The power of artificial intelligence (AI) to enhance digital transformation initiatives has become increasingly evident to engineers as they seek to improve operational efficiencies, scale innovation and gain a competitive edge.

While digital transformation is hardly new, AI and large language models (LLMs) have emerged as a formidable accelerator by changing business processes, reshaping products and services and sometimes upending entire industries.

AI’s ability to learn and improve over time, coupled with digital transformation, means that organizations can realize faster processes, reduced costs and more efficient operations.

These AI benefits contribute to an environment of continuous improvement and innovation that is often key to success in a competitive environment.

Enhancing data-driven decision-making

Engineers know that data-driven organizations use digital insights to shape strategies, optimize processes and respond rapidly to market changes. However, harnessing the potential of integrated digital data at scale for decision-making requires far more than traditional data analytics.

AI’s capability adds unprecedented speed and precision to decision-making by:

• Sifting through structured data, identifying patterns and generating predictive insights.
• Analyzing vast volumes of unstructured data more effectively than search engines, specialized databases or software developers to generate predictive insights.
• Avoiding the cost and elapsed time associated with custom data integration of diverse data sources using software developers.
• Autonomously detecting trends and forecasting outcomes.

Adding AI and LLM capability to data-driven decision-making helps engineers optimize operational and strategic decisions while reducing the need to base decisions on history, experience, in-vogue ideas or hunches.

Examples of adding AI capability to data-driven decision-making for engineering include:

• Monitoring large volumes of IIoT data from production equipment to identify performance anomalies to avoid unscheduled downtime.
• Sifting through the external media for general and industry audiences to identify competitor initiatives that may require a response.
• Summarizing patent data, trademark data and research journals maintained in multiple languages to identify potentially relevant technology developments.

Automating processes and workflows

Automation is a fundamental aspect of digital transformation. AI-powered tools like robotic process automation (RPA), machine learning and cognitive computing, a type of AI that simulates human thought processes, have taken digital transformation to new heights.

While valuable, previous generations of automation that engineers implemented were limited to well-defined, repetitive tasks and detailed, rule-based decisions. AI expands automation to more complex decision-making processes, pattern recognition and more generalized problem-solving.

Examples of adding AI capability to automating processes and workflows include:

• Adding more accuracy and sophistication to simulations. For example, engineers can refine and enhance their designs through successive simulations to reduce limitations, which leads to more innovative solutions.
• Enhancing supply chain management for better product demand forecasting, logistics optimization, order fulfillment and risk assessments for component shortages. Achieving these improvements requires the integration of disparate data sources maintained by partners.
• Adding more intelligence to RPA transaction workflows such as invoice and shipment receipt processing. Examples include identifying potentially duplicate invoices, assessing the materiality of discrepancies and identifying likely fraud.

Improving data quality

Engineers are painfully aware that insufficient data quality is the number one reason for the failure of digital transformation initiatives. Asking data analysts to identify and correct data quality issues is slow, tedious, expensive and subject to further errors.

AI can automate data quality improvement work using pattern recognition. AI can achieve more speed and consistency at a lower cost than human analysts.

Examples of using AI capability to automate data quality improvement include:

• Recognizing that existing equipment can’t manufacture the designs due to dimensions, lack of accessibility and unachievable tolerances.
• Identifying and correcting instances where numeric values are associated with different units of measure or measurement systems creates errors and confusion.
• Sharply reducing the number of duplicate and incomplete inventory master records.
• Generating synthetic data to augment existing datasets to improve AI models.

Persisting knowledge

Organizations lose surprising amounts of essential knowledge and intellectual property (IP). Too often, engineers reinvestigate problems or wrestle again with design refinements because of a lack of awareness of prior work. Loss of knowledge and expertise typically occurs due to:

• Staff turnover and transfers.
• Reluctance to share knowledge.
• Lack of management support for knowledge management.
• Lack of time to document work.
• No repository in which to store work products.
• No easy ability to search and retrieve documents.
• Organization restructuring, acquisitions and mergers.
• Confusion caused by inaccurate, outdated or redundant versions of information.

Addressing these issues without digital transformation is impossible. Including digital knowledge management to the scope of digital transformation initiatives can significantly increase the value organizations achieve from the knowledge they have accumulated, often at considerable effort and cost.

Adding AI agents to knowledge repositories can add another increment of value. AI agents are intelligent software that use an LLM to perform query tasks, make decisions and learn from their experiences like humans. AI agents are a significant advance on the more familiar chatbots.

Examples of using AI agents to enhance digital knowledge management include enabling engineers to:

• Query “tribal knowledge” to improve production performance.
• Discover best practices.
• Better troubleshoot production equipment problems based on records of historical incidents.
• Query IP such as patent records, test results, research reports, development studies and licensing agreements in support of current work.

Challenges and considerations

While AI and LLMs add potential to digital transformation, engineers must acknowledge the challenges and ethical considerations associated with its deployment. These include:

• Ensuring data privacy to maintain customer and employee confidence.
• Addressing biases in AI algorithms and training data to maintain trust and inclusivity.
• Recognizing that LLMs may be incomplete or misleading.
• Training a skilled workforce that can effectively manage AI-driven processes.
• Fostering a culture that embraces innovation to ensure the smooth integration of AI and LLM technologies.

Engineers can establish robust data governance, prioritize transparency in communication and continuously monitor AI systems to mitigate unintended consequences.

Artificial intelligence is a powerful accelerator of digital transformation. Its impact spans most industries and functions, enhancing efficiency, agility and resilience. By embracing AI’s transformative potential, businesses can achieve a sustainable competitive advantage and drive long-term growth.

The post Where AI can accelerate digital transformation appeared first on Engineering.com.

Digital transformation reshapes many industries by changing how organizations operate and deliver customer value. Many engineers have recognized the compelling benefits of digital transformation, including increased productivity. Through advanced software, automation, data analytics, and enhanced connectivity, digital transformation enables businesses to operate more efficiently, innovate faster, and deliver better outcomes.

Below, we explore more ways in which digital transformation boosts productivity. To read the first article in this series, click here.

Reduce costs and optimize resources

Digital transformation often leads to significant cost savings by reducing waste, improving asset utilization, and optimizing resource allocation. Through digital technologies like the Industrial Internet of Things (IIoT), simulation and digital twins, engineers can monitor equipment and processes in real-time, ensuring that resources like energy and materials are used more efficiently.

Predictive maintenance, enabled by IIoT sensors and AI, helps businesses reduce downtime and prevent costly repairs by identifying issues before they become critical. Avoiding unscheduled outages contributes significantly to production productivity. Cloud service providers (CSP) can reduce IT infrastructure costs, which have become a material expense, by allowing businesses to:

• Pay for only the CSP resources they use.
• Avoid investing in an on-premise computing environment.
• Avoid operating costs for an on-premise computing environment.
• Utilize a CSP computing environment with superior cybersecurity defenses.
• Access enormous CSP computing resources instantly when needed.

