Defining a right approach towards AI implementation is the key for successful deployment. It is feasible to think on the possibilities of what AI can do, but the groundwork required to make it happen could be the very first challenge. No organization would like to see failure at the deployment stage, as the loss of capital and time without a projected results jeopardizes the business goals.
Industry leaders are more inclined towards shifting to a fast-paced digital environment. This is to gain a competitive advantage through customized solution offering and achieve highest operational efficiency with Intelligent Process Automation. With AI shaping out as a critical requisite, the major challenge many leaders or executives see is its smooth implementation and recognizing the exact application area in their business model.
In this blog, we will highlight some mistakes that can ruin your AI-driven scale up goals, right before its starts.
Some common mistakes to avoid while Implementing AI solutions
New technology comes with new complexities; however, technical challenges can be resolved with upgradation of present infrastructure. The strategy side faces the maximum resistance to this change.
Underestimating the complexities
Some companies believe that their existing software policies and processes are well suited for AI adoption. This laid-back approach to oversee the complexities of enterprise AI could slow the transformation journey for business. For a typical company to successfully build an enterprise-grade AI system on its own, it must integrate dozens of open source and third-party services. To be successful, these tools must work seamlessly together to address the challenges of data ingestion, transformation, virtualization, and processing. On the other side, challenges of developing, deploying, monitoring, and running a number of models through a traditional development and technology stacks could also increase the project timeline.
For organizations that have determined the applications areas for AI implementation, it is suggested to build specific capabilities and attain a certain maturity level in terms of technology, strategy, data, and security.
No vision for AI roadmap
Businesses functioning in Banking, Finance services, and Insurance tend to prioritize AI only for those use cases that cover a wide scope or opt for easy AI initiatives to gauge its complexities. With this approach, there are high chances of missing important processes that have productive results if AI is deployed strategic way.
Businesses should draft a long term vision for AI. This will help them choose the most strategic areas as their use case, generate momentum, and plan for their enterprise-wide AI implementation step-by-step. The obvious winners will be those who that implement strategies that can quickly respond to rapidly changing market dynamics through AI.
Depending on Siloed data
Aligning different data components and integrating multiple APIs on the grounds of security and compliance through a data silo is a complex task. For any company, e.g. financial institutions to do this effectively, they must make sure that the present data is collected and organized correctly, and this information enables machine learning models to function smoothly and aligns with the business goals.
In this scenario, partnering with an AI vendor that offers comprehensive security offerings along with a secured AI solution can eliminate the unnecessary silos in data and in the process. Altogether, their expertise will enable companies to consolidate these data components, paving the way to success and scaling up for ambitious AI projects.
Ignoring security and compliance part
One of the critical challenges of AI in financial services is the volume of data collected that comprises sensitive and confidential information needs additional security measure. On the grounds where risk of security and data breaches is on the peak, specifically businesses dealing in BFSI sector should collaborate with AI solution providers who are compliant to both industry- and region-specific regulations such as GDPR, ISO, HIPAA, CCPA, and SOC2 Type II. These vendors offer secured AI ecosystem that is complaint to data, network, and platform security.
Moving ahead, while trying to avoid these impediments during AI implementation, witnessing a pitfall could delay your AI implementation goals.
Pitfall: Initiating pilot projects in-house
Organizations look forward to developing holistic development environment dedicated to building inhouse AI capabilities. Applying a DIY approach to develop an enterprise-grade AI solution could seem tempting but requires heavy investments in resources, open-source, third-party solutions. Also, organizations look forward to developing inhouse AI projects as a promising method to reduce the development cost, but it could turn out to be a risky strategy.
Road to Success in AI implementation – Rightsizing is the key criteria
Even before getting into the need analyses stage, businesses should be clear on a specific approach of, why they need AI for?
In most scenarios, organizations choose AI for a holistic upgradation of core process automation. Businesses should conclude if they want a complete makeover or pick core processes and automate them one by one. Identifying and prioritizing for domains and key areas should be done based on the impact. This impact can be measured based on efficiency, performance, and customer experience with respect to results and ROI.
How can Simplifai as an AI company help organization address these challenges?
At Simplifai, we have engaged with businesses functioning in Banking, Finance, Insurance to make their AI adoption in their digital transformation journey a success.
We have also assisted organizations that,
- have built and failed with their inhouse AI projects
- have first use case but did not meet end-results
- have basic AI capabilities but require assessment and rapid development and deployment support.
Simplifai is an AI solution company that provides end-to-end automation for businesses with the help of Digital Employees. We use Intelligent Process Automation to help achieve process efficiency. Our AI-powered solutions can work in any third-party system and can be programmed to carry out specific tasks at the front- and back-end.
Curious to know how our Digital Employee works? Click the button below: