What You Need To Know About AI

Artificial Intelligence is now everywhere. From a Science-Fiction concept to an widespread technology, it often comes with clichés and misconceptions (Yup, we’ve all seen how it ended in 2001 Space Odyssey, The Matrix or Terminator…)

More and more organisations of all sizes and industries are trying to understand how they can leverage AI and get aboard the hype train. But from a buzzword to an actual solution delivering strong value, their are a few things to keep in mind before taking the leap.

Artificial Intelligence projects can fail if they are not led for a really, really good reason. This might seem obvious, but having witnessed the significant impact they have on an organisation, the business case must be ambitious though pragmatic, focusing on tangible business outcomes. Also, and as sexy as AI can be, is it the most adapted technology to solve that specific business case?

There are many compelling use cases in all industries and departments, from Sales to Finance, Marketing, Health & Safety, Logistics… and more which create competitive advantage and massive savings. This Gartner paper also encompasses good practice when building your business case.

Until future systems can embrace human-like top-down decision-making, current Artificial Intelligence technologies relies on data. A lot of data. It must combine quantity with quality, needs to be clean, comprehensive and avoid siloed information, biais or partial data (limited history, demographics…)

If it is sometimes challenging to figure out what data you will need for your algorithms to learn and deliver efficiently, working backwards from the problem you are looking to solve can help determine where to start.

In today’s digitized economy, the ability to use data represents a real and essential competitive advantage. To get to a future state of mature analytical competency, there’s real work to be done in integrating the data you have already. This is a strategic goal for the entire company and, when addressed properly, will lead you to develop experience and a data infrastructure that unlocks every next step. HBR

In other words, walk before you run and get a good understanding of your data first: Conduct an audit of what data you have and where it sits, making sure you have easy access to all relevant information.

As mentioned earlier, AI, Machine Learning (ML), Robotic Process Automation (RPA) are subject to many misconceptions. Amongst the most common ones are:

  • AI is a physical entity – Yes, some people still think of AI as a Terminator-like robot. Well, HAL 9000 would almost seem like a more accurate description…
  • AI can think like a human-being – Replicating the complexity and flexibility of a human brain is way beyond existing AI technologies. “The biggest misconception around AI is that people think we’re close to it.” Prof. Gary Marcus, NYU
  • AI can learn on their own – You have to teach them how to learn and improve, tweaking their algorithms constantly and feeding them with data
  • AI is alsways objective AI can have massive biais too
  • AI will take your job – It doesn’t replace jobs, it makes them more strategic, according to many serious studies
  • AI can use and figure out your messy data – As seen above, you need to figure it out first.

Education is key, not only to raise awareness around what AI will bring to the business at an operational level, but also to get executives onboard, promote data literacy and manage their expectations. Support from the top-management is critical to drive change, get access to all data, overcome political hurdles and lead with vision.

Driving AI projects is everything but a one-off process. The best performing organisations have in-house experts and developers constantly improving models and algorithms, making sure they are updated on a frequent basis and adapting to the latest requirements and information.

Project Management methodologies also need to be adapted to this iterative approach, ensuring constant alignment with Business Outcomes and continuous improvement: Scrum, Agile

We have seen how strategic your data is, especially when it comes to AI. Data governance gives you a real competitive advantage, as it will directly impact the quality of your predictions and algorithms. An efficient data governance strategy must cover:

  • Security – Managing access to data
  • Integrity – Making sure the data is accurate
  • Loss prevention & backup – Mitigating data loss/corruption risks and protecting privacy, especially when it comes to sensitive information
  • Lineage – Keeping full transparency for everyone to understand where the information comes from and how it’s been processed, avoiding the “black box” effect
  • Completeness – Avoiding biais and partial views
  • Ethics – This is sometimes overlooked but we cannot emphasize enough how critical this is, in business as in our everyday life.

Automating Sales Tasks

Sales reps face the constant battle of time management, and when it comes to completing those make-or-break tasks, finding the time can be difficult. By implementing a sales automation process, your business can maximise efficiency. In both a professional and personal sense, AI-powered software and tools are becoming a normal way to save time. Not only do companies with a sales automation process have a 53% conversion rate, they also have a 3.1% increase in annual revenue. Automating sales tasks is a budget friendly way to reduce your operating costs by up to 90%, according to a recent study.

