Whether or not you are selling Industry 4.0 techs like Artificial Intelligence (AI) and the Internet of Things (IoT), they will change the way you sell. Highly integrated, IoT and AI based solutions will change expectations of enterprise and the shape of B2B sales interactions for good. Knowing how will help us thrive, close deals and power growth. The market is demanding a new way of selling which which is about adapting to sell to businesses that are becoming ever more digital, adopting AI and Industry 4.0 at scale to become ever more global. Many are calling this approach “Sales 4.0” and it is happening right now – here’s how.

Case study – Enel / C3.ai

C3.ai, a venture of Silicon Valley legend Tom Siebel marries AI and the Internet of Things (IoT) on an enterprise platform to deliver AI applications at scale. C3.ai work across energy, utilities, banking and other sectors to deliver applications focusing on functions as diverse as predictive maintenance, energy management and anti-money laundering.

C3.ai has implemented an enterprise AI solution for Enel, a multinational energy company and one of the world’s leading integrated electricity and gas operators. Enel operates a network of over 20 million smart electric meters across Italy and Spain – one of their projects with C3.ai was to apply AI based fraud detection on this network. Their target was to double the recovery of unbilled energy while making the business process more efficient. Identifying and investigating these losses in most utilities, using conventional techniques, takes huge amounts of time by highly experienced teams using some tools that haven’t changed in decades. 

This is a great use-case for AI/Machine Learning to copy and match the performance of complex tasks learnt from human experts. But this is also a real example of how multiple data sources can be integrated using AI in an enterprise application to generate powerful insights into behaviours within a complex business. The solution prioritises potential cases of energy fraud (or theft) at service points, considering the amount of potential energy recovery and likelihood of fraud at each customer meter. C3.ai and Enel are proving the value of integrating AI, IoT at industrial enterprise scale.

What are we selling? AI and Industry 4.0 benefits

Industry 4.0 technologies including AI and IoT are changing what we sell and how we sell it – the above example raises the huge opportunities but also significant challenges in closing successful Industry 4.0 deals. These technologies offer huge benefits to customers and opportunities for growth. Selling them will demand new skill sets, approaches and competitive strategies. Here we investigate some different ways that change is happening, how successful companies are selling and how we need to adapt but first lets look at some of the benefits in more detail. The potential business value of AI and Industry 4.0 is vast and varied depending on use-cases and verticals – some examples are summarised below:

AI and Industry 4.0 technologies combined with cloud and data analytics offer huge steps in reduced cost, increased efficiency and safety. This will be delivered by automation of real time analytics of data from huge sensor networks; predictive analytics new product services and revenue opportunities.

Success in selling these deals demands a different focus on a wide range of elements in terms of how we articulate value; navigate the client organisation; work with partners and price deals. Here we explore some of these topics in more detail. 

Value Proposition

Communicating the value proposition powerfully is always a challenge but with these technologies, doing so can be even more important and demanding and we need to adapt our approach to client perceptions and value over the project life. Some target industry verticals are less open to new technology and an amount of mis-selling of these new techs is already making our job harder.

Articulating and quantifying the value of your tech requires a clear understanding of how it translates to more revenue, less cost or some other valuable measure to your customer. Also some of the benefits of these techs will be realised in the future (e.g. as more AI training data is processed or new service lines developed). The long term nature of these results combined with some market scepticism make it important to identify shorter term gains. These can be phased to support the business case – as in the Enel case, Machine Learning algorithms take months (or years) before delivering reliable fraud detection, so, the sales team need to identify benefits in the nearer term. Integrating data from multiple sources and presenting new insights (e.g. how much losses could be attributed to faulty equipment) would deliver valuable outcomes and probably achieved in a few months. These proof points can support the business case, giving confidence to decision makers enabling much bigger commercial deals to be realised.

More complex, longer cycle, more stakeholders

The Enel example shows that the lifecycle of these solutions can be long – Enel and C3 have been working together since 2013, over which time the technology was tested, iterated, scaled-up, implemented and integrated. 

Extended sales cycles in these projects are driven by factors including clarity and bounding of business requirements and specifying technical requirements including data management and security. The potential business value of these solutions demand engagement at the most senior levels in the end customer. Combined with the above factors this drives a need to sell both “high and wide” in the target organisation, this includes engaging with multiple stakeholders from business problem owners to IT and executive teams. It is often the case that these projects expose unclear, diverse and often conflicting business requirement. Engaging as early as possible in the sales process will help shape the definition of the project and the roadmap for delivery, aligned with a solid business case.

While many of these are typical issues for enterprise tech sales some are not and familiar challenges are often amplified in these projects, demanding careful attention to achieve success.

