Artificial intelligence is enabling more people than ever before to use digital solutions to solve organizational problems. For learning and development teams, that creates real opportunity.
But spotting the issue and coming up with an idea for a digital solution is only the start. The harder part is turning that idea into something real inside a large organization. AI-powered learning projects still need to be funded, supported, approved, built, launched and adopted, while surviving the small bumps and big waves that come with digital project delivery.
Many L&D leaders and practitioners may not have experience executing projects that cross over risk, legal, IT, brand, project management and procurement domains. That is the context behind the Digital Project Survival Guide, which was created to help people who have spotted a problem or opportunity inside a large organization and believe a digital solution could help solve it. The guide contains 88 practical tips to help get a digital project funded, built and adopted inside a large organization. Those tips have been distilled into these 10 key elements of digital project success.
A clearly defined problem
Every successful digital project starts with a clearly defined problem. Before jumping to solutions, you need to understand what you are solving, for whom and why it matters to the organization.
This is especially important when AI is involved. It is easy to fall in love with a solution and then look for a problem to solve, especially when the technology is new or impressive. But a digital project should begin with the problem or opportunity. Is it a big problem for the business? Is it impacting the ability of the organization to achieve its goals? Will solving it move the needle from a leadership perspective?
A strong business case
A strong business case is the core thread throughout the project lifecycle. Some people find the formal tone of a business case intimidating, but it does not need to be. The business case is simply the story of the problem, your solution, how you will execute and the benefits to the organization.
It is also more than a template. Your business case is how you get an organization to invest limited resources into your concept. For AI-enabled learning projects, the excitement around AI may help open the door, but the case still needs to explain why the organization should invest, what value will be created, what risks need to be managed, what happens after a successful pilot and what recurring costs should be included.
The right resources
Digital projects need the right mix of people and technology. This can include sponsors, vendors, your core team, risk people, potential customers, advisory panels, freelancers and AI resources.
Resources are always scarce. Time and money are limited. That means project leaders need to build their dream team, call in favors and accept where they need help. You may have listed the core team in your business case, but what about the people who can strengthen the case, challenge the thinking and help the project succeed?
The right resources also include the right vendors and partners. Technical skill matters, but so do trust, working style and cultural alignment. An AI project should not be treated as something that can be solved by technology alone.
Influence
Influence starts by validating the problem with stakeholders. Share the problem statement with as many stakeholders as practical to confirm that the problem is real and refine its definition. This strengthens your understanding and builds early support for the initiative.
If stakeholders acknowledge the problem, let them know you are working on a potential solution and ask if they would like to be involved in reviewing it. Early engagement can generate valuable input, secure sponsorship and even attract resources such as funding, time or personnel.
Influence also means finding aligned sponsors. Look for leaders with objectives similar to your project, then align your case with their goals and priorities. The right sponsor may not be in your division—department or vertical—which can make finding them more challenging, but their support can be critical to helping the project move forward.
Considered design
Considered design starts with whether the digital experience can be used by as many intended users as possible. Accessibility expectations matter, including contrast, font size and the readability of fonts. Brand requirements also need to be considered early, including color schemes, fonts, logos, sizing and spacing.
If you are using third-party developers or design professionals, provide brand guidelines early so they can start designing closer to the outcome needed from day one. Engage brand compliance early, keep the brand team across what vendors are designing and avoid feedback arriving late in the process.
Experts are great at what they do, but you are the person who needs to understand what will work in your organization. Keep design agencies, stakeholders and internal contributors on track, and remember that cultural differences can affect both design and adoption, particularly when the experience will be used across different groups, teams or countries.
Respect for compliance and risk
The people responsible for risk, brand, legal, security and privacy should be engaged early, not left until the end. They need to understand what you are trying to do, what you are not trying to do and what requirements may affect the design, cost, timing or launch of the project.
The consequences of late engagement can be serious. Risk teams may misunderstand your project and create unnecessary delays. Brand feedback can consume time and money if it arrives late. Without legal sign-off, your launch may not occur. Security reviews can create unbudgeted costs and project timeline delays. Privacy requirements can affect platform architecture, terms of use, data capture and the critical path.
For AI-powered learning projects, this is not a box-ticking exercise. Engage the right people early, explain the project clearly, document important discussions and treat the review process with the respect it deserves.
Design for data
If your business case starts with a pilot, be clear about what data you will capture and return with after successful execution. Designing for data helps ensure you have the data to support the ROI in your business case and adds rigour to the case you are making.
Digital experiences enable you to collect data at a volume unachievable by physical experiences. You should be able to report on the key objectives outlined in your business case, but also provide further insights about your users. This means designing for data capture and resourcing a work stream to investigate and surface insights.
Data can also help improve the digital experience. User surveys, user segmentation and feedback from different types of users can all help shape the enhancement pathway. There may also be useful data outside the platform, such as employee turnover or sales data, but additional data sources can increase complexity and cost.
Resilience
There are positive peaks and negative troughs in creating and activating digital solutions. You need to accept that there will be troughs of disappointment, frustration, anger and despair.
They can come from anywhere, and be big or small. Funding can be challenged or frozen. Sponsors can leave or withdraw support. Critical team members can move on. Alternative solutions can attack yours. Companywide restructuring or personal or family illness can also affect the project.
It is not whether one of these big waves will hit your project, but what you do when one does. This is where sponsors, mentors, belief and energy matter. A resilient mindset helps you survive difficult periods and continue moving the project forward.
A learning mindset
A learning mindset means filling skill gaps, staying curious and growing through the project. If this is your first digital project, you will probably encounter areas that are new to you, including strategy, business case authoring, procurement, project management, financial management, IT security, privacy, legal terms, brand, marketing, communications, data architecture, partnering agreements, research design and activation planning.
Even experienced campaigners need to stay up to date with AI developments and implications. You need to take on a mindset of “I’ll learn it as I go.” For your weakest areas, commit to learning more and being more capable at the end of the project than at the start. This is not your last project. The learning will help you for the rest of your career.
Responsibility and trust
Taking responsibility for the users of your digital experience is a critical part of project leadership. Where users are exhibiting repeatable, unproductive behaviors, it is your job to remedy the situation and seek ways to prevent the occurrence in the future. As the guide puts it, take responsibility for your creation.
For AI-enabled digital experiences, this responsibility also includes making and documenting conscious decisions about the ethical use of AI platforms and activities. This should occur across the whole development cycle. Depending on the project, those decisions may sit with the individual running the initiative, the broader team or stakeholders who need to be consulted.
Trust is another enabler across the whole project. Building trust with stakeholders through truthful, respectful interactions where promises are kept is critical to long-term project support. Treat every interaction as an opportunity to build trust, because trust will increase the chance of project survival and support future endeavours inside and beyond the organization.
The path forward
AI has increased what is possible for L&D teams and organizations more broadly, but possibility is not the same as delivery. The real test is whether the idea can survive the organization around it: the funding process, the stakeholder landscape, the risk review, the design decisions, the data requirements, the launch effort and the inevitable waves that come with delivery.
That is where AI initiatives will succeed or fail, not just in the effectiveness of the technology, but in the effectiveness of the project execution around it.