There are a few sales tasks in particular which can benefit from automation, such as reporting, email personalisation and pipeline management.

When it comes to reporting, by automating your reporting process you free up valuable time to spend improving your business in other areas. As a sales manager, it can be frustrating when you’re scouring through out-of-date information to pull together a report. By automating this process, you can have confidence that your data is always up to date and relevant.

By personalising your email content, your audience will feel that you understand them, increasing your engagement rates. Personalising the first and last 10% of your emails seems to be the most effective strategy, and you can gather this data using a customers purchasing history, their personal information like age and gender, as well as tracking their awareness of your product.

The best way to monitor your leads is by automating your pipeline management system, which removes the urgency of manual tracking and ensures that quality leads aren’t lost. Automated sales processes allow you to generate leads with more accuracy, increasing your retention rate. Your lead prioritization and distribution can also be improved with automation. Sales automation helps your employees function at their best. A good sales automation system is easy to use, functional and effective. At AtoBI, our team can help you achieve the highest level of efficiency using data. Contact us today to find out more about what setting up a sales automation process can do for you. 

How Analytics Can Help You Make Better Marketing Decisions

Today’s business owners are faced with many challenges and keeping up with data is one of them. As technology advances, business owners are finding new ways to market their products. These new strategies bring with them a sudden and large multitude of statistics and metrics which can easily overwhelm anyone trying to make sense of which figures are worth analysing. Data-driven marketing gives business owners a chance to really dig deep into their customer base, their sales, and their budget, to find out the most effective ways to market their products or services. It is through analytics you can find answers to all your questions, and once you have business goals in place, data can help ensure you are on the right path.

When analysing your business data, there are some main areas to focus on. While these may differ depending on your business and its interests, it is important to keep these things in mind when making marketing decisions.

Customers and clients are the focus for almost every business, and it is important to know where they are coming from, and if they like what you’re doing. You can do this by tracking the origin of your highest quality leads and being sure to make the most of that source. It can be easy to get caught up in social media engagement, and while that may be the key source of leads for some companies, it is important to find out where all your leads are coming from, and which of those leads are giving you the best returns. When it comes to tracking enjoyment, there are a few ways you can do this. You can analyse time spent on your website, the number of pages a customer visits, or their click-through rate (CTR). It is also important to note when a certain post or blog gains more attention, and to find out why; was it the content, the style, the layout? All important factors which analytics can help break down for you.

Data-driven marketing also considers the negatives, such as what might make potential customers disengage with your content, and if your current strategy is effective in making your business money. When it comes to creating an efficient marketing strategy, businesses can struggle to break down the cost of each individual lead. Understanding how much your customers are costing to acquire is an important part of your strategy.

Do you feel as if your business isn’t making the most of your data, or maybe you’re feeling overwhelmed by what all of this means? No matter the size of your business, AtoBI are here to help.

AtoBI are experts in helping you track, break down and understand your data. Our consultants can help ensure that you understand your audience and use this information to market your products effectively. For more information on what AtoBI can do for you, contact us on (03) 9017 1820 or email us at info@atobi.com.au.

4 Key Factors to run a successful Business Intelligence/AI project (or how to make failure constructive)

Because there’s no better way to start than a quote from one of the most iconic athletes of the decade, as Lebron James once said:

“You can’t be afraid to fail. It’s the only way you succeed. You’re not gonna succeed all the time and I know that.”

In the age of unprecedented information growth and data-driven businesses, it appears most Business Intelligence (BI), Artificial intelligence (AI), Corporate Performance Management (CPM) and other data-related projects will fail, or won’t fully reach their objectives.

Keeping in mind failure is a risk one can only mitigate, the question is:

How can organisations maximise their chance of success when selecting and implementing technology, and in the unfortunate event it goes south, how can they leverage failure to later generate higher value?

1- Engage with all key stakeholders

All data-driven projects, from BI to AI & Machine Learning (ML) aim at breaking silos of information within the business, consolidating data from various sources, building a 360° view across all systems, providing more context to support business-driven decisions. However, this is often seen through the technology lens and organisations also often need to break silos of communication. This can only be achieved with the support of senior management.

