Forget Bold Strategic CRM/AI Initiatives: For now, Focus on the Tactical and Practical

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AI is at Peak Hype.  Yes, one day we will have autonomous transportation pods and ubiquitous language translation services. Just not yet.  For now, focus CRM/AI applications on tactics, not bold strategic moves.

AI Hype > AI Reality

AI is currently at Peak Hype. 

The good news

The technology infrastructure that enables artificial intelligence is a reality and getting stronger all the time.  This infrastructure includes:

  • Cloud computing resources that deliver on-demand processing and storage
  • Software and AI development tools that enable teams to work more quickly and cost-effectively
  • Application development and deployment platforms that target non-development business professionals and democratize the development process
  • Database applications and tools that enable the management of large and dynamic data sets (Big Data)
  •  Processing, communications, and storage that reside at the edge (think: vehicles, drones, phones, medical devices, health monitoring devices: all smart devices that collect, analyze, and respond to data at the point of use)
  • People with the skills needed to develop AI and advanced analytic applications (although demand still far exceeds supply)

Cautionary News

Hype, inflated expectations, and wishful thinking fog our view.  Most organizations under estimate the challenges:

  • The complexity of managing big data and deriving meaningful, actionable insight from that data
  • The limited availability of AI developers with the necessary technical skills
  • The limited availability of business professionals with the experience necessary to apply AI to real-world problems
  • The speed with which most organizations can assimilate change

Our AI imaginations get carried away, often with the tacit encouragement of technology vendors that are contributing to the hype surrounding AI.   Yes, some industries and application areas are working toward major AI breakthroughs: the use of AI in diagnosing medical conditions, making investment recommendations, enabling robotic manufacturing and autonomous vehicles.  The challenge is that these “moonshot” projects require space program levels of investment.  Right now, I recommend pragmatism and profitability.  Focus your AI-related CRM projects on tactical improvements, not major strategic investments. 

Your next CRM/AI project should be focused, finite, and pragmatic.  Read: Profitable.

CRM: Hype, then Results

Even in the technology industry, where hype is the lingua franca, CRM stands out.  For almost two decades the CRM industry has walked a fine line between hype and results.  CRM vendors are expert at generating excitement, creating a vision, making promises – and then, slowly but surely, delivering on that promise.  Consider:

  • The CRM segment realized double digit growth rates for the past decade
  • CRM software investment was almost $40 M in 2018 and drove an additional $20 M in services
  • CRM is bigger than enterprise resource planning (ERP), supply chain management (SCM), and human capital management (HCM)

We are seeing a similar Hype/Results trajectory with regards to AI and CRM today.  Vendors are creating a storyline on AI transforming CRM and the customer experience.  Analytics, product recommendations, and sales dashboards are now all “AI applications”. 

CRM/AI is challenging, complex, and has high expectations.  Start with proven and (mostly packaged) CRM/AI solutions.  Remember:

  • Building your own CRM/AI applications is time consuming and expensive.  There is a relatively small set of organizations that have the resources to take on moon-shot projects. 
  • CRM/AI is complex.  Success depends on the aggregation and management of large sets of data, the development and maintenance of complex algorithms, and the ability of the organization to effectively implement and leverage the output of the CRM/AI application. 
  • Your management team has limited patience. 

Expectations in the executive suite for AI are high
 and may exceed the current AI reality. 

Stick with Proven CRM/AI Use Cases

Developing your own CRM/AI application is a bit like needing a car, but then buying an engine, a transmission, and an assorted basket of parts so you can build your own.  You won’t necessarily be not successful, but it is sure to take a lot more time and money.  If you are a well-financed NASCAR team with the Amazon, Google, or <vendor of choice> logo on the hood, that might not be a problem.  On the other hand, if you are looking to improve your customer experience while also increasing sales, customer sat, and profitability, the internal development road might not be the best for you – at least, not at this stage of AI market maturity.

Here is our formula for achieving CRM/AI success.

Start with a well-defined project scope that directly impacts revenue.  Quantify and document the results. 
Expand incrementally. 

The CRM/AI market, at the current state of maturity, has coalesced around a handful of applications and use cases.  Despite the market hype the number of implementations of CRM/AI is relatively small, compared to the size and scope of the CRM market.

Some of these use cases have a direct revenue impact: lead management and customer churn management applications are examples.  Other use case focus on cost reduction and efficiency: Natural language processing apps aim to replace call center agents or make the customer transaction faster.

Here are the leading CRM/AI applications to focus on:

  • Lead management: Scoring, qualification, and prioritization of high potential leads to that result in higher conversion rates of higher value leads
  • Customer churn: Predicting the likelihood of a customer (typically with a subscription or recurring revenue license model – think cell phone service, cable TV and internet services) to cancel their service, typically based on a reduction in usage
  • Product recommendations: The next generation of personalization engines for digital commerce, recommendation apps will promote products or services that are likely to be of interest to the buyer based on prior purchasing activity, expressed interest, browsing behavior, etc.
  • Natural language processing (NLP): NLP-enabled applications are used in a range of customer support and service applications; they will respond to customer’s voice commands, listen for key words (verbal or written) to facilitate customer service activities, and sometimes identify customer emotion to anticipate and better manage customer satisfaction issues. NLP apps have also been used to record and analyze customer sales/support calls for training and process improvement.
  • Social media and market research: Searches for and analyzes publicly available data, including social media, news sites, and public market research sites, for insight.  Provides guidance to marketing, sales, product management teams for the development of campaigns.  Less focused on data and analytics than these other sectors, and with less of an impact on the bottom line than some other applications cited here
  • Price optimization and management: Analyzes large inputs of data, including inventory levels, customer demand, seasonality, availability, and competitive activity to develop and deliver a dynamic, time-sensitive price recommendation that helps to maximize profitability.  (Think of the way airline tickets or hotel rooms are automatically priced).

There are other examples of CRM/AI applications that meet our suggested criteria, but the ones above represent a large majority of the CRM/AI applications that are available and implemented.  (Beware of AI claims that are not backed up with live site references).

3 Things to do Now

  1. Look for CRM/AI applications that directly impact revenue and profitability.  In sales that means better lead management; in customer service, churn management; in digital commerce, personalization and conversion.  There are other use cases, but these are the most mature and offer the best chance of success.
  2. Start small.  Managing large, dynamic data sets from multiple sources is complicated, and organizational processes (like those in sales and service management) take time to change.  Pilot deployments that focus on a small segment or region of the business have the best chances of success.  Grow from there.
  3. Set reasonable expectations with senior management.  Document and quantify the impact on the bottom line.  Ask for more resource (people, time, and investment) to move to the next level of CRM/AI.


Disclosure: I wrote this post. It represents my opinion. I have no financial interest in any of the parties mentioned.


The views and opinions in this analysis are my own and do not represent positions or opinions of The Analyst Syndicate. Read more on the Disclosure Policy.

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