This is classical “white-paper” format from PwC who know very well on spinning issues. Primarily, they are trying to gain customers by pushing their own model of implementation. I have included a screenshot from their “paper” and it does look impressive on the first look. However, pay special attention to what value scoping and value discovery have to say.
They don’t define the efficiency gains in whatever way the data is required to be “scoped” or “processed”. A data scientist and a developer can churn out a dashboard with visual representation of the metrics but remains a glorified premise of the excel sheets. I made some animated charts to “wow” my audience for some statistics related to the activity in my work place. However, it served little purpose in the actionable information required further.
I won’t be able to comment on the entire article, but I am not discarding it outright. There’s some practical advise here:
AI requires significant quantities of annotated data in order to learn. Data should be both representative of the problem you are trying to solve and inclusive of the different complexities you may anticipate. In the case of invoice process automation, you would not only need lots of samples of previously annotated invoices, but you also would need to confirm the samples themselves are different enough from one another so the models can learn to annotate new invoice types effectively.
Data aside, building and running AI applications can often be compute-intensive, especially with more complex models that are trained on massive quantities of data. This technology cost should be borne upfront before companies can accurately estimate the business value of the applications they look to build or deploy. Seeing high and continually increasing costs before realizing any benefit is not easy for many organizations to stomach, and many drop their AI initiatives before they can give them the time to realize their value.
As addressed previously in the past, on-premise computing becomes imperative. Data security, graded access and the insights from output require significant resources. There may be issues related to compliance with privacy and legal requirements which drive up the costs significantly and require entire teams to negotiate the complex landscape.
They do not address these in the writeup and remain a significant lacuna. Should the companies hire these consultants? If you cannot figure out your own requirements, how can an outsider provide you with a sterling perspective after charging you hefty fees?