The Human factor is obviously critical for success/failure and must start from the very beginning. Engaging with all parties can be compared to an internal sales process:

Create win-win relationships

The first question that needs to be answered is: “What’s in for me?

You want the Sales team to stop using spreadsheets for their monthly forecast because it’s error-prone, inefficient , and forces the Finance department to spend hours in manual data processing? Well, it’s only going to work if you also understand how it can provide value to them: helping negotiate better deals, answering customers’ questions faster, making more commissions… and balance with the perceived cost of change.

The Finance team needs a flexible budgeting tool because the planning solution managed by IT is too complex for ad-hoc analysis, what-if scenarios and instant queries (especially in times like monthly re-forecast or closing)? How can it be implemented with the support and in conjunction with IT, complementing their effort & investment rather than running under-the-radar, parallel processes?

Many Artificial Intelligence projects failed because they couldn’t link operational and strategic outcomes, provide clear value & ROI to front-line managers and employees.

Identify roles & responsibilities

Mapping key stakeholders, influencers, decision-makers, supporters, detractors… is vital not only to get the project started, but also to keep everyone engaged and accountable for the project outcome. I have seen projects where everyone from IT, Sales, Finance departments, both at operational and management levels were extremely engaged and excited at kick-off, but two months into the project failed to deliver due to the lack of clear responsibilities and accountability.

Common personas in successful projects are:

  • The Champion(s), who will get their hands dirty and learn how to translate the business needs into a technical language by mastering the technology. They will be the main interface between the organisation and the technology vendor/partner.
  • The Enabler(s), who will create bridges between all stakeholders, keep them engaged and mobilise resources throughout the project.
  • The General(s), accountable for the project outcome at a top-management level with the authority to make decisions and keep a strategic focus.
  • The Engineer(s), in charge of keeping the project compliant to the company’s IT standards & policy and providing the technical infrastructure.
  • The Customer(s), (I am referring to internal customers here: the end-users) who must provide consistent feedback and communicate back on how the project is adding value (or not) at an operational level. Their voice is not always audible or heard but determines the long-term success of any implementation.

2- Define clear outcomes

This can be a tricky step, because the impact of a BI/CPM/AI… roll-out is not always easy to quantify, i.e. “You don’t know what you don’t know”.

Measuring success is comparing the difference between where you were, where you are now and where you are planning to be.

It all starts with a purpose

Projects start with challenges to overcome. Artificial Intelligence and Machine Learning initiatives have a purpose because they will help increase customer satisfaction by 25%, automate the reconciliation process so the finance team can support the CFO with more frequent & in-depth reports, reduce risks of hospital acquired complications by half, lower inventory costs… Not because it can beat a human at Go or looks good on a resume.

Some organisations adopted AI and ML for the wrong reasons, or failed because they lacked to clearly articulate the use case correctly.

Like a new high-tech spaceship, BI, CPM or AI technologies only have a meaning when you know your destination and what the steps are to get there.

A double-edged approach

Success can be measured in both quantitative and qualitative measures. Automating the budgeting process might not increase productivity by an expected 20% ratio or dramatically change the outcome from what it was in the past, however, will help your Finance team spend more time on providing better-quality recommendations, prevent them from calling sick or burning out and improve employee retention. Providing high-tech, fancy instant visualisations and nice-looking dashboards, but less flexible than old Excel spreadsheets might actually bring low value to the end-users.

Short term outcomes and low-hanging fruit is tempting, but must also serve long term goals (Some companies still invest in legacy tools to solve immediate issues, which can hinder long-term initiatives) We can also measure value and set priorities by factoring Risk, Cost, Complexity…

Defining clearly your criteria to measure business outcomes and adjusting them constantly is your compass to success (or failure, which, in this case, will help you learn and adapt).

 3- A project is not a one-off

Implementing a new BI platform is like learning a new language: you can only improve and realise how much value you can get out of it. Don’t think it will be over anytime soon: it does not end with the delivery of the initial scope but must build new habits, initiate new projects and raise further questions.

