Artificial Intelligence: Achieving Repeatable Success with AIHow AI will impact the future of business - and how to get in front of it – Part 2
Overcoming AI’s barriers
Finding the Right Problem
The starting point to a successful journey is identifying the correct problem to solve. Too often, firms do this by looking internally at their own operations. Instead, companies should employ the powerful tenets of design thinking, which focus on customer empathy and outside-in thinking to iteratively test prototypes.
Leveraging the Data to Solve it
Once a company has leveraged Design Thinking to identify the solution that addresses the core issue, focus can shift to finding the data required to power the solution. Given the massive volumes of data AI thrives on, this can be intimidating. The good news for companies, however, is that to create significant value, not all algorithms require millions of data points. While it is certainly true that voice and image recognition were areas of early AI progress because of the massive availability of data to train these algorithms, even data sets of a few thousand can, in some circumstances, yield vastly superior outcomes to the status quo.
Building a Successful AI Solution
After doing a full mapping of the data inputs, teams can then shift to execution mode. Like many technologies, AI solutions are best implemented using agile methodologies in which algorithms are trained, tested and tweaked in an iterative fashion.
Using AI to Drive ROI
AI has been navigating its way through a maturity curve. While early work focused on core building blocks – first laying the foundations for neural networks used to identify non-linear patterns and then layering in context-specific machine learning plug-ins – we are now moving into what can be referred to as Applied AI, where software companies are providing solutions to ever more specific use-cases.
In our work with Fortune 500 clients, we are seeing a diverse set of business applications:
- Business process automation: advances on narrow, industry- and company-specific tasks
- Conversational commerce: Chatbots leveraging voice recognition, customer churn prediction
and prospect prioritization
- Customer service: progressively increasing levels of automation and reduction in the number
of tickets requiring human intervention provide superior levels of customer insight and service
- Industry operations: predictive maintenance of equipment, reducing costly operational disruptions
- Energy: learning algorithms to automate buying and selling of resources to optimize prices and yields
- Intelligent hiring: machine learning to predict the cultural fit between teams and potential recruits
- Project management: proactive prediction of delays and more intelligent resource planning
Build a Winning Team
As more firms arrive at the decision to launch AI initiatives, they are immediately confronted with the question of how best to construct a team capable of success. In our experience, to successfully craft AI-centric solutions, a specific compliment of skills is required. At a high-level, three core elements are required:
Purposeful AI is at the Intersection of Technology, Domain Expertise and Strategy
Create a World-Class System
Building an effective ecosystem of partners and vendors with the knowledge of which AI solutions are relevant to a given problem becomes a fundamental part of a successful AI journey.
By combining the right team with the right ecosystem, any organization can successfully implement an AI solution capable of adding outsized value. We have also found, however, that to achieve enduring traction– and to replicate the success enjoyed by a stand-alone project across an entire organization – a more permanent structure is required. Assembling core AI capabilities into an agile, lightweight team, backed by strong executive leadership, provides the robust, flexible foundation capable of accelerating and amplifying the benefits of AI.
An effective Center of Excellence (CoE) will ensure that learnings are captured and shared across each initiative, mistakes are seldom (if ever) repeated, and the most effective approaches reused.
Through this structure, companies can build prototypes with a long-term view and continuously enhance ecosystems and partnerships to facilitate purposeful AI.
While it took companies 10 – 15 years to go digital, the AI wave is arriving more quickly, creating what can sometimes be an uncomfortable situation. Working with firms from the initial phases of design thinking / data analysis to the full implementation of an AI Center of Excellence, we help companies implement and sustain AI-driven competitive advantage.
Alex is a veteran Partner at the firm, joining Lodestone as Director in 2009. He leads key technology and advisory services including strategy transformation for clients across Switzerland. He has 20+ years of industry expertise in manufacturing and has spent much of his career advising top global organizations on their growth and operational strategies – with a focus on how innovative technology can enable competitive advantage for them.
Mark is a partner at Infosys Consulting and the leader of the firm’s disruptive technologies practice – which combines some of the brightest minds around digital, big data, artificial intelligence and automation. In his 25 years of consulting experience, Mark has advised and delivered strategic solutions to clients globally, with a focus on the retail, manufacturing, transport and logistics sectors.
Jonathan is an automation and artificial intelligence partner at Infosys Consulting. He has spent over 30 years developing strategies and programs to help large clients transform their operations. He is an experienced program manager, enterprise architect and software engineer. Jonathan is also one of the firm’s leading design thinking practitioners.
Tom is a member of Infosys Consulting’s disruptive technologies practice in Europe and also leads the organization in Germany that focuses on digital transformation and AI. His mission is to help transform companies into digitally-centric organizations, with a focus on customer interactions, new business models and product portfolio optimization.
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