Too often, engineers find they’ve added managing their project sponsor to that lengthy list. That perspective is misguided. Instead, engineers should view their project sponsor as someone who can help them, someone who can be expected to accept specific thorny tasks that only a senior executive can deftly handle for the benefit of the project.

Here are some of the ways that project sponsors help engineers. Consider using some of the points in this article during your next meeting with your project sponsor.

You can explore these and other tips to help project sponsors and engineers be more effective in our new book, A Project Sponsor’s Warp-Speed Guide – Improving Project Performance. It’s available from Amazon at this link.

Communicates project benefits throughout the organization

The project sponsor and steering committee members must enthusiastically communicate, sell and defend the project benefits in meetings and informal discussions throughout the organization. If these individuals fail to champion the benefits or, worse, challenge the benefits or criticize the project, the project is doomed.

For example, these leaders frequently remind the organization of the project’s value proposition to maintain its commitment to the project at various management meetings or town hall events.

When this visible public support is not evident, engineers provide project sponsors and steering committee members with brief talking points to encourage more communication.

Guides the project manager

The project sponsor guides the project manager. The project sponsor offers organizational insights about internal politics, corporate history, and prejudices held by various stakeholders. This information is valuable to engineers, who often do not have enough seniority and reputation for the organization to accept their necessary but unwelcome recommendations.

When engineers feel neglected, they can reach out to their project sponsors to reconfirm the following best practice points for building their relationship:

• Commit to a firm schedule of meetings with the project manager. The frequency is usually weekly or bi-weekly.
• Respect the project manager’s mandate and delegation.
• Provide open, frank feedback to the project manager on project observations and what improvements are needed.
• Demand honest opinions from the project manager about project status and issues.
• Will not create pressure to provide a false, overly optimistic project status.
• Operate the project manager relationship based on mutual trust.

Conversely, if the project sponsor loses confidence in the engineer, the project sponsor must replace them.

Encourages the team

The project sponsor occasionally speaks to the entire team to publicly provide kudos, express encouragement and boost morale. On these occasions, the project sponsor strongly supports the project and the team’s work.

For example, the project sponsor can share some senior management scuttlebutt and organization performance metrics that would be good for the team to hear and reinforce the importance of the team’s work for the organization.

When teams feel unappreciated, engineers can diplomatically encourage their project sponsors to inspire the team.

Ensures resource commitments are fulfilled

When the project was approved, various stakeholders accepted resource commitments to work with the project. However, as the project proceeds, the business departments are typically hit with new resource demands and gradually de-commit from the project. Only the project sponsor can reverse this trend.

For example, only the project sponsor can effectively glare at senior managers or VPs to rebuild the commitment. Engineers can’t do that and survive in the organization.

It’s up to engineers to point out this failure to fulfill commitments to their project sponsors for resolution.

Resolves issues that the project manager cannot resolve

Every project develops issues related to scope, priorities and approach. Only the project sponsor can resolve or lead the resolution of the more significant issues that tend to cross organizational lines.

For example, the project depends on manufacturing data, and the data quality is low. Only the project sponsor can march into the office of the VP of Manufacturing and ask that the data be cleaned up and extract a commitment that the data will remain high quality into the future.

It’s up to engineers to raise these issues with their project sponsors for resolution.

Shields the team from distracting internal politics

To the greatest extent possible, the project sponsor shields the team from distracting and harmful internal politics. The project sponsor also defends the project team from being hijacked to solve a crisis in the business.

For example, if the team is distracted and upset by rumours of a reorganization or downsizing, the project sponsor can reassure the team.

It’s up to engineers to raise the concerns with their project sponsors for attention.

You can explore these and other tips for effective project sponsors in A Project Sponsor’s Warp-Speed Guide – Improving Project Performance, a new book I wrote with my co-author Jocelyn Lapointe. It’s available from Amazon at this link. View the book as a reference tool. You don’t have to read it all to obtain actionable insights.

