Artificial Intelligence driven business models has been saved
Artificial Intelligence driven business models
A strategic approach to capture the full potential of AI
Many companies have started to explore the potential of AI for their future business. Yet only a few have actually managed to capture the value in real-life business use cases. Where to start and how to configure for success?
Go directly to
- Artificial Intelligence Value Assessment
- Leadership enablement
- Getting things done
- Agility as a starting point
In the last few years we have seen enormous and rapid developments in the world of Artificial Intelligence (AI), and it is hard to overestimate its potential. Big tech giants like Alphabet and AWS are developing science fiction like applications in their labs; algorithms that teach themselves winning game strategies, can recognize human emotions or mimic a human conversation.
At the same time, some multinationals are implementing impressive new applications that help automate and robotize back-office processes, that can scan millions of knowledge heavy documents to find particular insights or automated intelligent chatbots in customer services processes. But besides the good news, we also see many AI initiatives getting stuck in proof of concepts and pilots, making a lot of companies struggle with the question where to start and how to scale.
“A lack of a sound vision and right prioritization causes a lot of AI projects to stall”, says Naser Bakhshi, senior manager Artificial Intelligence at Deloitte. “Many initiatives start with a technology that sounds cool, without thinking how it really can make an impact on the organizational goals. They should start with forming a vision on AI that is aligned with the company’s strategy, rather than just letting the one that shouts the loudest experiment freely.”
Artificial Intelligence Value Assessment
Deloitte has developed a proven methodology to facilitate the discussion on ‘where to play’ and ‘how to win’ with AI. “By taking a company’s strategy and long term vision as a starting point, and look at the goals and aspirations and where AI can actually make an impact”, explains Bakhshi. “One of our clients is acting in a highly competitive market, where margins are under pressure. They should focus on automating their processes, leveraging AI and Robotic Process Automation. Whereas another client is a highly specialized firm, depending on high-end expert knowledge. They benefit most from an AI-powered expert system that can process many unstructured documents.”
The AI Value Assessment (AIVA) is an assessment, designed to assess the strategic themes, the existing processes in an organization and unique data sources that can deliver tangible value by applying Cognitive and AI technologies. The AIVA follows a structured three step approach to test if generated ideas are desirable, feasible and viable for execution. Bakhshi: “It boils down to three simple questions: ‘do we want this?’, ‘can we build it?’ and ‘does it make sense?’. At the end of the AIVA you will have a thorough understanding of the exact cases that could benefit from AI and the related value.”
“Getting the right inspiration is an important prerequisite in these discussions”, says Stefan van Duin, partner at Deloitte. “In so called ‘art-of-the-possible’ sessions we bring examples and ideas from all over the world. These examples may come from the same industry as our client’s, but often the best ideas are coming from completely different industries.” These inspirational sessions are helping to make the potential of AI more tangible. “Often AI stays very abstract,” says Van Duin. “People may have expectations that the current technology just can’t deliver, or they think too much in small incremental steps.”
The longlist of ideas needs to be evaluated against the potential impact. “When looking at an innovative idea for the application of AI, the big question is how it will move the needle,” explains Van Duin. “We don’t always have to look for a positive business case in terms of profitability. If a company has high ambitions in zero-footprint operations, an AI solution that helps reducing carbon emission may have a very high impact.” Bakhshi adds: “Small and incremental improvements may be relatively easy to accomplish with AI, but we like to look at big impact: what is the ‘moonshot’ idea for your company?”
Moonshot ideas are transformational and radically change the way of working. They could potentially imply new business models or service offerings, that could change the competitive landscape. An example is the US based insurance company Lemonade, who based their whole client interaction and processes on digital channels and AI, completely changing the way customers interact with their insurer.
AI initiatives should not be perceived in isolation but must connect to the long term strategy of the company and be part of digital transformation. As an example, think about “customer centricity” as strategic theme. Having a fact-based sense of your customers needs and how to serve them in a differentiated and optimal way, is very important but can be challenging. How could AI contribute to become a more customer centric company and improve service towards customers? This is a question that triggers thinking along the strategic priorities instead of taking technology as starting point. The answers, often use cases, will be then attached and valued against the strategic direction of the organization.
