As with all technical investments, the success of an artificial intelligence installation is highly dependent on the way it is selected and then integrated within a business. Despite questions raised by headlines such as ‘Companies Are Rushing To AI – But Few See A Payoff’, the pathway to ROI and success with AI can be boiled down to a simple process.
A report from the Boston Consulting Group and MIT, ‘Winning with AI’ is particularly helpful in illustrating the reasons some businesses succeed in adopting artificial intelligence, while others fail (with huge financial losses to boot). In a nutshell, the report says, there’s a right way and a wrong way to bring AI into your business.
There’s no great surprise in that conclusion. Something similar might have been written decades ago when businesses first began to ponder how they could benefit from connecting themselves to the internet.
So how can your business ensure a clear return on investment with artificial intelligence?
According to the BCG and MIT report, companies can be grouped into four main categories.
The first three are AI ‘adopters’, and are ranked according to the degree of AI integration into their operations, their commitment to AI, and the depth of their understanding. The fourth group is for companies ignoring AI.
Unsurprisingly, the stars of the report are the companies with a good understanding of AI, and a whole-hearted commitment and integration of it – these are the ones that show the greatest return on their investment. Companies that are dabbling, ‘evaluating’, or just hopping on the bandwagon are the ones that are yet to see a payoff.
Among the more striking findings are several that show how important organisational, cultural and process factors are.
“AI is not just a miracle tech fix: it can’t be bolted on to an existing enterprise and bring about a magical, multi-dimensional transformation all by itself"
AI is not just a miracle tech fix: it can’t be bolted on to an existing enterprise and bring about a magical, multi-dimensional transformation all by itself. The greatest potential of AI is that it offers a way to create new and better ways of doing business. The report notes how successful companies made efforts to:
The research also reveals a remarkable upside when AI is the sole responsibility of a C-level exec other than the CIO, and the report warns of the less impressive returns when AI becomes siloed as ‘just another IT project’.
Having a CFO or COO actively involved in the selection and adoption of AI within a business can help to align the technology with business objectives and aid cultural transformation efforts. ‘To a large extent,’ the authors found, ‘difficulties with generating value from AI show up in the [report’s] data as organizational rather than technological.’
In attempting to build its own AI capability, the report finds, a company faces major difficulties and risks. Finding and hiring expertise in a very competitive field, and acquiring enough high-quality data to provide significant new insights are just two of the factors that can make a bootstrap AI initiative expensive and unlikely to show returns for years.
Linking AI expertise and domain-specialist knowledge is another. ‘Today, few AI solutions have off-the-shelf applicability’, the authors of the BCG / MIT report state. Due to the nature of the challenge, it’s quite possible for an organisation to spend $Xm on an AI initiative, only to discover that a practicable, profitable solution is going to cost $5Xm.
The report draws a key distinction here between the roles played in an organisation by AI ‘production’ – that is, the development of an in-house AI capability – and what the authors call ‘consumption’. Consumption means using AI solutions for practical purposes, and includes ‘buying what’s available on the market, rather than developing applications in-house’. Buying-in solutions is an area ‘where [the] leading AI practitioners are making real gains’. ‘Production’ and ‘consumption’ exist side-by-side in the most successful companies surveyed.
So, to return to those ‘slow payback’ headlines: what sort of ROI can adopters of the 7bridges logistics artificial intelligence expect?
The 7bridges platform is of course one of those solutions ‘available on the market’, of the type that the authors refer to as a key contributor in the companies it identifies among the more successful performers. As such, it offers very rapid ROI, with none of the ‘blank cheque’ risk of an in-house AI initiative.
“7bridges customers see ROI of greater than 10x within their first year. Depending on the depth and breadth of the adoption, many customers see considerably higher returns."
The 7bridges logistics artificial intelligence and automation platform enabled internet fashion brand Thread.com to save 50% on transportation costs in their first year and medical technology provider, Ability Matters, to save 20% on logistics costs in their first eight weeks.
And the scale of the investment?
Without the need to recruit and retain specialist staff, develop technologies and amass datasets, the investment required for a business to adopt and use the platform is negligible – especially when compared to the cost of an in-house AI initiative.
But here’s a critical point not addressed in the MIT/BCG report: there are some problems for which an AI solution simply cannot be developed in-house. Why? Because any individual company could never gain access to the enormous, industry-wide datasets on which a logistics solution such as 7bridges is built. Logistics optimisation is one of these cases, because it relies on access to data from hundreds of carriers.
So while companies that succeed in building a in-house AI capability can certainly benefit from it in many aspects of their operations, they could never build an AI logistics solution that would give the returns, scalability or strategic planning and transformational capacity of the 7bridges platform.
This is a good example of why a successful artificial intelligence strategy with good ROI should include buying ready-to-run solutions from the marketplace.
Have a clearly defined and practicable set of objectives. AI has great open-ended promise, but without focus and a realistic understanding of what can be achieved, the costs will outrun any return.
Buy ready-made solutions where appropriate. In many use cases, a purpose-built solution from a specialist vendor is a better choice than an in-house development project.
Initiate a project only when sure you have access to the datasets you’ll need. If the data is tied up by third-parties or commercial rivals, trying to develop an AI solution will be a waste of time and money.
Drive the AI initiative from C-level; not as an incremental IT project. The strategic view and commitment of senior execs is vital for realising the full potential and payback from an AI implementation.
Commit to process change and carry it through. If you have a successful AI solution, it needs enterprise-wide commitment and adoption – which means evolving your methods and processes.
Use AI as a profit-growing technology, as well as a cost-cutter. A good AI solution should offer both economies and new routes to profit; use its capabilities to re-think and re-invent the ways you do business.
Considering a logistics solution powered by artificial intelligence? Ensure you achieve maximum impact and a rapid return on investment, by working with a proven solution provider. 7bridges has a world-class, AI-powered logistics solution that can generate ROI for your business in less than 1 month.