The post How project sponsors help project managers succeed appeared first on Engineering.com.

Scoping a digital transformation program is never easy because the number of attractive opportunities available to engineers almost always exceeds the available budget and staff capacity. Sometimes, ambitious blue sky projects are approved because their vision is so appealing. That never ends well and wastes significant resources. Every project should be scoped to advance some aspect of the business strategy.

Here’s a five-step process for scoping projects that will avoid boondoggles and deliver value for engineers and their organizations.

Prioritize opportunities

Before engineers can start work on any digital transformation project, they need to understand which opportunities offer the highest return and lowest risk. Use these questions to rank proposed projects:

• Does the scope of the proposed projects address significant pain points and opportunities? This question differentiates urgent needs from wants.
• Are there senior executives prepared to be the project sponsors and champions of the projects? Proposals without a project sponsor should be rejected due to likely failure.
• How appealing are the business cases for the proposed projects? Low-return proposals should receive a low priority.
• How does the scope of the proposed projects align with the business strategy? Poor alignments indicate lower-priority proposals.
• Are any of the proposed projects prerequisites to other projects? Sometimes, foundational projects, often with little or no business case, must be completed before other higher-benefit projects can start.
• How risky is the scope of the proposed projects? Projects requiring extensive data cleanup, emerging technologies or significant specialty skills should be deferred.
• How enormous is the scope of the proposed projects? Projects that are too small don’t advance digital transformation much. Projects requiring massive budgets or organizational resources should be split up or rejected.
• Will the scope of any of the proposed projects take more than one year to complete? Longer projects consume significant staff resources and are at higher risk of not finishing. These should be broken up or deferred.

Answering these questions leads to a ranked list of digital transformation projects and rejected proposals. Projects with the highest return and lowest risk will be near the top. Engineering managers can then approve as many projects as budget and staff constraints allow.

Understand the business context

Before engineers can scope an approved digital transformation project, they need to understand the business context and the drivers for change. Start by answering these questions and documenting the answers in the project charter:

• Who are the key stakeholders, such as internal departments, customers, and vendors involved or affected by the project?
• What are the needs, expectations, and preferences of the key stakeholders?
• How will the project scope impact the business processes, culture, and organization structure?
• What external forces, such as competition, technological advances or political change, are driving the project scope?

If you can’t answer some of these questions, you must conduct more analysis or conclude that the opportunity is not as appealing as initially thought.

Write the project charter

The project charter is a document that summarizes the project goal, supporting objectives, deliverables, scope, business case, organization, budget, assumptions, constraints, and risks. It serves as a basis for planning, executing, monitoring, and controlling the project. Writing this document requires the collaboration of the project sponsor, team members and stakeholders to reach a consensus on every element. Consensus-building helps avoid misunderstandings, disputes, or rework later during the digital transformation project.

If the collaborating groups can’t reach a consensus on the project characteristics, such as scope, one or more of the following issues are occurring:

• Lack of agreement on scope and priorities.
• Wishful thinking about what the allocated resources can achieve.
• Personality or power conflicts.
• A fantasy business case.
• A focus on technology potential rather than business value.
• An attempt to tackle more scope than the organization can absorb.

The organization may be unprepared for the opportunity if the project manager and project sponsor can’t facilitate the needed consensus. It’s best to stop before the project starts and consumes significant resources for no business beefit.

Build support for the project scope

Once the project charter has been accepted, it’s time to build support for the digital transformation project scope among a wider audience of senior management, stakeholders, and employees. Helpful techniques used to review and validate the project scope include:

• Scope feedback – a continuous process of collecting and analyzing feedback from customers, end-users, or other stakeholders on the project deliverables or outcomes).
• Scope validation – an informal process of testing, inspecting, or evaluating the project goal, objectives, scope description and deliverable list to confirm they meet the scope and high-level requirements.
• Scope verification – a more formal process of obtaining acceptance and sign-off of the project charter and its scope description.