Leading simulation software vendors include Avena, Autodesk, Dassault Systèmes, GE, and Siemens. Leading CSPs include Amazon, Google, IBM, and Microsoft.

Boost collaboration and communication

Engineers and others struggle to collaborate with staff and external partners due to data-sharing limitations and incompatible technologies. Sometimes, well-intentioned security measures become impediments.

Digital transformation introduces tools that enhance communication and collaboration across teams, departments, and geographical locations.

This interconnectedness reduces bottlenecks, shortens project timelines, and fosters a more agile workplace where engineering teams can collaborate more productively. With the rise of remote work, these tools have become even more critical, allowing businesses to maintain productivity even when their employees are not physically present in their respective offices.

Cloud-based collaboration software includes MindMeister, Miro, Microsoft Teams, Slack and Zoom. Project management software includes Asana, Microsoft Project, and Trello. This software makes it easier for employees to work together in real time, regardless of location.

Bolster agility and flexibility

Many organizations are comfortable with their current processes. That comfort often precludes an agile response when changes in the business environment threaten the business plan.

Digital transformation equips engineers with the agility to respond rapidly to changing market conditions, technological disruptions, and customer needs because the needed data is immediately accessible. In the past, engineers would often have to wait weeks or months to implement responses or launch new products, but digital tools enable them to do so in days or even hours.

For example, cloud computing allows businesses to scale up or down based on demand, enabling them to be more responsive to fluctuations in market needs. Similarly, AI and data analytics enable businesses to pivot quickly based on real-time data insights. This flexibility enhances productivity by ensuring businesses allocate resources optimally and capitalize on opportunities as they arise.

Leading software vendors for data analytics include Google Data Studio, Microsoft Power BI, Minitab, Tableau, TIBCO and TrendMiner. Leading AI software vendors include Anthropic, Google, IBM, Meta Platforms, Microsoft and Open AI.

Empower the workforce and build skills

The workforce, including engineers, frequently feels hemmed in by narrowly defined roles, inadequate digital tools and stifled by a ponderous top-down decision-making culture.

Digital transformation drives productivity by empowering employees with better tools and access to information. With the right digital tools, employees can complete tasks more efficiently, collaborate more easily, experiment and make better decisions.

Digital transformation often opens opportunities for employees to upskill or reskill, enabling them to perform new roles or handle more complex tasks while improving productivity.

Many businesses are investing in training and development programs that teach employees how to use advanced technologies like generative AI, ML, and data analytics tools. This training enhances individual productivity and positions the organization to adapt quickly to technological changes.

Leading immersive training software vendors include AllenComm, EI, ELB Learning, Learning Pool and SweetRush.

Digital transformation is a fundamental shift in how businesses operate and deliver value. It has proven to be a powerful driver of productivity, enabling businesses to streamline processes, automate mundane tasks, make data-driven decisions, enhance customer experience, and boost collaboration and communication. It’s not just a technological upgrade.

Organizations that harness digital transformation’s power will enjoy sustained productivity gains and long-term success. They are better equipped to navigate the complexities of the marketplace, innovate faster, and remain competitive.

The post More digital transformation ideas for raising productivity appeared first on Engineering.com.

Digital transformation has become a vital force in reshaping industries. Adopting and integrating digital technologies into most business areas fundamentally changes how organizations operate and deliver customer value. As engineers worldwide navigate this evolution, one of the compelling benefits of digital transformation is its ability to raise productivity. Through advanced software, automation, data analytics, and enhanced connectivity, digital transformation enables businesses to operate more efficiently, innovate faster, and deliver better outcomes.

Below, we explore the various ways in which digital transformation boosts productivity.

Streamline business processes

Today, engineers spend too much time in low-productivity work such as:

• Hunting for data.
• Seeking access to data.
• Cleaning data to a reasonable level of accuracy and completeness.
• Integrating data using Excel.
• Waiting for others to complete manual work.

Digital transformation delivers significant productivity gains when it includes re-engineering business processes by:

• Simplifying the steps involved.
• Designing steps to minimize the opportunities for errors.
• Improving the availability of relevant digital data to those performing the process.
• Integrating more efficient digital tools into the process.

Digital transformation often triggers the replacement of legacy systems with current software, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and industry-specific software-as-a-service (SaaS) solutions. Current software packages and SaaS solutions offer:

• More comprehensive functionality.
• Access to best practice processes.
• Better alignment with various business functions.
• Outsourced software maintenance.

For example, ERP systems consolidate many business processes—such as financial accounting, procurement, production management and supply chain management—into one seamless system, reducing the time spent on redundant tasks and increasing the speed at which businesses can operate and make decisions. Streamlined processes lead to improved staff productivity, fewer delays and higher accuracy.

Leading software to design and automate custom processes include Appian, Microsoft Power Automate, Outsystems, Pegasystems Pega, and Oracle BPM Suite. Leading ERP software vendors include Infor, Microsoft Dynamics 365, Oracle Netsuite, SAP S/4 HANA, and Workday.

Automate repetitive tasks

In many businesses, repetitive tasks consume significant time for engineers. The work is not rewarding and error-prone.

Digital transformation enables the automation of repetitive tasks to drive productivity and data quality. Businesses can automate routine and repetitive tasks, such as data entry, report generation, and many customer service interactions, through the use of artificial intelligence (AI), robotic process automation (RPA), and machine learning (ML).

For example, RPA can streamline back-office operations like finance and HR by processing transactions, aggregating data, and performing multi-step workflows without human intervention. This automation reduces the likelihood of errors and speeds up processes. It also frees up time for employees to focus on more strategic tasks that require critical thinking and creativity that directly contribute to business growth.

Leading software vendors for automating repetitive tasks include Automation Anywhere, Datamatics, SS&C Blue Prism and UiPath.

Support data-driven decision-making

Today, engineers and others make decisions in many businesses based on experience, partial data and hunches. That’s often riskier than it seems.

Digital transformation facilitates collecting and analyzing vast amounts of data, which organizations can leverage to make more informed decisions. Data analytics tools help engineers rapidly analyze performance patterns, customer behaviours, and market trends, assisting businesses to adjust strategies quickly and stay ahead of competitors.

Using data to drive decision-making processes enhances productivity by enabling more precise forecasting, leading to better production management, inventory management, and resource allocation. Data-driven decision-making processes are particularly valuable in industries like retail, manufacturing, and healthcare, where many minor improvements in efficiency can lead to significant cost savings and faster service delivery.

Leading software vendors for data-driven decision-making include Altair, Alteryx, Databricks, IBM Watson Studio, Oracle Analytics, SAP Analytics Cloud and Snowflake. Leading software vendors for data visualization include Google Data Studio, Microsoft Power BI, Minitab, Tableau, TIBCO and TrendMiner.

Enhance the customer experience

Organizations continue to experience difficulties delivering the customer service they aspire to.