“Another important dimension to capture the full value of AI is around leadership”, says Jorg Schalekamp, Lead Partner for the Analytics practice in EMEA. At executive level there is not always a good understanding of what AI can deliver and how to scale and embed it in the organization. “It is essential that the executive leaders understand the technology to the extent that they are comfortable taking (investment) decisions for it to implement. Helping leadership teams to understand the fundamentals of AI so they can have the right conversations with their teams is an important task that should be initiated from the very beginning of any AI initiative. It also takes an entrepreneurial mindset to invest in AI and drive it past the Proof of Concept phase into real adoption. Giving employees the room and support to work on such innovative projects, is key. They should feel supported and rewarded, also in the case of AI failure, but only if failure comes fast”, explains Schalekamp.
Getting things done
Defining a vision and finding the high-impact ideas is one thing, but getting these ideas actually implemented proves to be highly challenging. Bakhshi: “Of course you need the technical capabilities to develop your idea into a workable solution; AI specialists, data specialists, engineers, designers et cetera. But it doesn’t stop there. People may need to learn a new way of working. There may be new operating models or process redesign involved.”
Many organizations make the mistake of keeping the AI innovations locked in a lab. But a successful approach requires a roadmap that links the strategy to people, processes, data and technology. Van Duin: “In our experience, to really make an impactful change, you need to be prepared to take radical steps. We supported one of our clients in a global training program, in which all management teams of all departments -we are talking hundreds of people- were given hands-on training in learning to work with these new technologies.”
Bakhshi adds: ”We have found that the formula for success is to form a multidisciplinary project team with experts from Deloitte and the client to embed knowledge in the organization. AI driven transformation will impact many dimensions of the organization. Therefore experts from different disciplines are required to ensure that a scalable, safe and valuable solution is implemented.
“Beside the technical profiles such as AI engineers and data scientists, you will need to think about factors such as GDPR, where privacy & risk expertise are required. Also change management specialists are needed to make the transition of workforce smooth and adoption of AI achievable. Moreover, strategy consultants are part of the team to assess value of the use-cases, define a roadmap for ROI and structure the new business models. Last but not least, legal professionals are essential to manage Intellectual Property and contracts with (external) data and technology vendors et cetera to prevent possible issues in legal space.
“To navigate through this complexity it is required to setup a so called Cognitive Control Tower, which ensures an aligned approach and methodology, cross leverages best practices and learnings, and brings in subject matter expertise as mentioned above were needed together.”
Agility as a starting point
Deloitte has embedded a lot of elements of the agile methodology in their AI approach. This ensures for value based, relevant prioritization of tangible deliverables. Bakhshi: “Each phase ends with a clear cut-off point and a go/no-go decision for the next phase, allowing for the client to assess if they want to continue development. This way you can not only scale fast, but also fail fast if the idea turns out to be not as successful as originally thought. This is part of the job when working on extreme innovations.”
If necessary, Deloitte helps in getting started. Bakhshi explains: “Deloitte’s ‘Asset Light approach’ allows for the client to utilize Deloitte’s assets as long as they need to postpone making large investments in tooling, hardware or people, until they are certain about the solution.”
The really nice thing about Deloitte’s Asset Light approach is that it enables companies to minimize the risk of losing money on long term commitments (e.g. licenses, hardware). Moreover, It also enables your company to explore different AI platforms and technologies before making a final decision for a preferred technology which can be a (private) cloud, on-prem or an hybrid model. Deloitte has partnerships with big tech firms (e.g. Amazon, Google, IBM, Microsoft etc.) but also works closely with various niche players in the field of AI that develop many state-of-the-art AI solutions not necessarily available in the big-tech platforms.
No two organizations are the same
“We strongly believe in a tailored approach to specific situations,” says Van Duin. “We have learned that the best approach is dependent on the ambition level of leadership, the current capabilities, the change readiness and the applicable rules and regulations. Therefore we always assess the situation and adapt our approach. After all, no two organizations are the same.”