As legacy systems and processes continue to rely heavily on manual methods, migrating to digital will require more expensive and disruptive changes to infrastructure and operations. Despite understandable concerns about the cost of digital transformation, businesses can avoid heftier overhaul costs and opportunity costs by starting their digital journey earlier. We outline several strategies that can help you mitigate costs while securing quick wins.
How Much Does Digital Transformation Really Cost?
Those that believe digital transformation should be a priority may not be ready to allocate the capital necessary to begin the hard work it requires.
The average cost of digital transformation can range from tens of thousands to tens of millions of dollars based on a number of factors, including:
- Company size and resources
- Industry standards
- Data volume and integrity
- Project scope
- Compliance or regulatory needs
- Starting position (i.e., reshaping legacy systems or starting from a clean slate)
- Talent capabilities
The cost of digital transformation continues to be a barrier to businesses, despite the knowledge that it can provide great value. However, as “born digital” competitors and other businesses forge ahead with emerging technology, business leaders stand to lose revenue and market share from lagging innovation, slower strategic planning cycles and even an inferior product.
Opportunity Costs: Why Waiting Costs You More
When creating a cost-benefit analysis for a digital transformation project, ensure that you also include the cost associated with not doing the project. Those costs not only include lost opportunities, but they often also come with serious risks.
Consider the cost of continually putting out the fires of a legacy system or dealing with issues associated with the data from an acquired business. Each has a significant impact on both the function and budget of operations. Providing a valuation of those costs can help enlighten resistant executives who would prefer that profits be spent elsewhere.
Additional costs to consider if digital transformation is deferred include:
- Missed opportunities and revenues that competitors pursue
- Lost talent who are eager to work with modern digital systems
- Lost customers due to failure to meet expectations
- Deferred updates that create technical debt and cause increased transition costs
Opportunity costs can be difficult to measure, but they will ultimately hit your bottom line in the long term.
Strategies for Mitigating the Cost of Digital Transformation
While 89% of global enterprises have an AI or digital transformation underway, they’re only capturing 25% of the cost savings, which leads many businesses to believe transformation can wait. But businesses willing to build a strong data culture, rethink their talent models and do the work upfront will see greater ROI and cost savings.
Use defined methodologies and services to manage costs and achieve your desired objectives:
Project Management
Reduce the cost of digital transformation by taking a focused, gradual approach rather than an all-in approach.
- Use agile project management and SCRUM sprints to stay organized and keep the top priorities where they should be.
- Use phased rollouts to move functionality into production while the next features are being worked on, such as using a new insurance platform for new policies while renewal rules are being captured.
- Start with a big win. Focus on high-ROI technology applications first while considering the costs and risks.
- Prioritize capabilities that boost productivity and minimize expenses, using internal end-users as your informants.
Product Selection
Ensure that the tools and vendors you choose align with your long-term goals while still being realistic to your company’s capabilities.
- Leverage managed service solutions to supplement fixed costs.
- Focus on ease of use to ensure adoption across the business and ease digital change management.
- Consider low- and no-code solutions to minimize customizations and increase speed-to-market—but understand when a customized solution is actually better for the long term.
- Maximize platform integrations through APIs (Application Programming Interface).
- Minimize or consolidate the number of vendors you use to increase efficiency and lower subscription costs.
Talent Resources & Stakeholders
Your talent, both internal and outsourced, will be key to maximizing ROI.
- Get end-users involved early with data cleanup and data governance practices. Starting an AI model implementation, for example, will only be efficient and effective if data architecture and quality is managed early.
- Address data silos and collaboration issues before implementing new tools. Do not rely on digital tools to fix existing issues but rather to enhance capabilities.
- Be mindful of your team’s bandwidth and consider supplementing your team with fractional talent in both data science/engineering and finance to support both technical implementation and business strategy. These professionals bring needed experience to the table without adding an FTE to your payroll, and freelance consultants may be easier to find than local full-time candidates.
- For building out talent, explore partnerships with freelance platforms that specialize in providing experienced professionals for short-term projects. This minimizes risk as you assess what roles require full-time utilization—and it also infuses your team with fresh perspectives and specialized skills.
Optimize Your Investments With Fractional Expertise
Skill gaps are less dramatic than an obvious technology need, but they’re insidious in nature. They can undermine a costly migration to a new ERP system or adoption of an AI forecasting model, costing you the loss of talent in a very real and unpleasant way.
Projects such as digital transformations are ideal opportunities to supplement your team with professionals experienced in the tools and applications needed to make your project a success. Contact Paro today to learn how we can connect you with our network of fractional finance experts who have experience in system implementations of all types.
