A McKinsey survey reports that nearly 50 percent of firms have fully adopted AI, while an additional 30 percent have implemented AI pilot programs. AI is gaining prominence in advancing areas, creating new business values across different industries. The increasing benefits of AI adoption have changed the equation from if to when and how as AI has become embedded across multiple sectors and operations. With AI proving to be the new imperative in business, the question is: to build or to buy?
The Make-Buy Decision: Questions You Should Be Asking.
In 2019, more than 40 percent of organizations will use AI to automate their businesses. Note the following major points if considering a “build” decision:
1. Primary Firm Focus
Focus on the firm’s area of expertise. Your organization may have a robust IT department and a proficient data science team, but does it have the resources to build and scale up a new product? Building is justified only if it’s based on the firm’s core technology, that which differentiates it and offers a competitive edge. It is important to understand that building a product isn’t always a source of competitive advantage, especially if it can be built or used by another firm .
2. Strategic Business Need
Zero in on the objective impact of this product. How fast does it need to be built for these goals to be realized? Will buying it help achieve set goals faster and smoother? Consider how things will change for the firm should the technology be implemented faster.
3. Integration and Maintenance
It is essential to have a dedicated team in place to update and maintain the product as new data sets are added and features are expanded on. Does the organization have the necessary resources and time to integrate this into everyday systems? How will these updates be processed and how will it affect user teams? Determine if it is – possible to obtain an integrated view of the process across departments and via multiple channels prior to building it.
Determine the complete cost of building a product vs buying it from a provider. Are the two comparable, especially in regards to ROI calculation? Are there other projects where the same in-house skills and resources are required without a viable service provider option? Be sure the organization has the required resources for maintenance and ongoing feature enhancements.
Is Building a False Choice?
Any product goes through these four phases: creation, maintenance, evolution, support and cost. Determine a make-buy decision by considering each phase.
L Assume your engineers are capable of building the product required. Unless it’s an incredibly specific product catering to a niche domain, if your engineers can build it, so can someone else.
The engineers who built the product have the potential to move on to other projects or firms before the product maintenance stage begins. How will you maintain the product in the following years? In most cases, maintenance after the initial launch tapers out, but is still essential.
The product needs to be updated constantly to keep pace with technological developments, legislations and changing demands of end-users. Building a product in-house, from scratch, often doesn’t compare to the experience or feature sets that a dedicated firm can offer.
With every product, a dedicated section for customer support should be in place, including a team able to resolve customer queries at odd hours, from late nights to weekends. The customer’s needs will nearly always evolve faster than the internal team, making the ongoing support stage one that requires a strategic action plan
Potential Risk in Building
Lack of Expertise
Building a product requires a team with a strong knowledge base and expertise. Lack of technical know-how either with respect to AI/ML or the industry will undoubtedly prove to be problematic. While a finished, up-to-date AI product may seem simple, it requires more than just an algorithm.
An in-house cost estimate is not always accurate, as there are several variables that go unaccounted for. Most firms displace resources from projects that are more nuanced and necessary to the building of a product that could be obtained from a vendor. Building this, however, is never a one-time cost. A project of this scale requires consistent financial flow to maintain and update. The wrong decision here could impact the growth and solvency of the product and the company itself in the long term.
Evolving market conditions must be considered. Is there a risk the product become obsolete in a few years? A custom solution is often deterred through inaccurate scoping by the internal team. Organizational priorities could stall the progress, without proper IT or tech support for large scale integration and implementation.
Implementation is not the last hurdle to cross. Building a product translates to responsibility for updates and maintenance . The downtime required could have several implications in customer relations and marketing. With new technologies arriving every day, integration into the product may not have been included in the purview.
The revenue of the AI industry will reach $1.2 trillion by the end of 2019 and is expected to grow to $3.9 trillion by 2022. AI has the potential, but it can’t be unlocked by everyone. Only data scientists with the right domain expertise can create a viable, AI-first solution for genuinely improved outcomes and clear ROI.
At Dasceq, we are propelling monetization of data for the collections industry with our next-gen, AI-driven 2i™ platform which offers actionable insights to boost efficiency and overall collected amount. Multivariate testing enables continuous learning to optimize contact and treatment strategies while learning from successful collections trends.
With the implementation of 2i™ platform, our clients in the auto finance and subprime lending sectors have seen over 30 percent boost in collections. Maintaining ROI for an internal product is critical and the product has to be maintained to keep up with the latest industry trends. Dasceq empowers your organization to drive collections leveraging these industry trends and best practices.
Our clients concur that creating a model like Dasceq’s would cost them a non-strategic level of time and resources. Through our phased implementation approach, we manage organizational change. We provide continuous support, and we are always adding next-gen features to the 2i™ platform.
While it’s possible to replicate this kind of data model for collections with technical expertise and industrial know-how, Dasceq’s 2i™ platform ensures lenders, creditors, and collectors will achieve strategic business goals with a higher ROI in far less time. Feel free to get in touch with Dasceq for a free product demo.