty avoidance cultures, defined escalation processes offer security and clarity, while in more individualist cultures, such platforms empower team members to voice concerns directly. - Empowering Local Leadership and Change Agents
Local leadership plays a key role in bridging cultural gaps. Leaders’ familiar with local customs can adapt change initiatives to meet their team’s expectations. In hierarchical cultures, empowering local leadership ensures that change efforts respect the existing structure, while in collectivist cultures, local leaders can foster group consensus and collaboration, making change more sustainable. - Feedback Loops and Continuous Improvement Mechanisms
Regular feedback loops are essential to address concerns early and adjust change initiatives based on real-time input. We’ve found that continuous improvement mechanisms are especially effective in long-term-oriented cultures, where gradual growth is highly valued. In high uncertainty avoidance cultures, frequent feedback reduces fear and builds confidence in the change process.
Case Study: Managing Collaboration Across Multiple Stakeholders in a Global Transformation
In one of our recent engagements, we worked with a client facing the challenge of managing collaboration across multiple stakeholders—both internal and external—spanning several regions, including the UK, Sweden, the US, and India. The complexity of this setup was compounded by the fact that different cultures, priorities, and working styles needed to be aligned for the transformation to succeed.
To address these challenges, the company implemented several ground rules that ensured effective collaboration:
- Put the Company First: This principle guided all stakeholders to prioritize resolving business problems over individual disagreements. It helped to maintain focus on the company’s overall success.
- Be Accountable: Everyone was expected to demonstrate accountability for their roles and responsibilities. This fostered a sense of mutual responsibility, encouraging transparency and ownership.
- Work Collaboratively: The mantra was simple but powerful—everyone’s efforts collectively contributed to the company’s success. This reinforced the importance of partnership across internal teams and external suppliers.
- Share Knowledge: Knowledge sharing was emphasized as essential to the success of the transformation. All stakeholders, whether internal or external, were encouraged to ensure that no part of the ecosystem was left unprepared.
Alongside these ground rules, we facilitated cultural awareness training to help the teams understand the diversity in communication styles, decision-making processes, and approaches to authority across different regions. This holistic approach—combining structured ground rules and cultural sensitivity—ensured that all stakeholders, regardless of location, could work together cohesively and achieve the desired outcomes.
Conclusion: Communication, Relationships, and Psychological Safety as Catalysts for Success
Managing cultural differences in global teams requires more than just awareness—it demands flexibility, trust, and a commitment to continuous learning. From our experience, the most successful teams are those that understand the pivotal role of communication and relationships in bridging cultural divides. Open, flexible communication fosters trust and builds psychological safety, where team members feel comfortable expressing their ideas and concerns without fear of judgment. This environment of psychological safety is crucial in diverse teams, as it encourages open dialogue and supports collaborative problem-solving.
Equally important are the relationships within the team. Strong personal connections, built through both formal and informal interactions, help break down barriers and create a sense of unity. These relationships make it easier for team members to navigate cultural differences and collaborate effectively, even in high-pressure situations.
By embedding communication, psychological safety, and relationship-building into the core of your change initiatives, organizations can turn cultural diversity into a strength. When teams embrace cultural differences as an asset, not a challenge, they unlock the full potential of their global workforce. This inclusive, adaptive approach not only enhances collaboration but ensures the long-term success of your initiatives.
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