You Need AI in 2019, Period
Chances are that cloud is at the top of your investment plans for key technology initiatives this year. Cloud investment continues to grow, with a new survey from KPMG showing that 75% of IT leaders are moderately or significantly investing. If you are planning to invest, then you have identified specific use cases for applying or experimenting with transformative, cloud-enabled solutions across your enterprise. One of those solutions should be artificial intelligence (AI). Specifically, AI for conversational intelligence, robotic process automation, analytics and insights.
Companies that effectively use next-gen digital technologies like AI see higher customer-centricity, revenue growth and employee satisfaction. KPMG, for example, found that digital leaders—companies enhancing customer experience and operational performance with stable, scalable transformation—are 2.5 times more likely to invest in robotic process automation. Overall, 17% have significantly invested in AI compared to only 5% of companies on average.
The bottom line is that digital leaders are more likely to invest in this kind of technology, and they’re reaping the benefits. Consider the top companies significantly investing in AI today: Google ($3.9B), Amazon ($871M), Apple ($786M), Intel ($776M), and Microsoft ($690M). Even among those without ample spend, research shows 46% are already seeing an increase in revenue when AI is applied to customer service. This profitability is a result of proven demand, with our research showing that 60% of consumers are open to advanced technologies like AI.
The demand and return are clearly there. So, why are most organizations stalling investment? That’s a can of worms I am going to attempt opening in this informal guide to AI.
What’s Stopping Companies from Investing in or Seeing Success with AI?
At this point, there is little trepidation in terms of the security and resiliency of AI. Top barriers to adoption today include lack of business alignment, immature or outdated technologies, and crippling skills shortages. Nearly 70% of CIOs surveyed by KPMG report skills shortages that prevent their organization from keeping up with the current pace of change, and 55% rate their IT and business alignment “moderate” or worse (our research also shows disparities between IT and LOB in terms of transformation ownership). A new study from Deloitte, meanwhile, found the No. 1 challenge of AI implementation to be difficulty integrating with existing processes and systems. Included in this is the difficulty to build competent, industry-specific AI solutions.
Sound familiar? Here’s what you should start doing to see improvements:
- Think outside of the box to fill your talent pipeline: Digital leaders recognize that any strategy isn’t complete unless the right people are there to make it happen. It’s not a bad idea to onboard contractors and external consultants while you assess internally (85% of companies are currently doing so, according to KPMG). Outsourcing can be particularly helpful for experimenting with AI in terms of development and testing. The goal, however, should be to creatively plug the skills gap for sustainable, long-term transformation. People make up the fabric of what a company does every day. AI is necessary for continued growth and innovation, but people are the driving force behind your mission and vision. This understanding is a red thread among today’s greatest digital influencers. For IT leaders to fill the talent gap, they must be willing to broaden their horizons and welcome new possibilities. I recently explored this concept in depth, specifically the people and culture-related issues within DevOps.
- Get introspective to gauge your level of IT and LOB alignment: IT leaders should ask themselves some key questions, like what departments in the company are (or should be) responsible for AI initiatives and what the primary drivers are behind those initiatives. Identify the goals of your organization’s AI developments and what the desired impact of those developments will be on each of these goals. I recently offered four steps for repositioning IT and LOB for transformation success, which can certainly be applied to AI initiatives.
- Start the transition from proprietary technology to software automated architecture: There are so many things to be said in terms of technology for AI success: silo elimination, intelligent automation, multi-database analytics. What makes this all possible, however, is software automated architecture that allows for the seamless integration of third-party services, strategic business tools and—you guessed it—next-gen technologies like AI. This is the foundation for a highly-sophisticated digital platform that better aligns with business, user and vertical needs. Assuming you’ve already begun moving from on-premises services to cloud deployment models, you’ll want to start transitioning to software automated architecture for leveraging the full power of the cloud. This allows you to begin easily and securely scaling to drive AI-enabled outcomes.
How Should AI Be Applied Across the Organization?
Taking steps to effectively implement AI, the question then becomes what applications to target. Research from Teradata shows that most AI implementations are aimed at product innovation and research and development (50%), then customer service (46%), supply chain and operations (42%), security risk and mitigation (40%) and sales (34%). Meanwhile, the most dominant AI capabilities in use today are data engineering, intelligent workflow and decision automation, and analytics operations at scale.
Here are a few ways our customers are applying AI to create experiences that matter:
- Chat automation, enabling virtual assistants that can engage in a secure and controlled manner to assist customers and even employees internally (62% of organizations expect virtual assistants to have a place in their companies within the next two years, according to Dimension Data).
- Smart routing for cloud and outbound that dynamically optimizes interactions based on prior purchases, transactions and other key business insights.
- Conversational intelligence that transcribes and summarizes voice interactions—including tags, keywords and sentiments—as well as monitors agents and suggests scripts.
- AI analytics and insights, offering advanced predictive insights so enterprises can quickly understand and address customer needs while driving personalized, goal-specific outcomes.
- Business Rules Engine, a centralized, customer engagement-oriented decision engine for multi-channel applications to find the best decision for customer interactions.
- Rapid feature development with full integration of UC, contact center and AI to meet different customer, organizational and industry needs.
PwC estimates that artificial intelligence could add as much as $15.7 trillion to the global economy by 2030. There’s no denying the power of AI as companies assess what they must do to remain competitive in the future. There will likely be bumps along the road to adoption, but that doesn’t mean you should avoid investment. You need AI in 2019, and now you know how to make it a reality for your organization.