A key goal of digital transformation is improving the customer experience in all channels, digital or otherwise. Tools such as AI-powered chatbots, self-service portals, and mobile applications allow businesses to serve customers more efficiently and responsively. Digital transformation enables engineers to interact with customers in real time, providing faster responses to inquiries, better product recommendations, and more personalized experiences.

Improved customer experiences can increase customer satisfaction, loyalty, and repeat business. When it’s possible to assign fewer employees to resolve common customer issues, productivity increases and costs decrease. Dedicating more employees to customer relationship building and innovation drives sales and profitability.

Leading software vendors for customer experience management include Birdeye, HubSpot, Microsoft Dynamics 365 Customer Insights, Podium, and Zendesk. Leading software vendors for call center operation include Five9, Nextiva, Nice CX-One, RingCentral and Talkdesk.

Digital transformation is not just a technological upgrade but a fundamental shift in how businesses operate and deliver value. It has proven to be a powerful driver of productivity, enabling businesses to streamline processes, automate mundane tasks, make data-driven decisions, enhance customer experience, and boost collaboration and communication.

Organizations that embrace digital transformation are better equipped to navigate the complexities of the modern marketplace, innovate faster, and remain competitive. As technology continues to evolve, the businesses that effectively harness the power of digital transformation will enjoy sustained productivity gains and long-term success.

The post How digital transformation raises productivity appeared first on Engineering.com.

That’s easier said than done. The real challenge lies in execution. Starting with a digital transformation roadmap helps turn ambitious goals into tangible digital outcomes that produce business benefits.

A digital transformation roadmap elaborates the high-level strategy into a guide for navigating the complex journey of integrating more digital technology into most business areas. The roadmap bridges the gap between your long-term digital vision and the multiple projects needed to turn the vision into digital reality.

A successful digital transformation roadmap isn’t a plan; it’s a strategic guide for navigating the complex journey of integrating digital technology into every area of your business.

There’s no simple formula for digital transformation or a one-size-fits-all solution. The digital transformation will likely look different even for every company that competes in the same industry. The differences result from various factors, including size, history, in-place technology, degree of centralization, leadership styles and culture.

Digital transformation drivers

The drivers that are causing many organizations to invest in digital transformation include:

• Fewer customers shopping in person and more shopping online.
• More employees working remotely part of most weeks.
• Pressure from competitor actions or to gain competitive advantage.
• Increasing customer expectations for product quality and customer service.
• Increasing product and service complexity requiring more multi-disciplinary collaboration.
• The availability of cheaper and more capable information technology to support digital transformation.
• Improved digital data quality to support digital transformation.

Value of a roadmap

Because digital transformation has a wide-ranging impact on the organization, it’s easy for senior executives, engineers, various stakeholders and project teams to lose track of the vision and strategy. The value of a clear, actionable roadmap includes:

• Ensuring that digital transformation projects are well aligned with business goals.
• Communicating the vision and strategy to maintain organizational commitment to digital transformation.
• Translating sometimes vague digital transformation concepts into multiple clear, actionable objectives that individuals can understand and support.
• Providing an initial insight into the resources the digital transformation projects will require.
• Guiding the projects that will implement various parts of the digital transformation.
• Keeping project teams aligned and focused while understanding their work’s broader digital transformation context.
• Avoiding common pitfalls that impede digital transformation.

Scope of a roadmap

Surprisingly, the scope of digital transformation roadmaps is similar across industries. They typically include many of the following:

• Enhancing the customer experience through technology.
• Leveraging internal and external data with analytics for insightful decision-making.
• Streamlining product design, manufacturing and distribution with process automation.
• Shortening and reducing the complexity of the supply chain through improved digital collaboration with suppliers and dealers.

• Applying available digital technologies to advance the business plan.

Steps in building a roadmap

Engineers typically collaborate with other professionals to build a digital transformation roadmap using the following steps:

1. Define a digital vision for the organization and seek senior executive approval.
2. Develop a digital strategy that captures business process and technology opportunities while recognizing people and facility constraints.
3. Seek senior executive approval for the digital strategy.
4. Confirm the collaboration and people change management strategies.
5. Build a list of digital transformation projects that implement parts of the digital strategy. Ensure every project advances the strategy.
6. Build a high-level plan for every digital transformation project. Ensure no project is planned to run for more than 10 – 12 elapsed months.
7. Describe the required project team skills and experiences. Estimate the approximate effort needed for each role for every digital transformation project.
8. Sequence the digital transformation projects. Prioritize low-complexity, high-value projects earlier. Recognize precedence relationships across projects.
9. Select the appropriate supporting digital technologies and tools. Employ in-place technology where possible.
10. Define the risk management process and the initial risk list with recommended mitigations.
11. Develop a management strategy for project governance.
12. Conduct a short pilot of digitizing a business process using the selected digital technologies and tools to confirm the planned approach.
13. Revise the roadmap based on findings from the pilot.
14. Seek roadmap approval from stakeholders and senior executives.

The digital transformation roadmap implements the strategy that describes the following topics:

• Organization-specific digital transformation drivers.
• The organization’s current state of digitization and automation and how much the digital transformation is expected to advance digital work.
• The approximate amount of business process change, people change, and digital skills upgrading that will be required.

The following categories of digital technologies, tools and data should be considered for the digital transformation projects:

• Advanced data analytics tools.
• Generative AI software.
• Hybrid work management software.
• External data.
• Simulation software.
• SCADA/IIoT systems for operations.
• Software-as-a-Service (SaaS) solutions.
• Custom software development tools.
• Cloud service providers.

Steps in executing a roadmap

Organizations execute the approved digital transformation roadmap using the following steps:

1. Execute the next project in the approved sequence.
2. Review project outcome against project goal and strategy.
3. Refine the roadmap based on project learning and changes in the business environment.
4. Revise project scope definitions based on the previous step.
5. Return to step 1.

Every digital transformation project will include examples of the following deliverables:

• A list of business processes to be improved with rationalization and digital support.
• Additions to the computing infrastructure.
• Additions to the list of datastores.
• Custom software for datastore integration.
• Implementation of apps and SaaS solutions.
• People change management.
• Data quality improvements.

If sufficient resources are available and projects are truly independent, it may be possible to execute two projects concurrently. The benefit will be the earlier start of the benefits of the digital transformation roadmap.

How to recognize a superficial or ineffective roadmap

Engineers can recognize a superficial or ineffective digital transformation roadmap if it contains one or more of the following features:

• Restates the vision and strategy without offering elaboration.
• Discusses technology at length, perhaps to the exclusion of business issues.
• Relies on leading-edge technology.
• Fails to reference the associated business process changes.
• Discusses intangible benefits in detail.
• Omits estimates of tangible benefits.
• Describes the required projects in great detail or not at all.
• Omits a risk and mitigation discussion.
• Contains only cursory references to people change management.
• Proposes a pace of project work that will overwhelm the organization.

