The success of lead generation services for AI solutions depends on accurately targeting and engaging the right buyers with the right use cases. This article discusses the growth of the artificial intelligence market, market challenges that make AI sales lead generation complex, and best practices to increase the odds of success.
AI Technology Sales: Challenges for Generating Qualified Leads
Mid- to large-size companies in the B2B space have been the biggest investors in artificial intelligence (AI) technologies to date. They possess large-scale data and business operations that make AI investments economically meaningful.
Although big companies are the largest buyers, AI’s adoption rate among large companies was only at 23% as of late 2017 and less than half of large companies have an AI technology strategy, according to a recent survey released in the MIT Sloan Management Review:
According to Delotte’s second “State of the AI in the Enterprise” report in 2018, 42% of executives believe AI will reach critical mass within 2 years. The report goes on to note that organizations are ramping up investments in AI. The percentage of active pilot projects in the chart above supports that assertion.
However, while some organizations are experimenting with AI or applying it in niche areas, the majority of companies are still passing on adoption. The adoption of AI technologies (AI platforms, digital agents, chatbots, algorithms, analytics, etc.) poses certain sales barriers that must be addressed.
AI companies need to understand these barriers to adoption so they can develop strong use cases and value/benefit calculations in their lead generation efforts.
Barrier #1 – Artificial Intelligence is still viewed as bleeding edge technology
Larger companies typically have the budgets to be early adopters, but they don’t move fast enough to jump on new technologies. Since many large companies have not yet adopted artificial intelligence technology, there are not many decision makers in a position to have a viable sales conversation about AI use cases.
Barrier #2 – Built-in organizational unit resistance to AI
AI technologies have clear use cases in several functional areas (e.g. customer support) where large-scale human based processes can be improved. However, people-based business units have a tendency to be managed in traditional ways because it has worked that way for a long time. Potentially disruptive technologies can trigger risk-adverse fears in operations staff, including a potential job loss if the implementation goes bad. As a result, many operational leaders deflect conversations about AI by saying it’s an “IT thing.”
Barrier #3 – Company silos
According to Forbes, “AI will demand a radical culture shift because it alters the relationship between machines and humans, changing machines from passive receivers of commands into informed, sentient collaborators.”
Functional and technology silos, reinforced by cultural differences, often prevent departments from harnessing and sharing data. AI technology needs access to data sources to operate effectively. This requires a specific strategy and evaluation of data sharing policies before
Barrier #4 – Information security and talent issues
Both of these fears are significant concerns of non-adopters. AI use cases, lead generation campaigns and sales conversations need to hit these issues head-on. Clearly demonstrate how your AI tool will hold up to a company regulatory compliance and risk assessment and provide a detailed talent profile to set up, manage, and use the technology.
Navigating the Organization to Target AI Sales Leads
Despite the barriers to adoption, AI has significant potential benefits across many business functions. Use cases involve the application of several technologies, including chatbots, data analytics, algorithms, quality control, lead scoring and supply chain scoring.
According to Techemergence, the top four organizational departments adopting AI technology include Marketing, Operations, Technology and Customer Service. Lead generation services that target these business groups tend to perform better overall. We have found these four groups most responsive to AI conversations and fairly reachable with the right value-based approach.
Headed by strategic visionaries, Marketing teams are responsible for a company’s outward appearance and customer brand experience while creating a unique selling proposition for the company. Marketing groups want their companies to implement systems and technologies to maximize customer experience and avoid negative reputation events, especially in social media.
AI tools gather information and suggest solutions to help optimize and improve customer experience – a critical practice that increases brand loyalty and reduces customer churn.
AI can also lower the cost of advertising, enhance outbound marketing campaigns, facilitate onboarding new customers and help generate higher revenue per customer.
There are many potential use cases where AI can be used to help IT functions. Lead generation services for AI companies must target and engage new technology evaluators (not the purchasing department) and technology executives that support functional business units where AI can be implemented. Since AI is transformative, rather than iterative, the digital transformation team within the technology function is a good place to target.
Although Customer Support leaders are often resistant to sales conversations about AI, the amount of data and customer communications within these groups make them strong use cases.
An example of a strong customer support use case is SmartBotHub’s enterprise chatbot platform. About 70% of normal customer service issues can be addressed purely by sophisticated chatbots. These AI “digital agents” connect enterprise applications and services on the back end, and human-to-human communication (phone, email, text) on the front end. This allows live agents to scale their productivity up to 15 times when they use a chatbot in tandem with searches and other services for internal and external customers. Chatbots enable customer support organizations to scale to 24/7/365 operations while keeping human costs low.
Other Target Groups
AI has seen some inroads in the financial sector and banking, offering tools such as the Clareti Platform from Gresham Technologies that provide enterprise data integrity solutions for highly-regulated environments. Financial departments within regulated environments and strategy groups are good targets for AI lead generation services.
Business Case-Driven Lead Generation
To be successful, lead generation services must target the right individuals, avoid a long buyer education process, and offer solid use cases that start conversations and trigger downstream adoption decisions.
AI is most effective in mid- to large-sized companies where data problems and human-to-human conversations are bigger and more prevalent. The challenge is education and reaching the right people tasked with business transformation. You need to systematically identify, target, contact, engage, nurture, and convert the right prospects using the right message.
The message needs to be a real business-plus-technology use case. It’s not enough to just target a list of IT or marketing people with a fluffy “AI is good” message. Your target prospects must be in a role that is responsible for business transformation and your sales message must address real concerns about implementing AI.
You must also address the “why” for having the AI conversation in the first place — why this is good for the business, why it makes sense for the person’s career trajectory, why it reduces pain, increases customer satisfaction, etc.
The key to driving new sales opportunities is to make AI tangible and prove it’s implementable in the prospect’s business environment. If you use a lead generation services company to generate discovery calls, the outbound campaign must provide evidence that your AI solution is designed to handle the prospect’s business use case. Lead generation campaigns can be very difficult for AI offerings unless you arm your lead generation partner with quality use cases, scripts, FAQs and tangible evidence of success.
Address implementation risks. Large companies are slow-moving and conservative, with long evaluation processes to avoid technology implementation failure. Implementation risks, regulatory risks, and compliance risks must all be addressed directly in your conversations and use cases. If possible, work with your prospect to visualize the process and provide metrics surrounding implementation. Any AI technology will need to be reviewed by compliance, so it’s good to be prepared up front.
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