If the project manager and project sponsor encounter significant challenges or hesitancy while building support, the project charter may need to be revised. If stakeholders challenge the business case or the scope description, the opportunity is not as appealing as initially thought. Then, it’s best to stop before the project starts.

Manage scope changes

Once a project is underway, scope change proposals are unavoidable as the understanding of project details grows. Because digital transformation projects involve exploration, uncertainty, and innovation, the frequency of scope change proposals will be higher than for other project types.

When stakeholders see that you’re managing a project effectively, they want more scope. However, you won’t be a hero if you deliver more scope later than the original project completion date. Engineers can respond to this push for more scope as follows:

• Asking stakeholders to fund a change order only to analyze the impact of their proposed scope changes. If they don’t support that, you’ve dodged a bullet.
• If your team produces an analysis report of the impact of their scope changes, recommend that the work be deferred to the likely follow-on project.
• If your stakeholders want the change now, ask them what other scope they want to remove from the project to maintain the budget and schedule. Since the stakeholders want everything in the current scope, the new idea is dropped.

Once the project team recognizes that you’re not approving any proposed scope changes, they will question why the proposals are being documented and sometimes analyzed. The value of the documentation is that the unapproved scope changes provide an excellent starting point for scoping a follow-on project.

When engineers manage digital transformation projects by including only the scope described in the project charter and avoid adding appealing new scope, they will deliver successful projects.

The post Managing digital transformation scope appeared first on Engineering.com.

Many engineers are investigating digital transformation initiatives using artificial intelligence (AI) features within their organizations.

They’re generally aware of various AI risks and are searching for ways to better categorize, understand, mitigate and communicate them.

A group of scientists from the Massachusetts Institute of Technology (MIT) and other universities are also concerned with AI risks, so they built an AI Risk Repository to serve as a common frame of reference for discussing and managing AI risks.

They classified AI risks into seven AI risk domains:

1. Discrimination & toxicity.
2. Privacy and security.
3. Misinformation.
4. Malicious actors and misuse.
5. Human-computer interaction.
6. Socioeconomic and environmental.
7. AI system safety, failures and limitations.

When engineers use these AI risk domains to conduct risk management for their digital transformation initiatives, they will gain a better understanding of how AI can help their digital transformation initiatives without causing irreparable harm.

Discrimination & toxicity

The discrimination and toxicity AI risk domain consists of the following subdomains:

1. Unfair discrimination and misrepresentation – Unequal treatment of individuals or groups by AI.
2. Exposure to toxic content – AI systems expose end-users to harmful, abusive, unsafe, or inappropriate content.
3. Unequal performance across groups – The accuracy and effectiveness of AI decisions and actions are directly related to group membership.

If this risk becomes a reality, the business impact includes loss of reputation and the distraction and cost of lawsuits. If this risk turns into reality, the societal implications include unfair outcomes for discriminated groups.

Privacy and security

The privacy and security AI risk domain consists of the following subdomains:

1. Compromise of privacy – AI systems obtain, leak, or correctly infer sensitive information.
2. Security attacks – AI systems exploit systems, software development tool chains and hardware vulnerabilities.

If this risk turns into reality, the business impact includes data and privacy breaches and loss of confidential intellectual property, leading to regulatory fines and the cost of lawsuits. If this risk becomes a reality, the societal implications include:

• Compromising end-user privacy expectations, assisting identity theft, or causing loss of confidential intellectual property.
• Breaches of personal data and privacy.
• System manipulation causing unsafe outputs or behavior.

Misinformation

The misinformation AI risk domain consists of the following subdomains:

1. False or misleading information – AI systems inadvertently generate or spread incorrect or deceptive information.
2. Pollution of the information ecosystem and loss of consensus reality – Highly personalized AI-generated misinformation creates “filter bubbles” where individuals only see what matches their existing beliefs.