A comprehensive roadmap will guide a successful digital transformation and reduce the risk of failure among the projects implementing the strategy.

The post How to build a digital transformation roadmap appeared first on Engineering.com.

• Listing the associated risks in detail in the project charter to educate stakeholders.
• Including specific tasks in the project plan to avoid or minimize the impact of disasters.
• Conducting parallel projects to address these situations before they turn into disasters.

Here is a list of the most common issues that cause digital transformation disasters. Anticipating these issues will position your project for success, allowing the organization to squeeze more value from its data.

You can read the previous five disasters to avoid at this link.

1. Ignoring people change management

Some digital transformation projects pay little attention to people change management. Some engineers forget that their intended audience does not have intimate knowledge of the revised business processes and the technology they accumulated during the project. Sometimes, teams wrongly assume that everyone understands information technology sufficiently so adoption will be easy.

At a minimum, paying insufficient attention to people change management leads to slow adoption. Sometimes, it can lead to costly misunderstandings. In extreme cases, it leads to rejection of the new digital functionality and skepticism about the value of digital transformation.

The best way to build buy-in for the digital transformation is with people change management project tasks that include:

• Engaging end-users in project tasks such as design reviews, software accepting testing and data quality improvement.
• Offering formal training in the new business processes.
• Providing in-person support to staff as they switch to the digital way of conducting business.
• Ensuring that adopting the new business processes with their digital tools is a component of the annual review process.

2. Prioritizing technology

Some digital transformation projects prioritize work on information technology instead of business value. Various situations, such as the following, can trigger this problem:

• Senior management is impressed by information technology implemented by a competitor and mistakenly believes that a specific technology enabled the digital transformation advance.
• The project team is dominated by information technologists who want to build their resumes by building experience with new technologies. Technologists mistakenly believe that stakeholders will be impressed by sophisticated application architectures, creative use of technologies and artful user interfaces.
• An effective vendor sales team sells the organization on their information technology as the basis for a digital transformation.

The impact of a technology-dominated digital transformation project is to deliver a sophisticated, robust system with functionality that provides only limited business value. In extreme cases, the project ends as a no-value disaster.

Digital transformation projects that prioritize business value over technology are more successful. They prioritize the development of process improvements, data integrations and software based on development complexity, the number of end-users who will benefit and an estimate of annual business value. This approach will prioritize high-value items and repeatedly defer high-complexity development to future releases. Some proposed functionality may never be developed using this prioritization.

3. Ignoring business process changes

Some digital transformation projects ignore addressing required business process changes, erroneously believing:

• Advanced data transformation can overcome process issues.
• There’s no value or too much resistance to revising long-standing business processes.
• Such changes are outside of the scope of an information technology project.

Not considering business process changes significantly reduces the business benefits that digital transformation can deliver. For example, replacing manual capture of product test results with an identical Excel workbook ignores an opportunity to improve productivity and data quality by introducing standard codes and values.

Project teams deliver more value when they view business process changes as an opportunity to use digital data to reduce cycle times, improve quality and reduce costs.

4. Ignoring stakeholders

Some digital transformation project teams ignore collaboration with stakeholders, arrogantly thinking they understand more about digital technology and business requirements than most stakeholders.

The impact of ignoring stakeholders includes misunderstanding of business requirements leading to:

• An inadequate or useless digital transformation.
• Poor adoption of new digital functionality.
• Stakeholders ignoring the project, which is cancelled as a disaster.
• Missing the high-value opportunities or investing in low-value opportunities.

Successful project teams recognize that they don’t know everything. For example, when project teams design digitally enabled business processes such as fabrication or logistics in collaboration with engineering experts, the result is superior, and the implementation is more straightforward.

5. Proof of concept paralysis

Some digital transformation project teams conduct too many proofs of concept (PoCs) and never advance any digital applications to production status.

While PoCs build understanding and reduce risks, they do not deliver business value. Multiple PoCs also consume resources, create a scene of paralysis and become demotivating for the staff.

Instead, conduct one for two PoCs directly related to the application you expect to advance to production status. For example, use PoCs to confirm technology choices and deepen understanding of proposed changes to business processes. Use the learning from the PoCs to build the project plan to develop and implement the production application.

Addressing the most common issues that cause digital transformation disasters will position your project for success. For additional ideas that will enhance your digital transformation, please read Here’s why your digital transformation project is struggling.

The post 5 more disasters to avoid on the digital transformation road appeared first on Engineering.com.

Digital transformation projects create the opportunity to deliver value to engineers and their organizations every day. However, like other projects, digital transformation projects routinely face the risk of disasters. With awareness of the source of digital transformation disasters, projects can mitigate their impact by:

• Listing the associated risks in detail in the project charter to educate stakeholders.
• Including specific tasks in the project plan to avoid or minimize the impact of disasters.
• Conducting parallel projects to address these situations before they turn into disasters.

Here is a list of the most common issues that cause digital transformation disasters. Anticipating these issues will position your project for success, allowing the organization to squeeze more value from its data.

Too many data problems

Data problems often overwhelm digital transformation projects. Some typical data  problems include:

• Missing values such as incomplete component specifications or missing product descriptions.
• Incompatible key values for data columns, such as customer and vendor codes across systems.
• Incorrect data such as material and supplier codes, dates and discount percentages.
• Missing transaction history where data such as engineering change history or warranty claims is essential to analyzing trends.
• The absence of data quality standards.

Correcting data problems will add to the project cost and extend the schedule, undermining the project team’s efforts. The significant effort required to make corrections will surprise management and potentially reduce their commitment to digital transformation.

To manage data problems in ways that are helpful to advancing digital transformation, engineers can undertake the following actions:

1. Recognize the risk of data issues in the project charter to set stakeholder expectations.

2. During the feasibility phase of the project, profile all the potential data sources to determine the extent of data issues.

3. Share the data issues identified with the data stewards and encourage them to take action to make corrections.

4. Start the project by focusing on data sources that exhibit fewer data issues.

Lack of data literacy

Employees’ lack of data literacy is impeding the realization of benefits from digital transformation because they are not using the available digital data.

This lack of data literacy means the planned benefits of digital transformation are not a reality in the organization. The absence of visible benefits will reduce management’s commitment to digital transformation.

To overcome employees’ lack of data literacy, project teams can take the following actions:

• Develop a library of data analytic routines that employees can run and modify to suit their needs.
• Offer in-house, instructor-led training for the available data and data analytic tools.
• Offer one-on-one coaching for employees.
• Point employees to specific YouTube videos that will improve their conversancy with the available data analytic tools.
• Develop a library of frequently requested reports that employees can run and export the data to Excel.

Viewing generative AI as a silver bullet

The explosion of generative AI during the past two years has caused some to view this incredibly capable technology as a silver bullet that can be easily applied to many problems, including digital transformation.