Partner Relationships

Industry 4.0 technologies demand new types of partner relationships to be formed and present new opportunities for those partnerships requiring us to consider the roles and type of interaction with third parties in wider and more innovative ways. Much of the value of these techs relies on significant integration of data from multiple sources (e.g. IoT, legacy data) combined with AI powered analytics. This demands a broader view of the offering to meet end-user requirements and optimise the value proposition. Partners may be required to provide implementation, close technology gaps, to deliver integration or to develop the relationship in a vertical or territory. As with all partnerships, mutual value is the key – understanding what each party brings to the table, what they have to gain and why the combination is more valuable than the sum of its parts.

For tech start-ups in particular, Original Equipment Manufacturers (OEMs) or Independent Software Vendors (ISVs) can be important partners for early growth. As end-user sales cycles are typically long and the demand for early revenue at early growth stages of start-ups is critical, OEMs can provide a bridge to revenue and growth. Looking for a fit between your solution and those others who are targeting the same verticals and use-cases can bring much needed revenue as well as an established end-customer base and the valuable experience that brings. Try asking how another solution could benefit from your tech to close a gap or augment functionality or performance. 

We all know that data is the “new oil” and we need to consider what that means for partnerships. Building performance in many Industry 4.0 solutions (particularly using AI) means gathering and processing huge volumes of data which is becoming increasingly valuable. This is true to such an extent that some end-users are beginning to understand that value in the race to build and train algorithms and want to leverage that (with price, exclusivity and more) in procuring these solutions. Partners can provide very real value by providing access to that data if they are involved in acquiring, processing or storing it, accelerating the development and performance of your tech. 

Opportunities around usage and analytics (see below) can play an important role in partner relationships, for instance enabling innovative service and support models, generating revenue opportunities for partners while enhancing service to end-users.

New Products and Revenue Streams

Data from IoT sensors combined with analytics and even predictive analytics can provide extensive information on customer and equipment usage. This can be used to drive innovative pricing models, additional products and revenue streams. This can be an opportunity for vendors, end-customers and partners (see above).

One of the key functions of the C3.ai/Enel project is to identify fraud by using AI to analyse consumption data from millions of IoT smart meters. This requires analysis of billions of data points to discriminate between technical (e.g. measurement or performnance related) grid losses and fraudulent losses (energy theft). It would be reasonable to assume that this analysis could yield other very useful information that could drive new product or service offerings. What if this analysis also identified patterns in the energy usage of individual consumers? That data could be transformed into very useful information and pushed to consumers via smart devices like thermostats or even smart speakers – “hey Alexa, how am I doing on my energy usage this week?”. This type of B2B2C service offering can help consumer facing businesses improve customer service and differentiate their product. 

The same data could also yield important information about the behaviour of the smart meters, their performance and reliability. This could have a significant impact on the asset management of the smart meter asset base – an activity which costs most major utilities millions of dollars annually. This same data could also be a powerful tool for demonstrating to regulators that the utility is proactively managing the asset base and hence protecting their return on investment.  

These examples demonstrate that in many Industry 4.0 projects there are opportunities to introduce new service offerings, products and revenue streams across B2B and B2B2C businesses.

Pricing

Compared to traditional enterprise SaaS solutions, these technologies provide new opportunities around pricing. Although the advent of cloud-based SaaS changed the game in terms of recurring revenue, it didn’t have much real impact in terms of pricing – many deals simply spreading traditional on-premise prices monthly over longer periods and rolling up user support. Ultimately this has resulted in many SaaS players being driven down on price as their solution becomes more commoditised and competitive (just take a look at the number and price of CRM solutions compared to 5 years ago). 

Industry 4.0 offers opportunities to really innovate pricing with combinations of usage, transaction, seat and value based pricing. If you can reliably detect fraud to a point where the process is highly automated, it’s feasible to attach a success fee to each case, the billing of which would itself be automated with a human check if required.

It is also quite possible for your end-customers to utilise usage data to flex the way they bill to their customers, either based on IoT data or on a transaction basis. In one example, a business process automation platform for logistics and transportation, a feature was introduced which enable granular transaction fees. Instead of charging a single fee to customers for a logistics operation (e.g. reception and storage of cargo), the users were able to break down and monitor each step using IoT data. This enabled charging based on 10 steps which provided both logistics customers and operators more flexibility around pricing but also saw service revenues increase for many operators by 3-5% (millions of dollars).

Successful players in Industry 4.0 sales will identify and utilise these pricing tools combined with a clear understanding of their customers’ business drivers to compete and thrive.

Summary

The opportunities are clear. C3.ai have delivered significant, measurable value on an enterprise scale. Its not hard to imagine where a strong sales team could take that success in terms of further automation projects in other parts of the same enterprise, or similar customers.

Selling Industry 4.0 and AI demand that we cover many bases with an innovative, value focused approach, high and wide in the client organisation and involving a range of partners. If we learn to adapt, there are great opportunities for delivering game changing benefits and the rewards that accompany that success.