If this risk turns into reality, the business impact includes misleading performance information, employee mistrust and potentially dangerous product designs. If this risk becomes a reality, the societal implications include inaccurate beliefs in end-users that weaken social cohesion and political processes.

Malicious actors and misuse

The malicious actors and misuse AI risk domain consists of the following subdomains:

1. Disinformation, surveillance and influence at scale – Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda.
2. Cyberattacks, weapon development or use and mass harm – Using AI systems to develop cyberweapons, develop new or enhance existing weapons.
3. Fraud, scams and targeted manipulation – Using AI systems to gain a personal advantage over others.

If this risk becomes a reality, the business impact includes loss of business continuity, cost to recover from attacks and bankruptcy. If this risk turns into reality, the societal implications include:

• Manipulating political processes, public opinion and behavior.
• Using weapons to cause mass harm.
• Enabling cheating, fraud, scams, or blackmail.

Human-computer interaction

The human-computer interaction AI risk domain consists of the following subdomains:

1. Overreliance and unsafe use – End-users anthropomorphizing, trusting, or relying on AI systems.
2. Loss of human agency and autonomy – End-users delegate critical decisions to AI systems, or AI systems make decisions that diminish human control and autonomy.

If this risk turns into reality, the business impact includes misleading and potentially dangerous system outputs. If this risk becomes a reality, the societal implications include compromising personal autonomy and weakening social ties.

Socioeconomic and environmental

The socioeconomic and environmental AI risk domain consists of the following subdomains:

1. Power centralization and unfair distribution of benefits – AI-driven concentration of power and resources within certain entities or groups.
2. Increased inequality and decline in employment quality – The widespread use of AI leads to social and economic disparities.
3. Economic and cultural devaluation of human effort – AI systems capable of creating economic or cultural value reproduce human innovation or creativity.
4. Competitive dynamics – Competition by AI developers or state-like actors perpetuates an AI “race” by rapidly developing, deploying and applying AI systems to maximize strategic or economic advantage.
5. Governance failure – Inadequate regulatory frameworks and oversight mechanisms fail to keep pace with AI development.
6. Environmental harm – The development and operation of AI systems cause environmental damage, partially through their enormous electricity consumption.

If this risk becomes a reality, many businesses will likely collapse due to economic collapse. If this risk turns into reality, the societal implications include:

• Inequitable distribution of benefits and increased societal inequality.
• Destabilizing economic and social systems that rely on human effort.
• Reduced appreciation for human skills.
• Release of unsafe and error-prone systems.
• Ineffective governance of AI systems.

AI system safety, failures and limitations

The AI system safety, failures, & limitations AI risk domain consists of the following subdomains:

1. AI pursuing its own goals in conflict with human goals or values – AI systems act in conflict with ethical standards or human goals or values.
2. AI possessing dangerous capabilities –  AI systems develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy,cyber-offense, AI development, situational awareness and self-proliferation.
3. Lack of capability or robustness – AI systems fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences.
4. Lack of transparency or interpretability – Challenges in understanding or explaining the decision-making processes of AI systems.
5. AI welfare and rights – Ethical considerations regarding the treatment of potentially sentient AI entities.

If this risk becomes a reality, the business impact includes loss of reputation and the distraction and cost of lawsuits. If this risk turns into reality, the societal implications include:

• AI using dangerous capabilities such as manipulation, deception, or situational awareness to seek power or self-proliferate.
• Mistrusting AI systems.
• Enforcing compliance standards becomes difficult.

Do not expect every digital transformation initiative to have risks in every subdomain. Nonetheless, there is value in explicitly considering every subdomain during the risk identification task.

When digital transformation teams identify and mitigate project risks using these AI risk domains, they will ensure their risk management processes are as comprehensive as possible.

The post Managing AI risks in digital transformation appeared first on Engineering.com.

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.

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• 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.

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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.

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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.

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