Walk before you run

We all know Apollo 11, but landing on the moon was only made possible because of previous Gemini, Mercury and Apollo missions’ successes, and tragic failures too. (If you like space and lessons of persistence, the Soviet Venera program also speaks for itself…)

Back to the good old motto “Think Big, Start Small, Deliver Quickly”, it is not about technology anymore, it’s about managing change, driving transformation across all parts of the business step by step. And rather than “change” which is a one-time concept, we should switch to a more accurate one: “permanent adaptation”.

It also means no failure is permanent, and not achieving success in the first place does not imply you can’t achieve your goals in the next phase. Preparing for failure is:

  • Taking acceptable risks
  • Monitoring and documenting your journey
  • Learning and adapting from your experience

Don’t be dependent

The relationships between internal and external stakeholders is also a journey. I like to relate it to the dependency cycle of Katherine Symor, later adapted by Vincent Lenhard, where relationships tend to constantly evolve from:

  • A dependence stage: The traditional, old-school BI approach where the business depends on IT or an external third party to provide them with analysis.
  • A counter-dependence stage: Finance, Sales… running their own parallel spreadsheets in reaction to the lack of flexibility and autonomy, without complying to IT processes (Excel)
  • An independence stage: Both processes co-existing separately with no synergy, but it is inefficient and the risk is that this doesn’t meet corporate expectations. Silos still persist.
  • An interdependence stage: IT and the business collaborating and leveraging each-others’ efforts (Self-service BI)
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This means the way you manage internal stakeholders must change over time, but also how you engage with third-parties (vendors, partners, consultants…) from pure technology experts to trusted advisors who know your business/industry and provide value at another level without unhealthy dependence (technical, IP, commercial…).

4- Keep an open mind

Selecting a solution is not a 100% rational process (we’re just humans after all). But it can get close to it.

We could list all cognitive bias we are subject to (Have a look, it’s very insightful and see if you can relate), but let’s focus on the ones we encounter often in a BI/CPM or AI project:

What worked somewhere else might not work here

Starting with the Law of the instrument: “An over-reliance on a familiar tool or methods, ignoring or under-valuing alternative approaches. – If all you have is a hammer, everything looks like a nail.”

What worked in another environment, even very similar, might not always be applicable to the current one. Keeping an open mind means considering alternative solutions with a scientific approach and keeping out from personal bias.

Try before you buy

Would you buy a car without trying it first? (You might, but it’s taking an unnecessary risk)

Technologies are more and more flexible, agile and easy to use. Most vendors offer a trial version and there is no better way to gauge potential value by running a proof of concept.

Not only it will provide a better look and feel of the final outcome, but it is also a good way to evaluate the quality of your interactions with the vendor or partner. The difference between a POC and a full project is merely the size of its scope.

If you need assistance navigating through Data Management, Business intelligence, Corporate Performance Management or AI & ML solutions, please feel free to reach out to us!

Written by Olivier Bastard – Account Executive at AtoBI
Get in touch with Olivier

The Real Meaning Of AI

 There has been a significant shift over the last 5 years with focus moving from BI to AI and ML.  As market leaders AtoBI are aligned with technologies that are at the forefront of this change, to ensure that our customers have cutting edge solutions.

On 7 December 2017 a critical milestone was reached when Googles AlphaZero Program defeated the 2016 world computer chess champion, Stockfish 8.  Its human creators never taught it, it used machine learning principles to self-learn in just 4 hours.  Scary as it is, we shouldn’t be frightened of progress, in fact we should embrace these changes.

It’s not just understanding the technologies available but recognising the complexities that customers have, even in similar industries.  Finding the right solution to address your issues can sometimes seem a daunting task, but AtoBI is here to provide you with the confidence that our extensive experience and expertise will make your journey with these new technologies easier. We pride ourselves on our expert team and their ability to help our customers navigate this new world.

If you are still apprehensive, don’t be, try to think of AI as Assistive Intelligence rather than Artificial Intelligence.  AI is designed and ultimately controlled by humans, to increase the accuracy of insights and aid prediction.  We should embrace the new technologies available to us. To find out how AtoBI can help you and your organisation on your AI journey contact us at info@atobi.com.au.