Delivering generative AI features as part of a digital transformation project is not trivial and can lead to undesirable consequences, including:

• Poorly constructed prompts that produce erroneous results and then misleading recommendations.
• Leakage of commercially sensitive intellectual property into the hands of others.
• Risk of inadvertently infringing on the copyrights of others.
• Investment in multiple generative AI software packages that increase cost more than value.
• A more reasonable approach to applying generative AI for engineering applications includes implementing these elements:
• Orient your organization on generative AI capabilities, risks, and limitations.
• Architect a data and analytics environment that will include a data lakehouse to manage both structured and unstructured data.
• Design an AI computing infrastructure, including cloud components that is efficient, scalable, well-governed and at least somewhat future-proof.
• Strike a balance between leveraging vendor capabilities that provide little competitive advantage and developing in-house models and related software that will be costly.
• Choose where to deploy open-source and proprietary technologies.
• Identify which of the many AI use cases are suitable for your company and can deliver tangible business value.
• Build trust in AI-driven solutions through detailed verification of results.

Chasing the latest technology

Some digital transformation project teams become excited by or even fixated on the latest vendor announcements about information technology advances. Examples include:

• Incorporating a sophisticated data visualization software package when a simpler and cheaper one is sufficient.
• Including generative AI capability when it’s of limited value to the digital transformation.
• Using a graph DBMS when a relational DBMS is sufficient.
• Building a data warehouse when integrating data from multiple data sources is straightforward.
• Introducing a new integrated software development environment that is unfamiliar to the organization.

Often, teams see the potential benefits of new information technology without considering how the immature technology will add cost, create delays and introduce quality problems.

Changing technologies or adding more and more technologies mid-project will distract and overwhelm digital transformation projects. Impacts will include reworking software, training staff, and building familiarity with the new technology.

Engineers can take a superior approach by carefully selecting a set of information technologies near the beginning and sticking with the choices for the project’s duration. Information technologies do not advance so quickly that older technologies become obsolete within a system’s planned existence. Engineers successfully use software packages and application development tools that aren’t the latest and greatest every day.

Fantasy business case

Some companies approve digital transformation projects based on an unrealistic business case. Engineers can recognize a fantasy business case because it will include one or more of the following elements:

• Estimated future revenue increases that exceed the historical trend.
• Estimated future operating costs will decrease more than the historical trend.
• The project cost estimate is unrealistically low, does not include a contingency amount and does not recognize the cost of likely change orders.
• There is no discounting of the value of future benefits.
• Quantification of intangible benefits such as brand value or customer satisfaction. Intangible benefits can be essential aspects of digital transformation projects. However, quantifying these benefits is not realistic.

Engineers can promote a credible business case based on tangible benefits and a more reasonable project cost. While digital transformation offers companies many benefits, those benefits often indirectly support other goals, such as reduced operating costs, compressed product development work or increased market share.

The post 5 disasters to avoid on the digital transformation road appeared first on Engineering.com.

Ideally, project managers collaborate with project sponsors and stakeholders to position projects for success, reduce risks and mitigate the impact of various issues that arise during project deployment. Project sponsors are assigned by senior management to ensure the planned business benefits are delivered. Project managers manage the work of the project team and report to their project sponsor.

In reality, however, project sponsors often let down their teams and add risk to projects in many ways. Here are eight common project sponsorship missteps and how project managers can politely and diplomatically resolve them (and as much as expressing anger is tempting, it’s never helpful).

Sits on recommendations

The project sponsor refuses to act on team recommendations to resolve issues. In some organizations, it’s better to waffle than risk being blamed for the wrong decision. But in digital transformation projects, delays in waiting for a decision are always more expensive than correcting a decision that turns out later to be incorrect.

Instead of becoming angry, project managers can address this problem through diplomatic coaching of the project sponsor. Diplomatic coaching involves patiently explaining the adverse consequences on the project’s outcomes the project sponsor’s actions or inactions will cause. Diplomacy is required because the project sponsor is typically a powerful person in the organization and does not respond well to blunt criticism.

Project managers do not let the absence of a decision delay the project schedule. They proceed on the assumption that the recommendation will be accepted eventually.

Refuses coaching

When project managers try to make diplomatic suggestions about how the sponsor could better fulfill their role and support the project, the sponsor claims to be too busy or suggests the project manager can handle the issue independently.

Project managers address this refusal professionally by diplomatically assigning project sponsors small, tactical tasks to gradually increase their involvement, and then thank them when the tasks are complete.

Fails to support the project manager

Suppose the project manager feels the project sponsor doesn’t support them. They sense they will be blamed for project shortcomings. In that case, an experienced project manager will begin to think about how to exit the project quietly. Such an outcome can reflect poorly on the project sponsor’s carefully cultivated reputation, the project’s progress, and the team’s effectiveness.

To avoid this situation, project managers seek assurance that project sponsors will support them and the team in the following ways:

• Communicating and selling the digital transformation project benefits among the project sponsor’s executive peers.
• Publicly supporting project recommendations to stakeholders when complex issues inevitably arise.
• Proactively support the project work.

Pushes scope additions

On multiple occasions, the project sponsor has proposed surprising scope additions for approval by the steering committee. There was no prior discussion with the project manager. These additions would add value but are clearly out of scope as defined in the project charter for the digital transformation project.

The project manager politely reminds the project sponsor of the agreed scope management process and has an analyst on the project team complete the proposed scope addition form for review by the project sponsor. Project sponsors usually never review the form, and the idea dies quietly.

Contradicts agreed decisions

The role of project sponsors includes emphatic support of the agreed decisions in conversations with other executives. If it becomes politically expedient to support the contrary view, some project sponsors are tempted to make a U-turn, claim they weren’t part of the decision, and blame the project team.

The project manager should politely remind the project sponsor of the agreed decision and ask the project sponsor if the original decision needs to be reversed. If so, the project manager assigns an analyst on the team to complete the proposed scope change order with an estimate for review by the project sponsor and as a decision record. The form privately embarrasses project sponsors, who quit articulating the contrary view.

Criticizes the project manager

We’ve all observed project sponsors who are smooth political operators. They are reluctant to accept responsibility for anything. They are experts at deflecting criticism and blame. When minor project problems appear, they quickly criticize the project manager, ignore the team and distance themselves.

In this situation, a project manager will become angry and conclude they have been hired as the convenient scapegoat should a problem occur and not, as claimed, as a project manager with a mandate to deliver the project.

Project sponsors who play these political games cause project team turnover and failure. It’s often best for the project manager to lobby the stakeholders to assign another project sponsor.

Commits to a ridiculous project completion date

Sometimes, project sponsors believe they can impress their peers on the executive team by committing to an overly aggressive completion date for the digital transformation project without consulting the project manager.

Naturally, the project manager is angry about not being consulted and the real possibility that the project will be viewed as a failure when it can’t achieve the unrealistic date.

A solution to this problem that avoids embarrassing the project sponsor is to replan the project to create a release that can be achieved by the aggressive date, declare that a success, and then work on the rest of the project after that date.

