Regardless, the fact is that many organisations have failed to harness the power of AI solutions. Drawing upon over 8 years of experience and 300 AI projects, we’ve found that it is seldom the technology itself at fault and rather ill-defined objectives, solutions that don’t scale, or a lack of appreciation for the ongoing maintenance and monitoring required which hampers the chance of real business value.
Organisations in a hurry to implement data science and AI rush to build teams and departments, often with limited experience of how to go about it. It is understandable that many choose to accelerate this process to remain competitive, but Rome wasn't built in a day, and the value AI delivers is rarely discovered overnight. The challenges of building a synergetic team, coupled with the huge costs of lucrative salaries often result in unfinished or inadequate models.
In this blog, we will outline how every business can maximise their AI viability, eradicate unnecessary costs, and make AI an integral part of their operations without having to commit time and spend to building their own AI teams or departments.
Consider this; when companies launch additional content on their site, they don’t hire dev teams or departments to pivot their existing platforms. They find tools and expertise to complement their existing teams and resources, often deploying a CMS to allow them to bypass the chasm in the expertise they lack.
AI is no different; companies don’t need to test its capability by building out their own resource. Instead, businesses looking to develop POCs and optimise their operations can leverage the experience and expertise of specialised talent, at a fraction of the cost.
Building departments from scratch carries some advantages but is outweighed heavily by having on-demand bespoke outsourcing, not to mention the years it takes to ensure an AI department is generating efficiency and delivering outcomes.
“In my opinion, the barriers to AI adoption are around:
Creating a sound AI strategy showcasing ROI on execution
Finding skilled workers at the cross-section of business and technology to execute on the defined AI strategy
Governing, managing and collaborating on the data and the models to scale the AI programs
All three barriers can be overcome by having the right strategy across people, processes, technology, and data.”
- Rajat Sinha, Global Head of Alliances and Partnerships, Data Analytics & AI, Wipro Limited
As the rationale behind adopting AI is to optimise processes and solve business problems, scoping out your own team will require organisations to sacrifice that efficiency and create more business problems before they get there.
Having worked across a myriad of sectors, we know the types of models that work on particular problems, knowing when to utilise NLP (Natural Language Processing) to automate customer service (something that is of particular concern to businesses since the impact of Covid-19, with customer service teams overwhelmed and understaffed) and which type of recommender system works with e-commerce depending on the level.
This experience is the difference between one of these factors strangling your AI project or ensuring its success. Many ‘failed’ AI projects are simply the victim of poor planning and incorrect personnel.
Often, projects can be successful in their inception, but the lack of resource to deploy or update models proves to be the undoing of months’ and even years’ worth of work. For less than it would cost to employ just one data scientist, companies could maintain and update their models to ensure they remain optimal, generating their ROI many times over.
Pivigo believes in making AI accessible to all, whether a company is big, small, or on a budget. The approach we take to developing and deploying solutions is collaborative but does not require the people we work with to have extensive expertise in deploying and managing AI solutions. We can also assess if you have suitable data and structure it for you, through a process called data cleaning.
We work as an AI-as-a-Service model. This means we build and train your AI models for you, removing the need for domain expertise or hiring personnel. Afterwards, we handle the deployment, ensuring a successful launch.
When changes occur in the world that impact the way organisations do business, it can seriously affect the performance of AI models. Normally, this process can be small and unnoticeable, but, can lead to sudden underperformance and require lengthy and costly changes. At Pivigo, we mitigate the need for this concern, by giving all our clients access to ongoing-maintenance on a monthly subscription, so you only need us for as long as you see fit.