By: Paul Hardy, Chief Innovation Officer, ServiceNow
Global access to data is exploding. At the same time, our ability to categorise, classify and analyse this data is also expanding. As this new world of data unfolds, businesses are looking to create new data models―and their supporting data analytics functions—to directly and positively impact growth, profit and expansion.
But let’s go back to first principles for a moment. We know that Artificial Intelligence (AI) and Machine Learning (ML)―when correctly applied—can improve the way organisations work and operate. But do organisations know where to start as they look to create these new data models?
We―and by ‘we’ I mean you as the customers, us at ServiceNow, as well as our partners, everybody basically—need to ask where to categorise and compartmentalise processes and functions to build new digital workflows. We need to examine which aspects of the business should be most directly ‘exposed’ to AI. We also need to know what is and isn’t possible in the short, medium and long term.
In other words, we need to get smart about being smarter if we’re going to bring a new era of business forward. So, what does smart really mean in modern business terms? I think it is time to look at AI and digital workflows through the lens of SMART (Specific, Measurable, Attainable, Relevant, Time-bound) objectives.
Specificity
When we use the word ‘specific’ and demand a greater level of product or service specificity, we mean it in the most granular sense possible.
We can’t just say we need more paperclips, more salespeople, more office air conditioning or more field sales automobiles. We need to ask what kind of paperclip shape we need, what colour, what build strength and perhaps even what level of ‘clippyness’ every clip needs to exhibit.
When you are thinking about delivering AI and ML in the business you have to be really focused on what you are trying to achieve―and by that I mean, you need to be able to tie down specific use cases for each and every paperclip.
Measurability
Getting smart with new digital workflows also requires measurability. If you can’t measure it and put it in your business plan and balance sheet (a process, a service, a workflow element, anything at all) then you need to step back and ask whether you should really be doing it.
The reality is that data is often captured and not ever used. It simply falls unmeasured, and unloved, into the data lake. The real cost of this is the ‘noise’ that is created throughout the business because for one, wasted data goes crashing into the lake and secondly, there is then the splashing that occurs afterwards when users do actually realise that they have to start diving into the lake to look for the data that they might actually need in order to make work experiences better!
Attainability and relevancy
If an AI initiative is not attainable or achievable, then why has it formed a part of your current business strategy in the first place? Nowadays we can forecast how far AI will realistically be able to change any given business in real practical terms.
Similarly, if an AI business initiative is not relevant to the business and not able to exist within the context of the organisation’s current and immediate goals, then it forms no sensible part of any smart business plan.
Timeliness
Lastly, we come to timeliness. In the not so distant past, business cycles and the general approach to commercial objectives were typically annual. In this post-millennial age, firms are measuring themselves in much smaller strategic increments.
Key Performance Indicators (KPIs) and business targets used to change year-on-year. Today, they might be calibrated to change monthly, weekly or perhaps even on the basis of individual (tickets) activities relating to individual jobs.
Your next steps
The goal for any business should be to get to the point where they can use smart digital workflows to drive greater productivity, greater quality of all services and greater experiences for all employees.
We know that an increasing proportion of organisations are already examining where they can bring AI to bear and create new value in their business. We also know that many are already on that road and creating new applications and new experiences. Factors that matter most now include service quality, cost reduction, speedy delivery and the need for geographical availability for all new products and services. These are all the defining trends that should be shaping the way we develop new digital workflows that leverage AI and ML.
As vendors, we need to help businesses identify areas for improvement, not just before they start to lose profits and market share, but more significantly, before they start to actually lose contracts. There’s a new culture for predictive business strategy that we are underpinning and making possible.
Smart is smarter if it is more productive and creates greater experiences for everybody inside and outside your organisation. It’s where the smart money is, believe me.
- ENDS -
But let’s go back to first principles for a moment. We know that Artificial Intelligence (AI) and Machine Learning (ML)―when correctly applied—can improve the way organisations work and operate. But do organisations know where to start as they look to create these new data models?
We―and by ‘we’ I mean you as the customers, us at ServiceNow, as well as our partners, everybody basically—need to ask where to categorise and compartmentalise processes and functions to build new digital workflows. We need to examine which aspects of the business should be most directly ‘exposed’ to AI. We also need to know what is and isn’t possible in the short, medium and long term.
In other words, we need to get smart about being smarter if we’re going to bring a new era of business forward. So, what does smart really mean in modern business terms? I think it is time to look at AI and digital workflows through the lens of SMART (Specific, Measurable, Attainable, Relevant, Time-bound) objectives.
Specificity
When we use the word ‘specific’ and demand a greater level of product or service specificity, we mean it in the most granular sense possible.
We can’t just say we need more paperclips, more salespeople, more office air conditioning or more field sales automobiles. We need to ask what kind of paperclip shape we need, what colour, what build strength and perhaps even what level of ‘clippyness’ every clip needs to exhibit.
When you are thinking about delivering AI and ML in the business you have to be really focused on what you are trying to achieve―and by that I mean, you need to be able to tie down specific use cases for each and every paperclip.
Measurability
Getting smart with new digital workflows also requires measurability. If you can’t measure it and put it in your business plan and balance sheet (a process, a service, a workflow element, anything at all) then you need to step back and ask whether you should really be doing it.
The reality is that data is often captured and not ever used. It simply falls unmeasured, and unloved, into the data lake. The real cost of this is the ‘noise’ that is created throughout the business because for one, wasted data goes crashing into the lake and secondly, there is then the splashing that occurs afterwards when users do actually realise that they have to start diving into the lake to look for the data that they might actually need in order to make work experiences better!
Attainability and relevancy
If an AI initiative is not attainable or achievable, then why has it formed a part of your current business strategy in the first place? Nowadays we can forecast how far AI will realistically be able to change any given business in real practical terms.
Similarly, if an AI business initiative is not relevant to the business and not able to exist within the context of the organisation’s current and immediate goals, then it forms no sensible part of any smart business plan.
Timeliness
Lastly, we come to timeliness. In the not so distant past, business cycles and the general approach to commercial objectives were typically annual. In this post-millennial age, firms are measuring themselves in much smaller strategic increments.
Key Performance Indicators (KPIs) and business targets used to change year-on-year. Today, they might be calibrated to change monthly, weekly or perhaps even on the basis of individual (tickets) activities relating to individual jobs.
Your next steps
The goal for any business should be to get to the point where they can use smart digital workflows to drive greater productivity, greater quality of all services and greater experiences for all employees.
We know that an increasing proportion of organisations are already examining where they can bring AI to bear and create new value in their business. We also know that many are already on that road and creating new applications and new experiences. Factors that matter most now include service quality, cost reduction, speedy delivery and the need for geographical availability for all new products and services. These are all the defining trends that should be shaping the way we develop new digital workflows that leverage AI and ML.
As vendors, we need to help businesses identify areas for improvement, not just before they start to lose profits and market share, but more significantly, before they start to actually lose contracts. There’s a new culture for predictive business strategy that we are underpinning and making possible.
Smart is smarter if it is more productive and creates greater experiences for everybody inside and outside your organisation. It’s where the smart money is, believe me.
- ENDS -
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