Criticizes the project

When discussing the digital transformation project with stakeholders, some project sponsors express hesitancy about the benefits and criticize the performance of the project team.

Instead of becoming angry, the project manager should diplomatically explore the project sponsor’s hesitancy about the business case. The project sponsor’s commitment is typically strengthened if the hesitancy can be resolved.

If the project sponsor and manager cannot resolve the hesitancy, they should cancel the project immediately. Continuing will only waste money and perhaps lead to conflicts between the team and the stakeholders.

Project managers can often improve the performance of project sponsors with diplomatic coaching about how to best fill the role, explaining the role of the project manager and describing the value of collaboration.

The post Is your project sponsor dropping the ball? appeared first on Engineering.com.

Many companies are pushed into business transformation by changes in their environment. However, companies often struggle with business transformation due to complexity and resistance to change. Business transformation depends heavily on digital transformation because of the pervasive role of information technology (IT) and operational technology (OT) in today’s businesses and manufacturing floors. Whenever companies advance digital transformation, business transformation becomes easier. 

Business transformation is a change management strategy that aligns people, processes, and technology to companies’ future vision, shifting market demands and new opportunities based on technological advances. The evolution of product or service offerings is critical to companies’ continued success. Engineers constantly assess, adjust, and advance offerings to stand out from competitors. 

Multiple disruptive events or opportunities can trigger the need for business transformation. This article describes how digital transformation supports various business transformation events. 

Technology triggers  

Most technologies are advancing due to the research and development of a record number of scientists and engineers funded by their companies and government grants. The research results trigger engineering and operational transformation that impact the business transformation. Examples include: 

• Carbon fibre and nanomaterials transformed the product design and manufacturing of industrial products and many types of vehicles. 
• Advanced glass varieties revolutionized telecommunications and transformed the designs of many consumer products, including smartphones. 
• Generative AI is transforming the work of many professions, including engineers and improving their productivity. 
• Improved software development productivity led to more functional apps that transformed control over manufacturing processes, ensured consistent quality and accelerated research and development. 
• FinTech startups are transforming the banking and insurance industries with digital services and simplified loan approval and payment processes. 
• AI and robotics are transforming manufacturing for reduced cost, consistent quality, and transportation with autonomous vehicles. 
• Rapid molecular screening transformed the selection and testing of pharmaceutical candidates. 

When engineers incorporate technology advances into the business, they typically trigger the digital transformation of: 

• Various processes to reduce elapsed time and improve quality. 
• More and better supporting data with better data management. 
• Collaboration with critical vendors through digital collaboration software. 

The Internet triggered business transformation 

The Web and smartphones enabled the e-commerce sales channel. Many companies digitally transformed their businesses beyond their traditional brick-and-mortar sales channel to sell products through websites and apps. Prominent e-commerce examples exist in every product category. They include: 

• Amazon and many retailers threatened by Amazon and its clones. 
• Industrial and construction products dealers. 
• Computer and electronics manufacturers and their distributors. 
• Automobile manufacturers and their dealers. 

This additional sales channel produces new revenue streams and reaches customer types that were unreachable or ignored before. E-commerce requires significant investments in information technology and digital transformation of: 

• Sales, fulfillment, and return processes with sophisticated web-based applications. 
• Marketing communication through social media marketing. 
• Customer support through a call centre and a smartphone app. 
• Distribution, warehousing, and delivery with automation and robots. 

Competitive threats trigger business transformation 

Often, competitive threats to market share and profitability trigger business transformation. Examples include: 

• Japanese auto manufacturers introducing more reliable cars at a lower price triggered bankruptcies and a massive business transformation among American and European auto manufacturers. 
• Korean household appliance manufacturers introducing more advanced and reliable products led to the demise of various erstwhile competitors. 
• The emergence of Chinese aircraft manufacturers triggered a business transformation at Boeing and Airbus. 
• Brick-and-mortar chains, such as department stores, responded to the emergence of e-commerce-only competitors. 

When engineers respond to competitive threats, they typically transform the business by: 

• Designing new and revised products and services. 
• Building new production and distribution facilities. 
• Designing new and revised business processes. 
• Demanding more and better internal and external data. 
• Expanding the supply chain ecosystem. 

The success of these actions depends on advancing digital transformation. 

Price and performance of computing triggers business transformation 

The continuing improvements in the price/performance of computing components of all types, software packages and Software as a Service (SaaS) offerings enable business transformation. For example: 

• Engineers digitally transform product design processes using more sophisticated CAD, simulations, and digital twins. 
• Apple and Android-orientated manufacturers transform handheld computing devices to be more capable and easier to use. That advance increased consumer and business adoption. Engineers use mobile devices to monitor and improve manufacturing plant performance. 
• Products become both cheaper and more capable. Examples that engineers encounter include industrial control devices, simulation software, IIoT sensors, and video surveillance equipment. 
• Telehealth transforms the productivity of healthcare providers and the experience of patients. Less waiting and less driving improve the patient experience. 

When engineers want to exploit improvements in computing components and software, they typically: 

• Design new and revised products and services. 
• Upgrade facilities. 
• Design new and revised business processes. 
• Demand more and better operational data. 

All these actions depend on a high level of digital transformation. 

Regulatory requirements trigger business transformation 

Sometimes, new regulatory requirements drive business transformation. For example, the determination to address the adverse impacts of climate change is causing businesses to: 

• Measure and report on their environmental impacts more accurately, consistently, and comprehensively. 
• Transform their production practices to reduce energy consumption and related GHG emissions. 
• Transform their waste management practices to increase recycling volumes and reduce disposal volumes. 

When engineers respond to new regulatory requirements, they typically: 

• Trigger digital transformation of various existing and new business processes such as digitizing and quality-assuring the required data gathering. 
• Demand more and better reporting data which digital transformation provides. 

Business transformation challenges 

Business transformation is challenging to implement because of its wide-ranging prerequisites for success. It entails adjustments to a company’s structures, incentives, mindset, processes, habits, core capabilities, and technology. In short, business transformation changes much of a company’s culture. 

Business transformation creates new and revises existing business processes. This reality creates a need to comprehensively perform people change management tasks to ensure smooth implementation. 

Engineers can ensure that digital transformation enables business transformation and reduces implementation risk. Uneven digital transformation can become an expensive impediment to business transformation. 

Business transformation succeeds on the foundation of digital transformation. 

The post Technology advances trigger business transformation  appeared first on Engineering.com.

The complexity of digital transformation requires significant multi-disciplinary collaboration to understand, analyze and resolve problems. But, historically, information technology (IT) and operational technology (OT) teams have worked in isolation of each other. They have different priorities, often don’t speak to each other at all, and, when they do, they speak a different language when it comes to technology terms.

Bridging this IT/OT divide is an essential aspect of a successful digital transformation. But the need to collaborate to innovate also extends to other areas of the company, including the partner ecosystem.

For some clarity on strategy, this article will help identify ideas you can implement to enhance collaboration in your organization.

Value of collaboration

When engineers and others shift mindsets from a hyper-individualistic approach to a more collaborative culture, then teams make digital transformation progress and foster innovation.

Collaboration causes individuals on digital transformation teams to challenge each other. Those creative interactions lead to:

• Innovative designs.
• Practical solutions to problems.
• An improved level of team performance.
• Reduced risks.
• Avoidance of the common reasons for failure.

When organizations enhance their collaboration culture with software that aids collaborative work, digital transformation is more efficient.

Impact of collaboration

Cross-functional collaboration during digital transformation:

• Breaks down silos and fosters a culture that drives innovation.
• Engages stakeholders, employees and external partners for a holistic transformation approach.
• Incorporates digital technologies more efficiently into products, services and processes.

A collaboration culture connects teams that may be geographically dispersed in complex business and multi-cultural settings to promote corporate goals, foster shared values, and build personal relationships. This collaborative work environment helps lower costs, shorten timelines, and improve productivity. These outcomes increase return on investment (ROI), market share and customer satisfaction.

Conversely, a lack of collaboration coupled with communication breakdowns lead to misunderstandings, delays, errors and inefficiencies that negatively impact productivity and quality. These trends will lower sales, ROI and customer satisfaction, threatening the organization’s survival.

Typical issues requiring collaboration

Digital transformation projects frequently encounter the following issues that require collaboration among various professionals, including engineers:

• Correct data quality lapses such as incompatible dates, inaccurate product codes or incomplete material descriptions due to inadequate quality control of data entry work.
• Acquire missing data such as product designs not converted after an acquisition.
• Design, build and test complex data and systems integration, such as multiple BOM structures managed by different inventory or external supplier systems.
• Acquire new data sources from data vendors such as weather data, standards documents or real-time shipping status data.
• Design digitally-enabled business processes such as fabrication or logistics.
• Encourage reluctant stakeholders including the finance or facilities departments.

Benefits of collaboration

Collaboration in digital transformation produces these benefits:

• Accelerates the pace of innovation by sharing collective knowledge and expertise to ensure first-mover advantage.
• Improves decision-making processes by involving diverse perspectives.
• Enhances agility and adaptability to respond to evolving market dynamics.
• Encourages employee engagement.
• Improves business execution.
• Reduces rework.

Collaboration strategies

Collaboration strategies that engineering leaders can use to encourage the team, and which lead to successful digital transformations, include:

• Building a collaborative ecosystem by fostering strong relationships across departments and with technology and data partners.
• Encouraging knowledge-sharing.
• Establishing clear communication channels.
• Cultivating leadership qualities.
• Championing transparency that allows team members to reach out for help as soon as they hit a snag instead of worrying about the reactions of their seniors and team members.
• Fostering trust so team members can freely share their opinions, suggestions and criticisms.
• Recognizing team achievements.

Collaboration challenges

Successful digital transformation project teams work deliberately to overcome these collaboration challenges:

• Resistance to change processes and adopt new technologies.
• Achieving a consensus among leaders on digital transformation priorities.
• Interpersonal and political conflicts.
• Conflicting departmental objectives such as the IT/OT divide.
• Dated organizational structures and leadership styles.
• Focusing on technology potential at the expense of business value.
• Mistaking diplomatic communication for a willingness to collaborate.

Communication is centered around knowledge-sharing, while collaboration applies this knowledge to problems, opportunities and tasks.

Roles in digital transformation

Effective digital transformation collaboration requires a project team. The typical roles on the team include the following:

• The project sponsor is the senior executive who approves the digital transformation project and is committed to the project goal and objectives as stated in the project charter.
• The project manager leads the work of the project team. That includes developing a detailed project plan, supervising the deliverables, and resolving issues that invariably arise.
• Business analysts describe the business pain points, opportunities, customer experience issues and technology strategy. They regularly interact with business staff across the parts of the organization involved with the project.
• Data stewards manage an organization’s digital assets to provide engineers and other end users with high-quality data that is quickly and consistently accessible. They help the digital transformation team understand available corporate data.
• Business process experts, often engineers, understand the company’s current workflow and technological environment deeply. They identify processes that digital transformation can improve and confirm the accuracy of project deliverables.
• Designers expand the work of business analysts and process experts into detailed designs. The designs will be configured in software packages by business analysts or coded into custom software by software developers.
• Security and compliance specialists contribute to the project’s technology, architecture and design choices to ensure compliance.
• Software developers code and test the software required to integrate data from diverse datastores included in the digital transformation scope.
• Change management specialists have the communications skills to generate commitment and help end-users adopt the digital transformation.
• Implementation staff lead the implementation of process changes and new applications.

Digital transformation projects may not have to fill all these roles depending on their size and goals.

Collaboration software selection criteria

Software that can improve the effectiveness of collaboration is essential for successful digital transformation. Software enables digital and virtual collaboration and remote work among engineers and other employees regardless of their physical location.

Consider the following digital collaboration software selection criteria:

• Real-time file sharing: Enable team members to access, edit and retrieve the latest files with version control. Enable specific file and folder sharing to authorized individuals outside the team.
• Multiple device support: Enable team members to access the collaboration environment using laptops or mobile devices.
• Cloud-based: Allow end-users to access the collaboration environment anytime and from anywhere to stay productive in the office, at home, or on the road.
• Search: Allow end-users to use keywords, tags, and search filters to locate content, files, and previous conversation threads quickly.
• Meetings: Support audio and video conferencing.
• Personal and group calendars: Simplify scheduling using shared calendars to coordinate team meetings and activities.
• Ease-of-use: How easy is the user interface to learn and productive for ongoing use?
• Chat or direct messaging: Available?
• Workflow automation: Ability to define and operate multi-step business processes.
• Onboarding and training: Provide sufficient training and support so end-users can maximize the productivity value of the collaboration environment.
• Integration: Support integrating other apps into the collaboration environment.
• Security: Safeguard sensitive data with encryption to stay compliant with industry regulations.
• Uptime and backup: Ensure the collaboration environment has built-in redundancy and a comprehensive backup and recovery plan to minimize downtime.
• Access control: Only authorized end-users can view, edit, or share specific files.
• Usage analytics: Collate and analyze usage data to improve the collaboration environment.

The differences across the major collaboration software packages are insignificant for most digital transformation projects or digitally enabled work. Don’t acquire another software package when your organization is already using one.

In summary: Digital transformation integrates new technologies and digital data into business processes. To succeed, engineers must acquire new collaboration habits, skills and knowledge to use data-driven digital applications productively.

The post Digital transformation requires a collaborative culture appeared first on Engineering.com.

Too often, these projects are organized in ways executives find exhausting or downright scary.

If you find yourself struggling to build and maintain executive commitment to your digital transformation project, it’s time to start speaking the executive language. Follow these tips to win the support you need.

Release functionality updates often

Executives are demanding and impatient people. Organize your digital transformation project to respond to those high expectations.

Plan to release visible digital transformation improvements for production use about four times per year. Communicate and recommunicate the new functionality you’re delivering to remind your executives of the associated benefits. Always include data visualization among the functionality you’re releasing to illustrate progress unequivocally.

For example, add digital transformation functionality in every release with:

• A new data source, such as external supply chain information, IIoT data from another manufacturing facility or simulation data.
• New data visualizations of manufacturing performance against plan, product quality measures or demand forecasting accuracy.
• Data quality improvements for customer, shipment or materials inventory data.

Never plan a Big Bang project. Never promise to release spectacular results after a year or more of work, during which time executives see nothing they can recognize as progress. This approach will kill executive commitment.

Visualize data

Digital transformation improvements become more visible with engaging data visualizations than boring reports of the same data. Charts deliver value to engineers and build commitment with cautious executives because they’re easier to understand and more engaging.

For example, include powerful data visualizations in every release:

• Variance analysis of actual production or defects vs. plan.
• Trend analysis of gross margin or market share vs. competitors.
• Sales analysis to better understand technology preferences or adoption rate.

Powerful data visualizations exhibit these characteristics:

• Show trends, key performance indicators or variances rather than point-in-time data.
• Communicate an immediately clear message while avoiding clutter and ornamentation.
• Invite end-user engagement by supporting input to change the data visualization.
• Use animation, typically to illustrate change over time.
• Minimize legends.
• Avoid pies, donuts and treemaps because they’re challenging to evaluate visually.

Reports with endless rows and columns of data don’t build commitment with executives or communicate well with engineers.

Start small

Digital transformation is a multi-year journey that consists of many projects. Tackle a small digital transformation project where you can deliver a quick win first. A small project involves only two data sources, helps a single engineering workgroup and requires minor data cleanup.

Afterwards, your executives will support a slightly more ambitious, follow-on digital transformation project based on the initial success. Examples of small digital transformation projects could include:

• Improve the demand forecast for critical components for one product line.
• Confirm the accuracy of the physics formulas that drive a critical simulation.
• Simplify data imports for the CAD system.

Being too ambitious initially and then completing late or over budget because of team or stakeholder exuberance does not build executive commitment for digital transformation. Similarly, initial successes can cause executives to push for a schedule speedup. That push will undermine quality and tarnish your hard-won credibility. Push back diplomatically.

For starting digital transformation ideas, consider 5 technologies to quickly kickstart digital transformation in 2024.

Communicate risks

Almost all digital transformation projects include associated risks that could adversely affect cost and schedule. Executives can’t be aware of risks unless you tell them.   Communicate a summary of risks and your project’s mitigation plan to maintain executives’ commitment, trust and transparency during the project.

These frequent digital transformation risks need to be communicated. For example:

• Data quality lapses you didn’t plan for that will need to be remediated.
• Staff turnover will trigger the need to hire replacements.
• Unexpected data integration complexity will increase software development efforts.

When risks that executives don’t know about explode into reality, it will kill their commitment. Don’t exaggerate risks, because that will cause executives to wonder if your project should have been approved in the first place.

Prioritize tangible benefits

Executives value digital transformation projects that deliver tangible benefits that increase revenue or decrease cost. Tangible benefits are numeric and are not subject to challenge or dismissal.

Engineers can track tangible benefits achieved as digital transformation projects progress and regularly report the dollars on a chart to management. These charts maintain executive commitment. For example, the following tangible benefits can be quantified:

• Shortened elapsed time for product development.
• Reduced use of expensive physical prototypes.
• Reduced scrap rates from product manufacturing.
• Reduced warranty claim costs from customers.

Avoid exaggerated benefits that are not credible and intangible benefits where achievement is difficult to recognize.

Sell the project business case

Digital transformation projects require an appealing business case to achieve management approval. Management is paid to be skeptical about the supposed benefits of technological advances, including digital transformation. Build support by communicating your business case succinctly and avoiding exaggerations.

For example, if the business case is about:

• Improving product quality consistency, describe how better IIoT data will narrow variation. Don’t claim you’ll reach close to perfection.
• Accelerating construction work, describe how daily effort reporting will highlight schedule slippage better for intervention. Don’t suggest you can quickly reduce prior slippage.
• Reducing design defects, describe how more sophisticated simulations will reduce those before you build a prototype. Don’t claim you’ll eliminate the defects.

Don’t oversell the business case by overstating benefits or understating costs and risks. Don’t sell the value of advanced technology. Management may become skeptical, and you will set yourself up for failure.

Speak in business terms

All digital transformation projects require technical wizardry to integrate incompatible systems, fill in data gaps and produce systems integrations, data visualizations or simulation results. However, executives are not information technologists. Maintain their commitment by speaking in business terms.

For example:

• Indicate that a new database is improving simulation performance. Don’t discuss how in-memory processing or a graph database produces the improvement.
• Report that better use of supplier data is shortening the supply chain. Don’t explain that a new network gateway and advanced data transformation software underlie this improvement.
• Demonstrate how augmented reality headsets help plant maintenance. Don’t elaborate on the software development difficulties of integrating video and plant drawings.

Technical talk about complex hardware and software will scare executives, unreasonably increase their sense of project risk and reduce their commitment to the project.  Technical discussions are better held with the organization’s CTO or enterprise architect.

Manage change

Include addressing people, processes and behavioural change in your project plan to ensure a smooth implementation of digital transformation releases. If executives hear a lot of employees whining about your digital transformation project, their commitment to the project will wane.

For example, engineers will need time and support to build familiarity with new functionality such as:

• CAD or simulation software.
• Revised inventory tracking and reporting processes.
• An upgraded prototype testing facility.

Believing that engineers and others can simply adopt new processes and software on the fly will lead to undesirable outcomes, such as slow adoption, not achieving expected benefits or even active resistance.

Fail fast and learn fast

Set expectations with executives and stakeholders that small failures will occur on the road to digital transformation and that small failures form the basis for meaningful learning. Adopt agile principles. Be willing to shuffle the feature list significantly as you learn and as priorities and benefits come more clearly into focus.

For example:

• Initial use of data integration and visualization tools reveals serious shortcomings. Select and acquire more suitable tools.
• Design work reveals gaps in requirements statements. Reprioritize the feature list to design based on solid requirements first.
• Efforts to build new data visualizations reveal software defects in the current Excel-based charts. Redevelop accurate charts first.

Always prioritize schedule—jettison scope to achieve on-schedule releases. Don’t let others spin failed experiments as digital transformation failures and unnerve executives.

Keep executives committed to digital transformation by delivering frequent, modest advances and avoiding large, risky projects that can easily fail.

—

Yogi Schulz has over 40 years of Information Technology experience in various industries. He also writes for ITWorldCanada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

The post How to speak to executives and win support for your projects appeared first on Engineering.com.